From Brownian to Driven and Active Dynamics of Colloids...
Transcript of From Brownian to Driven and Active Dynamics of Colloids...
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart I
Masaki Sano
Department of Physics
The University of Tokyo
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Talk
bull Introduction to Brownian motion Fluctuation Dissipation Theorem
bull Stochastic Energetics (by K Sekimoto 1997)
bull Fluctuation Theorems Jarzynski Equality etc (1993-1996) rarr (Tuesday by Hyunggyu Park)
bull Measuring energy dissipation in small systems
bull Information Feedback control and thermodynamics
bull Self-propelled particles Active Matter
Part I
Part II
Einsteinrsquos Derivation of the Relation for Brownian Motion
bull Consider colloidal suspension in an equilibrium state under the balance between external field and diffusion
120588119907119891
120588(119909) Concentration gradient
120588119892
120588(119911)minus119863
119889120588
119889119909
minus120574119863119889120588
119889119911Current by diffusion
Current by external force
Under gravity
Total current
Equilibrium condition
(1)
bull Osmotic pressure due to particle concentration
bull Force due to osmotic pressure balances with the external force f
Stokesrsquos law of drag force
Drag coefficient
(2)
From eq (1) and (2)
Einstein-Stokes Relation
Mesoscopic relation connecting between atomic scale and macroscopic scale
Brownian motion and Langevin equation
bull Langevin equation
Integrating this equation
Multiplying v(t) and integrating it
Using equipartition
there4
Fluctuation prop Dissipation
(1)
(2)
bull Diffusion coefficient
119863 equiv
mobility
FDT (Fluctuation Dissipation relation of 1st kind)
FDT (Fluctuation Dissipation relation of 2nd kind)
Integrating (2) gives
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Outline of Talk
bull Introduction to Brownian motion Fluctuation Dissipation Theorem
bull Stochastic Energetics (by K Sekimoto 1997)
bull Fluctuation Theorems Jarzynski Equality etc (1993-1996) rarr (Tuesday by Hyunggyu Park)
bull Measuring energy dissipation in small systems
bull Information Feedback control and thermodynamics
bull Self-propelled particles Active Matter
Part I
Part II
Einsteinrsquos Derivation of the Relation for Brownian Motion
bull Consider colloidal suspension in an equilibrium state under the balance between external field and diffusion
120588119907119891
120588(119909) Concentration gradient
120588119892
120588(119911)minus119863
119889120588
119889119909
minus120574119863119889120588
119889119911Current by diffusion
Current by external force
Under gravity
Total current
Equilibrium condition
(1)
bull Osmotic pressure due to particle concentration
bull Force due to osmotic pressure balances with the external force f
Stokesrsquos law of drag force
Drag coefficient
(2)
From eq (1) and (2)
Einstein-Stokes Relation
Mesoscopic relation connecting between atomic scale and macroscopic scale
Brownian motion and Langevin equation
bull Langevin equation
Integrating this equation
Multiplying v(t) and integrating it
Using equipartition
there4
Fluctuation prop Dissipation
(1)
(2)
bull Diffusion coefficient
119863 equiv
mobility
FDT (Fluctuation Dissipation relation of 1st kind)
FDT (Fluctuation Dissipation relation of 2nd kind)
Integrating (2) gives
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Einsteinrsquos Derivation of the Relation for Brownian Motion
bull Consider colloidal suspension in an equilibrium state under the balance between external field and diffusion
120588119907119891
120588(119909) Concentration gradient
120588119892
120588(119911)minus119863
119889120588
119889119909
minus120574119863119889120588
119889119911Current by diffusion
Current by external force
Under gravity
Total current
Equilibrium condition
(1)
bull Osmotic pressure due to particle concentration
bull Force due to osmotic pressure balances with the external force f
Stokesrsquos law of drag force
Drag coefficient
(2)
From eq (1) and (2)
Einstein-Stokes Relation
Mesoscopic relation connecting between atomic scale and macroscopic scale
Brownian motion and Langevin equation
bull Langevin equation
Integrating this equation
Multiplying v(t) and integrating it
Using equipartition
there4
Fluctuation prop Dissipation
(1)
(2)
bull Diffusion coefficient
119863 equiv
mobility
FDT (Fluctuation Dissipation relation of 1st kind)
FDT (Fluctuation Dissipation relation of 2nd kind)
Integrating (2) gives
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
bull Osmotic pressure due to particle concentration
bull Force due to osmotic pressure balances with the external force f
Stokesrsquos law of drag force
Drag coefficient
(2)
From eq (1) and (2)
Einstein-Stokes Relation
Mesoscopic relation connecting between atomic scale and macroscopic scale
Brownian motion and Langevin equation
bull Langevin equation
Integrating this equation
Multiplying v(t) and integrating it
Using equipartition
there4
Fluctuation prop Dissipation
(1)
(2)
bull Diffusion coefficient
119863 equiv
mobility
FDT (Fluctuation Dissipation relation of 1st kind)
FDT (Fluctuation Dissipation relation of 2nd kind)
Integrating (2) gives
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Brownian motion and Langevin equation
bull Langevin equation
Integrating this equation
Multiplying v(t) and integrating it
Using equipartition
there4
Fluctuation prop Dissipation
(1)
(2)
bull Diffusion coefficient
119863 equiv
mobility
FDT (Fluctuation Dissipation relation of 1st kind)
FDT (Fluctuation Dissipation relation of 2nd kind)
Integrating (2) gives
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
bull Diffusion coefficient
119863 equiv
mobility
FDT (Fluctuation Dissipation relation of 1st kind)
FDT (Fluctuation Dissipation relation of 2nd kind)
Integrating (2) gives
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Theories on Non-equilibrium Systems
Classical mechanics
Newton
Quantum mechanics
ShrOumldinger
Non-equilibrium statistical mechanics
Liouville equation
BBGKY-hierarchy
Time dependent
correlation functions
Kinetic theory
Boltzmann equation
Master equation
Langevin equation
Fokker-Planck equation
Non-equilibrium
thermodynamics
Linear flux force relations
Onsager relations
Navier-Stokes equation
Particle level Particle level
Macroscopic levelMesoscopic level
Kubo
relation
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Stochastic Energetics
)(2)()(
)()(
2
2
ttTktt
tdt
dx
x
axU
dt
xd
B
daa
UdQaxU
m
pd
)(
2
dxt
dt
dxdQ )( Force exerted from heat bath x displacement
dWdQdE
dax
UdW
)(2
axUm
pE
by K Sekimoto JPSJ (1997)
See also Ken Sekimoto ldquoStochastic Energeticsrdquo Lecture Notes in Physics 799 (Springer)
Underdamped Langevin equation
Heat
External control parameter
Then 1 st Law of Thermodynamics holds for Langevin equations
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
dax
UdU
m
pd
dax
UdUdt
m
p
dt
dpdx
x
U
dt
dpdQ
2
2
daa
UdQaxU
m
pd
)(
2
dWdQdE
)(2
axUm
pE da
x
UdW
1 st Law of Thermodynamics for Langevin Dynamics
Separate conservative forces and non-conservative forces
How to derive
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Ito Integral and Stratonovich Integral in Stochastic Differential Equations
sdBsf )(
ssss BBsfdBsf )()(
ssss BBsfssf
dBsf
2
)()()(
Itorsquos definition
Stratonovichrsquos defintion
Computing
t
tB BBTkdss0
02)( Wiener process Bt
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Mesoscopic Nonequilibrium Systems
bull Jarzynski Equality(Relation connecting Equilibrium and Nonequilibrium )
Ex Unfolding of RNA
bull Fluctuation Theorem
(Probability of negative entropy production Proof of the second law in thermodynamics)
bull Harada-Sasa Equality
FDT violation = Irreversible Production of Heat
Ex efficiency of molecular motors
processesleirreversibFW
)exp()exp( WF
(1997)
(1993-)
(2005)
Some New Results
rate productionentropy 119875(120590)
119875(minus120590)= 119890120590120591
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Example Small Particle Driven by External Forces
Langevin Simulation of Active Transport ex Brownian Ratchet
Langevin Equation ( overdamped case )
0)(
)()(
txU
txUtxU
flushing
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
bull Energy dissipation rate (energy flow from the Brownian particle to the environment) can be defined as
according to Sekimoto(JPSJ 1997)
Energy Dissipation
But difficult to measure
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Optical Trapping (LASER Tweezers)
Toyabe amp Sano (2006)
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Multi-beam Laser Trap
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How to make Nonequilibrium State
Switching
Swinging
Surfing
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Tool Laser Tweezers and Colloid System
bull It is possible to capture and manipulate small particles
bull Particles feel harmonic potential around the focal point of the LASER
optv
x
))()(( txtxkxkF optopt
)()()( tvtFtJ optopt
dtvtFTk
opt
t
opt
B
t 0
)(1
Energy Input
Entropy production
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Fluctuation Theorem (steady state)
functionon distributi)(
)(log
1lim
Evans et al PRL (2002)
Negative Entropy Production
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Measuring Energy Dissipation in Small Systems
Harada-Sasarsquos relation PRL (2005)
Heat = Degree of Violation of FDT
TkD B )(2)( RTkC B
Correlation Function
Response Function
Equilibrium State
Einsteinrsquos Relation (1905)
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
The Equality is generalized for more complex systems
bull Time independent driving force with U(x)
bull Flushing potential
bull Periodically switching of potential U(xt+T)=U(xt)
bull Many particles (Colloidal suspension)
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How to make Nonequilibrium Steady State (NESS)
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Equilibrium Case
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Check of FDT(Fluctuation Dissipation Theorem)
FDT
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Shape of the potential
2654127 POINTS
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Equilibrium State ( Check of Fluctuation-Dissipation Theorem )
7854 RUNS52 RUNS x 32767 POINTS
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Non-Equilibrium Case
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
bull Flushing Switch LASER
position between two
positions temporally with
a poisson process
bull Every 10ms decide if
switch or not randomly
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Measurement of Response Functions
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Correlation Functions amp Responses
EQ Equilibrium State
switching rate
NESS1 1 sec-1
NESS2 2 sec-1
NESS3 3 sec-1
NESS4 4 sec-1
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Verification of Harada-Sasa EqualityLHS can be measured in this system
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
More general case (Memory effect)
)(~
2)(~
)(~
)(~
)())(()()(
RkTCI
tttxFdssxstt
Ohkuma amp T Ohta (2006)
Narayan Deutche PRE (2006)
Colloid in polymer solution viscosity has
memory effect rarr generalized Langevin
eq
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Micro-RheologyMeasure viscosity from fluctuations
FDT
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
)(~
2)(~
)(~
)(~
RkTCI
Left hand side
Right hand side
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Simplified proof
1st term
2nd term
response
)()()()(
)())()(()(
2 tvttvtJ
tdxttvdttJ
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Molecular Rotary Motor gt F1-ATPase
bull Abrahams et al Nature(1994)
bull Noji Yasuda et al Nature(1997)
bull Yasuda Noji et al Cell(1998)
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Results gt Violation of FDT
bull 04 uM ATP 04 uM ADP 1 mM Pi
S Toyabe et al Phys Rev Lett 104 198103 (2010)
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Results gt Harada-Sasa equality
bull Integration Range [-300 Hz +300Hz]
bull Frictional Coefficientγ
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Results gt Heat Dissipation
Error bars are SD
Energy input rate per unit time
S Toyabe et al Phys Rev Lett 104 198103 (2010)
ATPs Gv
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Outline of Part I
bull Stochastic Thermodynamics was introduced
bull Degree of the violation FDT relation is equal to heat flux to the environment in Langevin systems Such small heat flux can be experimentally evaluated by using a new theory
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
47
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
50
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Feedback control based on information contents
53
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Trajectories under feedback control
54
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Calculation of Free Energy Free energy gain
bull F
55Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Efficiency of Information-Energy Conversion
28 Efficiency
56
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
57
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
59
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Induced Polarization
Electric charging time
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Chemotaxis of Bacteria
77httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
78
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Summary
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Masaki SanoShoichi Toyabe1
Hong-ren Jiang2
Ryo Suzuki3
The University of Tokyo1Ludwig Maximilians University Munich
2National Taiwan University3Techinical University of Munich
From Brownian to Driven and Active Dynamics of Colloids
Energetics and FluctuationsPart II
IAS Program on Frontiers of Soft Matter Physics Tutorial
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Outline of Part II
bull Information Feedback Control and Generalized Jarzynski Equality
bull Introduction to Active particle Active Matter
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
2 nd law of thermodynamics and FDT can be derived from JE
Importance of fluctuations
Small system large fluctuation
2 nd law of Thermodynamics
Maximum work
Work W
start
end
W ge F = F2 ndash F1
exp[W kT ] = exp[F kT ]
F
W
Jarzynski equality (1997)
Free Energy F
Trials
P(W)
Jarzynski Equality
Work exerted to the system
Free energy gain of the system
119882
∆119865
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
New Theories in Statistical Mechanics
Entropy production
σ
-1 -07 -0
4 -01 0
2 05 0
8 11 1
4 17
2
Confirmed for driven Brownian particles electric current etc
Initial conditions producing positive entropy production is much more frequent than the negative entropy production
Evans 1993Gallavotti CohenSecond law of thermodynamics
Fluctuation dissipation theorem
Fluctuation Theorem
0
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
I mutual informationmeasurement and control have errors
Sagawa amp Ueda PRL (2010)119868(119860 119861)
H(119860) H(119861)
119882 ge ∆119865 minus 119896119879 119868
Work performed to the system
Free energy gain of the system
119882
119865
Correspondingly generalized Jarzynski equality
Fluctuation Theorem Jarzynski equality
119890minus(119882minus∆119865)119896119861119879 = 1
Generalized Jarzynski equality including information
119882 minus ∆119865 ge 02nd law of thermodynamics
119890minus119909 ge 1 minus 119909
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Maxwellrsquos demonbull Violation of the second law of thermodynamics
James Clerk Maxwell (1831-1879)
Opening amp closing door do not perform work to atomsrArr 2nd law really violaterArr controversial state lasted more than 150 years
85
(1871)
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Maxwellrsquos Demon and Szilard Engine
WQ 0T
QS
(a) (b)
(d) (c)
Heat Bath
2lnTkW B
Szilard (1929)
The simplest and analyzable Maxwellrsquos demon
Entropy gain of gas
02ln kS
Information gain rarr decrease of entropy
Information gain ln 2
Expansion
119882
Extracted work
119882 le minus∆119865 = 0Second law requires
However we gain
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Schematic illustration of the experiment
Toyabe Sagawa Ueda Muneyuki Sano Nature Physics 6 988 (2010))
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Experimental Setup
Dimeric polystyrene particle (300nm) is linked on the substrate with a biotin
Particles exhibit a rotational Brownian motion
Quadrant electrodes are patterned on the substrate1 μm (11000 mm)
88
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How to produce a spiral-stair-like potential
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Estimating a potential function from the data
Minimize ε2
Measured potential function
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Feedback control based on information contents
91
Time delay ε
Particle is observed in S
Switch
Repeat
Particle is outside of S
Do nothing
Observe position of the particle periodically
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Trajectories under feedback control
92
Time (msec)
Rev
olu
tio
ns
Time delay 120576
Up
Down
Time delay
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Calculation of Free Energy Free energy gain
bull F
93Calculate (ΔF-W)
∆119865
+w
-w
Work performed to the system
Free energy gain of the system
119882
119865
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Efficiency of Information-Energy Conversion
28 Efficiency
94
119868 = minus119901 ln 119901 minus 1 minus 119901 ln((1 minus 119901)
Information gained by the observation
1199011-119901
120599 =∆119865 minus119882
119896119861119879119868
Efficiency of Information-Energy Conversion
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
bull A fundamental principle to relate energy and information feedback
bull
Experimental test of generalized Jarzynski equality
generalized Jarzynski equalitySagawa Ueda PRL (2010)
Agrees within measurement accuracy
Feedback Efficacy
- Entropy Production
95
Toyabe Sagawa Ueda Muneyuki Sano
Nature Physics 6 988 (2010))
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How to measure the feedback efficacy
120574 = 119901119904119908 + 119901119899119904 lege 1
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Consistency with 2nd Law
bull 2nd law holds for the total system
bull Information-energy conversion is realized when we look at the small system
Total System
97
demon Micro system
Information
Feedback
Energy Work
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-propelled dynamics of Janus particle
bull By a local temperature gradient
Self-Thermophoresis
bull By electric field
Induced Charge Electro-Osmosis (ICEO)
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-propulsion of Janus particle I Temperature field
ttVtr
ttVtr
etVtrt
~)(
~)(
)]1([2)(
22
222
22
HR Jiang Yoshinaga Sano PRL 105 268302 (2010)
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Rotation of Chiral Doublet Thermophoresis
HR Jiang Yoshinaga Sano PRL (2010)
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Temperature distribution
Seed of self-thermophoresis of Janus particle is related to the response to the external gradient
Induced flow visualized by tracer particles around the fixed Janus particle
Temperature distribution around a Janus Particle
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-thermophoresis
Theoretical calculation by N Yoshinaga
Viewpoint in Physics PRL (2010)
Effective slip velocity
Light is absorbed on the metal side
Characteristic length
Interaction potential between the surface and fluid
Migration in an uniform external temperature gradient
Temperature diff across the particle
Migration speed is determined by the average of Soret coeff
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Stochastic Dynamics of Active particle
= 119951 times120597
120597119951
120574119959 = minus120597119880
120597119955+ 120573119951 + 120643(119905)
Fokker-Planck equation
Langevin equation
119951
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
119896 = 0for
Equilibrium distributionc
Correlation function of the polarity direction
Rotational Diffusion
MSD of active particle
Without potential
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
2 2 24r Dt V t
119881119868119862119864119874~1205981205761198771198642
120574
Self-propulsion of Janus particle II Electric field
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Motion of Janus Particle Top View
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
How do they move
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Self-propulsion of Janus particle II Electric field
Cross over frequency
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Formation of Chains at frequency higher than fc
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Doublet Triplet Linear Chain
Oscillation Wave
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Fluctuation in Janus Chiral Doublet
bull How does the driving force compete with thermal fluctuations
bull Use of Fluctuation Theorem
Role of Thermal fluctuation
R Suzuki HR Jiang M Sano ArchivePrecise determination of torque is possible
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Deformable self-propelled chain
Waving motion Spiraling motion
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Induced Polarization
Electric charging time
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Collective motion of self-propelled string
114
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Chemotaxis of Bacteria
115httpwwwsciencemagorgcontent3346053238abstract
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles
Information and Feedback in Different Systems
Fluctuation Information Feedback Outcome
Maxwellrsquos demon Thermal Speed position Biased Choiceof fluctuations
Gain Free Energy
Active Particle Thermal ------- -------- EnhancedDiffusion
Bacteria (Escherichia coli)
tumbling Chemotactic Signal
Change tumbling freq
Chemotaxis
Amoeboid cell(DictyosteliumDiscoideum)
Instability ofcell shape
Chemotactic Signal
Biased Choice of random protrusion
Chemotaxis
116
Summary of Part II
bull Information thermodynamics can be tested and demonstrated in colloidal systems
bull Different kinds of phoresis can be used to create self-propelled particles and control interaction of particles