Chapter 1 Introduction and Literature...

Chapter 1

Introduction and Literature

Review

With ever increasing pollutants in our ambience, there is an impelling demand

for reliable and cost effective sensors for monitoring them. These sensing devices

are broadly classified into physically and chemically sensitive devices. Physically

sensitive sensors measure various physical quantities such as pressure, tempera-

ture, velocity, acceleration, flow, liquid level, magnetic field etc. Chemical sensors

measure concentration change of chemical species in liquid and gaseous media.

The chemically sensitive solid-state devices discussed in this thesis are based on

the electrical response of the solid to its chemical environment. Hence solids like

tin oxide whose electrical properties are affected by the presence of a gas-phase

or liquid-phase species have drawn our attention to fabricate prototype sensing

devices. This change in electrical properties is observed and then used to detect

chemical species. The major advantages of solid-state sensors are that they are

very simple to operate, small in size and of low cost. The major disadvantages

of most solid-state chemical sensors are lack of stability, reproducibility and se-

lectivity. In this research work we have tried to alleviate some of these problems.

The term solid-state sensors has been widely used for denoting sensors based

upon classical semiconductors, solid electrolytes, insulators, metals and catalytic

materials including different types of organic membranes (Madou & Morrison

(1995)). Frequently recurring topics discussed in this work are introduced in this

chapter.

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1. INTRODUCTION AND LITERATURE REVIEW

1.1 Semiconducting Metal Oxide Sensors

It has been well known fact that the resistance of semiconductors is affected

by the gaseous ambience. The semiconducting metal oxides based gas sensors

exploit this phenomenon. Semiconductor materials sensing mechanism can be

understood as the reaction between semiconductor surface and gases in the at-

mosphere, which changes the conductance of semiconductor. This change in the

conductivity of the semiconductor in the gaseous environment may be attributed

to the adsorption mechanism: Oxygen from the atmosphere adsorbs and extracts

electrons from the semiconductor. If electrons dominate the conduction mecha-

nism in the solid, then conductivity will decrease as the electrons are extracted.

When an organic vapour is present in the atmosphere, it reacts with the nega-

tively charged oxygen, becoming oxidized and the electrons are returned to the

solid, restoring the conductivity (Madou & Morrison (1995)).

Other possible reason for this resistance change can be the reaction between the

semiconductor and the gas that leads to the ion exchange near the surface. An-

other reason for the change in the conductance of semiconductor is the reaction

between gas and semiconductor, that may lead to the change in the stoichiom-

etry, or due to formation of another compound. This may be interpreted as if

semiconducting oxide that is oxidized by oxygen from the atmosphere, lattice

oxygen is extracted when some organic vapours are introduced into the atmo-

sphere. Therefore the presence of the organic vapour lowers the cation/ oxygen

ratio in the oxide that is how- it changes the stoichiometry of the solid. Such a

change in the stoichiometry can have significant effect on the conductivity of the

material.

Taguchi (1971) designed first commercial sensor out of semiconducting metal ox-

ide, it was based on the compressed powder of SnO2 and it was used to detect the

reducing gases. The most attractive feature of these type of sensors is their low

cost. But the problem of reproducibility exist in these type of sensors because

of intergranular resistance; it is the parameter which varies with minute details

of the preparation. Hence every sensor can be expected to differ slightly in its

initial characteristics. As the sensor operates at high temperature, slow drift due

to stoichiometry changes or irreversible reactions with gaseous impurities in the

atmosphere affect its stability. Selectivity is another bottleneck which has to be

sorted out, and intensive investigations are going around the world to develop the

device which will be free of all the above mentioned problems. The operation of

semiconductor gas sensors is very complex, so undoubtedly the progress is slow.

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Each step in this direction will open a new avenue for the low-cost sensors. In

this thesis we have tried to alter the material properties of SnO2 by controlling

its particle size and then using it as sensor.

1.1.1 Conduction Mechanism in semiconductors

The semiconductor gas sensors are discussed on the basis of electrons being added

or removed from the conduction band, leading to large changes in the conduc-

tivity of the semiconductor. At 0 K in perfect semiconductors the conduction

band is empty of electrons. Any solid is naturally or artificially imperfect, lead-

ing often to some electrons in the conduction band or missing electrons in the

valence band. These imperfections/ impurities can be introduced by doping the

solid externally, a dopant can alter the electrical properties of the semiconduc-

tor significantly. These impurities are of two types: donor and acceptor. Donor

impurities in the crystal tend to provide additional electrons to the semiconduc-

tor. Whereas, doping with the acceptor impurities generate holes in the valance

band. Semiconductors are generally classified as n- or p- type depending upon

these majority carriers. If semiconductor conducts via donor-produced electrons

in the conduction band is defined as n-type semiconductor, otherwise if conduc-

tion is due to acceptor-produced holes in the valence band is termed as p-type

semiconductor.

1.1.1.1 Bulk Conductivity

Most of the metal oxides are non-stoichiometric and contains large number of

defects. The bulk conductivity of a semiconductor is given by

σ = nbqun + pbqup (1.1)

where σ is the conductivity in mhos cm−1, nb and pb are the bulk concentrations

of electrons and holes, respectively in cm−3, q is the electronic charge (1.6×10−19

coulombs), and un and up are the mobilities of the electrons and holes, respectively

(in cm2V −1sec−1). Normally either the density of electrons or the density of holes

completely dominates, and only one term of eqn. (1.1) needs to be considered.

The conductance G of the crystal is given by

G =σWt

L(1.2)

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where W is the width, t is the thickness, and L is the length of the crystal.

The mobility u of the carrier is its drift velocity per unit electric field. This drift

velocity is dominated by scattering. The electron or hole begins to move, forced

by the electric field; it then hits a scattering centre and losses its orientation.

After each scattering event, it again begins to move as forced by the electric field.

Scattering associated with the lattice is increased by the thermal vibrations. The

vibrations may be treated mathematically and may be considered as particles,

called phonons, with which electrons can collide. At reasonable temperature

(above room temperature) phonon interactions dominate. At low temperature

ionized impurities begin to dominate the scattering, and at all temperatures there

is a small contribution from the neutral impurities. Thermal scattering, the one

of the most interesting phenomenon in the semiconductor gas sensors, increases

with increasing temperature and, and u decreases as per following relation:

u = T− 32 (1.3)

The density of these carriers depends on the temperature, as they depend on

the energy level of the dominant carriers. If there are shallow impurities (i.e.

carrier levels lie near the conduction band) they will be completely ionized at

room temperature. In the case of SnO2 and ZnO normal donors are ionized at

room temperature and above. At slightly elevated temperature the concentrations

of the carriers remains relatively unchanged. At sufficiently high temperature

another effect comes into picture, that defects are also created thermally in the

lattice, and this changes the carrier concentrations and thus conductivity (Madou

& Morrison (1995)).

1.1.1.2 Surface Conductivity

At the surface the normal periodicity of the crystal is interrupted, and localized

energy levels exist there and they are often forbidden gap regions. Such energy

levels can either be acceptors or donors or both. In the case of metal oxides,

metal ions tend to capture extra electrons as they are cations, whereas oxygen

ions behave as anions and give up electrons.

In the case of homopolar semiconductors, the bonds in the crystal are formed

with each atom contributing an electron. So, between each pair of atoms there is

an electron pair shared equally by the atoms. At the surface, the surface atoms

find no neighbours to pair its electron with, and hence dangling bonds exist at

the surface. This is just like unpaired electron in an orbital extending out from

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the surface, and it can either accept another electron from the electron pair, or

it can inject itself to the bulk crystal leaving surface energy level (surface state)

unoccupied. These surface states form narrow bands of energy levels and reason

for this can be obviated from the fact that these states are not at same energy

level and as there are bonds in the bulk, because electrons in these energy levels

interact with each other. When there is no net surface charge it is known as

flat-band surface (Garrett & Brattain (1955)).

In the case of heterogeneous surface band of the surface energy levels too exists.

This formation of the band may be attributed to the heterogeneity of the surface,

for instance grain boundaries, defects, dislocations, mixed phases, amorphous re-

gions, impurities, patches of oxides or other foreign phase, etc. all of which can

affect the energy levels.

As the surface states will form bands of energy levels, so we may ignore the fact

and often consider the surface energy levels as being single levels. In some cases

depending upon the situation it becomes necessary to recognize heterogeneity or

other causes of energy level broadening, but in simple arguments we can neglect

band formation.

In flat-band n-type semiconductors, electrons in the conduction band are at much

higher level than the energy level of the acceptor surface states. So, conduction

band electrons should be lost while going into these lower energy states and of

course they do due to decrease in the energy. This results in completely filled

lower levels and completely empty upper levels. Thus the electro-chemical poten-

tial of the electrons at the surface states is lower than the conduction band and

electrons move to the surface states. Similarly, in flat-band p-type semiconduc-

tors, electrons in the donor surface states will drop down to eliminate holes in the

valence band, so holes will be lost from the valence band in p-semiconductors.

Due to such transitions (for n-type case), a charge builds up at the surface with

the countercharge in the bulk, this countercharge being that of the donor ions.

When the charge has moved from the donor ions to the surface states, double

layer is formed, with the positively charged donor ions as space-charge layer on

one side and negatively charged surface states on the other side. An electric field

develops between these two charged layers, and the space charge layer refers to

the region where the uncompensated donor ions (or acceptor ions, in p-type) are

the only important charged species. The charge density from such ions is

Ni = ND −NA (1.4)

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Where ND is donor density and NA is acceptor density. This region is also

referred as depletion layer or exhaustion layer; this can be interpreted as mobile

carriers have been exhausted from the region and moved to the surface (Madou

& Morrison (1995)).

1.1.1.3 Poisson Equation

One dimensional Poisson equation states that

d2φ

dx2=

qNi

εεo

(1.5)

where φ is the potential and Ni is the net density of ions in the space-charge

region. Here ε is the dielectric constant of the semiconductor, and εo is the

permittivity of free space. Poisson’s equation describes the change in potential as

the function of distance through the space-charge region. The donor density ND

(for n-type material) or acceptor density NA (for p-type material) is independent

of x (the distance into crystal), because homogeneous doping has been done. We

define the parameter V as

V (x) = φb − φ(x) (1.6)

where b is the potential in the bulk of the semiconductor. By integrating Poissons

equation we getdV

dx=

qNi(x− xo)

εεo

(1.7)

where xo is the thickness of the space-charge region. The thickness of the space-

charge region is determined by the distance necessary to compensate all the sur-

face charge. At x ≥ xo the semiconductor is uncharged, so we have used the

boundary condition that dV/dx = 0 at x = xo. For n-type material NDxo(= Nixo)

is the number of electrons (per unit area) extracted from the surface region of

thickness xo, and this equals the number of electrons (per unit area) moved to

the surface

Nixo = Ns (1.8)

where Ns is the density of charged surface states. The integration of eqn.(1.7)

yields

V =qNi(x− xo)

2

2εεo

(1.9)

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because we defined V = 0 at x = xo. This leads to the Schottky relation; the

value of the surface barrier Vs (V at x = 0) is

V =qNix

2o

2εεo

(1.10)

The energy qVs is the energy that electrons must attain before they can move

to the surface energy levels. Using Ni = 1023 m−3, Vs = 1, and ε = 10 we get

xo ∼ 100nm and varies slowly as the inverse square root of Ni. Using Eqn.(1.8)

in (1.10) we find

V =qN2

s

2εεoNi

(1.11)

an important relation describing the potential difference between the surface and

the bulk (or, as qVs the energy difference of electrons between the surface and

the bulk) as a function of the amount of charge Ns on the surface.

The density of electrons at the surface on an n-type semiconductor, ns, is given

by following equation multiplied by the Boltzmann factor involving qVs

ns = Nce− (Ec−EF )

kT (1.12)

The density of electrons in the bulk, nb, for simple semiconductors with com-

pletely ionized donors and a negligible density of acceptor is ND, the density of

donors. Consequently,

ns = Nce− (qVs+Ec−EF )

kT = ND = e−qVskT (1.13)

or on basis of eqn. (1.11),

ns = NDe− q2N2

s2εεokTNi (1.14)

This assumes electronic equilibrium right up to the surface of the semicon-

ductor. The parameter ns becomes important when we are estimating the rate

at which electrons move to the surface to adsorb a gas or to conduct electricity

across an intergranular contacts. When the rate of the process is first order in

electron density, it is first order in ns.

1.1.1.4 Surface states

The presence of surface states (or adsorbed gases) that induces a depletion layer at

the semiconductor surface leads to change in the resistance of the semiconductor.

As discussed above (Eqn. 1.2), the conductance of n-type film is given by

G =σWt

L(1.15)

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Using Eqn. (1.1) and assuming nb = ND, Eqn. (1.15) becomes

G =NDqunWt

L(1.16)

Where W is the width, t is the thickness and L is the length of the film (or

single crystal). This is the conductance if there is no surface barrier and if ND

electrons per unit volume are present throughout the whole thickness. A surface

conductivity σs is defined as

σs = N ′qun (1.17)

G =σsW

L(1.18)

Where N is number of the carriers per unit area of the thin film (N = NDt).

The units of σs are mhos, it is very useful when thin films or surface effects are be-

ing studied, because the surface conductivity can be defined with the Eqn. (1.18),

without the actual thickness of the region under consideration being known. In

the case of thin film, the conductance measured must be same by either the

surface or bulk conductivity, because N = NDt, it is the same.

For surface depletion layers, the concept of surface conductivity becomes sig-

nificant because extraction of electrons from the surface region can be interpreted

as a change in the surface conductance. Specifically, the conductance as defined

by Eqn. (1.15) will now change by the loss of conductance due to the surface

effects. The change in the conductance is defined as the surface conductance Gs,

calculated by the density of charge on the surface:

δG = Gs =NsqunW

L(1.19)

The loss of conductance due to the surface is usually a small fraction of the

bulk conductance G unless the sample is very thin (Madou & Morrison (1995)).

1.1.2 Conductance in the compressed powders

Compressed powders are of utmost importance in the semiconductor sensors. The

resistance of a compressed powder pellet strongly depends on the adsorbing gases.

Fig 1.1 (a) shows the schematic of a few grains of powder in contact and shows the

space-charge (depletion) region around the surface of each grain and in particular

at the intergranular contact. The space charge region being depleted of current

carriers is more resistive than the bulk. Thus the intergranular contact provides

8


most of the sample resistance. Fig 1.1 (b) represents the band model of the same

group of the grains. It can be visualized that the carriers must overcome the

substantial barrier of qV , in order to cross from grain to one another. Thus, the

current is proportional to the density of electrons ns with energy qV . With Go a

proportionality constant accounting for all other less sensitive factors determining

the conductance G, hence:

G = Goe− qV

kT (1.20)

+ + + + + + + + + + +

regionDepletion

O− O−O−

O−O− O−

O−O−O−

O−O−

O−

O−O−

O−

O−

O−

O−

O−

O−

O−O−

Barrier

OxygenAbsorbed

Electronic current

(a) PHYSICAL MODEL

Conduction bandelectrons

(b) BAND MODEL

Donors

+ + + + + +

Figure 1.1: Grains of semiconductor, showing how intergranular contact resis-tance appears and is analyzed. Image adapted from Madou & Morrison (1995)

The capture of electrons by adsorbed oxygen leads to a significant V (surface

barrier potential), causing the conductance change in the semiconductor pellet

that is used in the gas sensing. Surface states at the grain boundaries rather than

oxygen dominates in controlling V (Kulwicki (1984)).

1.1.3 Adsorption

Phenomenon of adsorption can be broadly classified into two categories: ph-

ysisorption and chemisorption. A chemisorbed species can interact more strongly

9


with the solid, and it results in the formation of new phase. In either type of

adsorption of an adsorbate (the adsorbing species), there is no movement of the

atoms of the adsorbent (the solid) from their normal lattice position. In the case

of chemisorption, there is movement and relocation of the surface atoms. The

formation of new phase can be understood as the point where the surface atoms

of the adsorbent change their bond structure, breaking bonds to the solid and

replacing them with the bonds to the adsorbate. The phenomenon of ionosorp-

tion form the basis of gas sensor mechanism, that is adsorption as surface state,

where charge is transferred from the conduction or valence bands to ionize the

adsorbate, but where the local bonding of the adsorbate to one or few atoms of

the solid can be ignored.

The dependence of rate of physisorption and chemisorption on the pressure

of the species in the gas phase can be assumed to follow the Henry’s law, which

is, “rate is proportional to the pressure”. Although this is complex relation

which may involve many analytical and empirical expressions still Henry’s law is

reasonably well applied to the low coverage of the surface, but it is inadequate

for the monolayer.

1.1.4 Physisorption

Physisorption is weak adsorption, usually associated with dipole-dipole interac-

tion between the adsorbate and the adsorbent. Every molecule approaching the

surface can polarize and induce an equivalent dipole in the adsorbent that typ-

ically leads to binding interaction of adsorbate to the surface. The energy of

the system is represented as a function of adsorbate/adsorbent separation, d, by

curve ’a’ in Figure 1.2. This figure is Lennard-Jones representation of physiosorp-

tion and chemisorption. As far as physiosorption is concerned the system is at

zero energy (defined) with infinite d, and it develops a dipole-dipole attraction

as the adsorbate approaches the surface and develops “billiard ball” repulsion as

d approaches zero. Physiosorption, with its small heat of adsorption, denoted by

∆Hphys in Figure 1.2, characterized by a high coverage θ at low temperature and

low coverage at high temperature.

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A

E

(a)

(b)

Hphys

chemH

d

E

(energy of system)

Figure 1.2: Lennard-Jones Model of physiosorption and chemisorption; (a) phys-iosorption of a molecule, (b) chemisorption, where at d = ∞ enough energy hasbeen introduced to dissociate the molecule. Image adapted from Madou & Morri-son (1995)

1.1.5 Chemisorption

For the stronger chemical bonding of chemisorption, normally a gas molecule

must dissociate into atoms. In Figure 1.2, the curve b represents chemisorption.

In this case adsorbate has substantial energy even at distance d, to account for

the dissociation energy provided. However, when atoms approach the surface

and strong chemical bonds are formed, the adsorbate energy becomes much more

negative than that of physiosorption. The heat of chemisorption ∆Hchem can

approach the heat of compound formation and in rare cases, exceed it.

1.1.6 Lennard-Jones Model

Lennard-Jones Model is a simple way to visualize the development of the acti-

vation energy ∆EA of chemisorption. This is minimum energy required by the

molecule to chemisorb. Suppose, in Figure 1.2, a molecule approaching the solid,

with the total energy zero as indicated in the curve ’a’. It is not necessary to

provide total energy of dissociating the molecule before it can chemisorb; a lesser

11


energy ∆EA suffices when the molecule is near the surface, where in the Lennard-

Jones model two curves a and b intersect. Thus the rate of adsorption is:

dθ

dt= kadse

−∆EAkT (1.21)

where θ is the fraction of available surface sites covered. Equation (1.11)

represents the rate for the normal case where ∆EA is provided thermally. In

this case, by eqn. (1.11), at a very low temperature cannot realistically occur.

Desorption, on the other hand, is represented in the Lennard-Jones model by

the energy ∆EA + ∆Hchem, where ∆Hchem is the heat of chemisorption. The net

adsorption rate can be given by the equation:

dθ

dt= kadse

−(∆EAkT

) − kdesθe−(

∆EA+∆HchemkT

) (1.22)

Setting dθdt

= 0 for steady state leads to an equilibrium coverage θ:

θ =kads

kdes

e(∆Hchem

kT) (1.23)

and θ decreases rapidly with increasing temperature. In the simple formula-

tion assumption is that the θ is very low, so the availability of the sites is not

limiting.

Thus the coverage of chemisorbed species shows an apparent maximum with

increasing temperature: The low values at low temperature occurring simply

because the rate of adsorption is negligible so in real time equilibrium is not

reached; the low values at high temperature occurring because when equilibrium

chemisorption is possible the coverage decreases with increasing temperature.

Figure 1.3 shows the schematic of temperature dependence of the coverage for

physisorbed and chemisorbed species on a solid.

From the Lennard-Jones model characteristics of the activation energy and

heat of adsorption are almost always practically apparent. Both activation energy

and heat of adsorption are dependent on the coverage θ. Specifically, ∆Hchem

decreases with increasing θ and ∆EA increases with θ, sometimes starting at zero

for θ = 0. The former can be attributed in part to the heterogeneity of the

surface. The first atoms chemisorbed will tend to be adsorbed on sites with

highest ∆Hchem, and only as these are occupied will the lower ∆Hchem sites

12


become occupied. Thus ∆Hchem will appear to decrease with θ. The Lennard-

Jones model explains the cause for ∆EA will increase as ∆Hchem decreases; from

Fig. 1.2 it can be observed that the deeper the well associated with ∆Hchem the

smaller is ∆EA.

Due to variation of ∆EA and ∆Hchem with θ, the surface coverage for chemisorp-

tion is not zero at low temperature, for when ∆EA is approaching zero at θ =

0, then, from eqn. (1.21), some chemisorption will occur even at low temper-

atures. As the temperature increases, amount of adsorbed gas will increase.

Consider gas adsorption to some rate dθdt

= a, from eqn. (1.21), we adsorb gas

until ∆EA = kT ln a/kad The higher the temperature higher the value of ∆EA

reached, and thus higher θ reached. A maximum adsorption is obtained at some

temperature Tmax. Below Tmax the chemisorption is irreversible because the rate

of desorption is negligible. Above this temperature the rate of desorption is

governed by the activation energy ∆EA + ∆Hchem becomes appreciable, and

equilibrium adsorption is attained the rate of adsorption becomes equal to the

rate of desorption. At temperature beyond Tmax the surface coverage decreases

with increasing temperature.

At low temperature there occurs physisorption which decreases with increasing

temperature. At very high temperature there exists equilibrium chemisorption,

it also decreases with increasing temperature (Madou & Morrison (1995)).

1.1.7 Ionosorption

Ionosorption is the process that predominately affects the phenomenon of gas

sensing. In this case there is no local adsorbate-to-surface atom bonding, but the

adsorbate acts as a surface state, capturing an electron or hole, and is held to

the surface by electrostatic attraction. In gas sensors ionosorption of oxygen is

very important, it can be ionosorbed in several forms: O−2 , O− and O2−. The

doubly charged adsorbed oxygen, is least expected for adsorbed species because

such high charge on the ion may lead to instability unless the site has a very high

Madelung potential. The Madelung potential is the potential at the site arising

due to all point charges, ions, in the crystal. To stabilize the doubly charged oxy-

gen, Madelung potential at the site would have to be extraordinary large positive

potential. Measurements of electron spin resonance on oxides show signals cor-

13


Tmax

Physisorption

EquilibriumChemisorption

IrreversibleChemisorption

Temperature, T

Vol

ume

Ads

orbe

d

Figure 1.3: Typical adsorption isobar. Image adapted from Madou & Morrison(1995)

responding to O−2 and O− that are easily distinguishable. When reducing agents

are introduced, the signal corresponding to the O− rapidly disappears, indicating

that O− is far more reactive among two forms. The O− form probably will have

some local interaction with the solid, but they can be ignored at this point of

discussion.

Local bonding of adsorbates to a solid, bonding that has negligible effect on

the carrier concentration, can still be important, because it can show up as a

variation of ∆EA and ∆Hchem with θ as discussed earlier. In the semiconductor

gas sensors the desired chemical reaction is the oxidation of the adsorbed reduc-

ing gas that is to be detected.

Adsorbed oxygen is a dominant contributor to the negatively charged surface

states. In air, the most common operating atmosphere for gas sensors, oxygen

is strongly adsorbed and the negatively charged oxygen dominates the surface

charge. So, oxygen adsorption leads to a high resistance in n-type semiconduc-

14


tors. The reaction of the reducing hydrocarbons removes the adsorbed oxygen,

restores electrons to the conduction band and decreases the resistance of the

semiconductor. This sensitivity of the resistance to the coverage of the adsorbed

oxygen leads to a direct relation between resistance and the partial pressure of

the reducing agent, and thats how standard semiconductor gas sensor detect the

presence of the reducing agent in the air (Sberveglieri et al. (1989)).

The concentration of charge on the surface is limited, as from eqn. (1.11)

when a reasonable limit is placed on the chemically induced surface barrier Vs.

The usual maximum for qVs is approximately 0.5 to 1 eV, as discussed earlier

the equilibrium qVs reflects the difference in the initial electrochemical potentials

for electrons, here between the solid and redox couple O2/O−2 or O2/O

−. Such

differences in electrochemical potential will in general be modest; the concentra-

tion of ionosorbed oxygen is limited as is directly seen from eqn. (1.11), where a

reasonable limit (0.5 eV) is placed on Vs.

In next section we will discuss more elaborately the ionosorption process,

especially oxygen and its effect on sample resistance.

1.1.8 Models for Semiconductor Gas Sensor: Electrical

effects of Adsorbed Gases

In the section 1.1.1 the effect of surface charge on the band structure of a semi-

conductor was discussed generally. It was discussed that the capture of electrons

or holes at the surface has a dominating effect on the surface barrier qVs. This

concept will be discussed here in more detail; we will be interested in the effect

of adsorbed gases, mostly the effect of adsorbed oxygen, on the semiconductor

surface properties. We will analyze first the influence of the surface barrier on

the conductance, particularly of a pressed pellet.

1.1.8.1 Conductance of a Pressed Pellet

From eqn. (1.20) as we know the conductance of a pressed pellet:

G = Goe− qVs

kT (1.24)

With reference to Figure 1.1(a) where we show three grains of a pressed pellet,

Figure 1.1(b), describes the band model of those three grains. We will consider

15


these grains to be n-type semiconductor such as SnO2. The adsorption of oxygen

extracts electrons from the surface region, indicated by the space-charge region

and for conductance electrons must cross this now insulating region. The inter-

granular contacts become high resistance parts of the pellet. From Figure 1.1(b)

it is observed that transfer of electrons from pellet to pellet must involve exci-

tation of the electron over the surface barrier (where the surface barrier is due

to the double potential drop between the adsorbed oxygen and the positively

charged donors in the space-charge region). Thus the Boltzmann factor enters

in eqn. (1.20), and only a fraction, e−qVskT , of the conduction band electrons can

cross from grain to grain. Thus the conductance is proportional to this factor,

and Go is a constant that depends on the contact area and other factors, such

as mobility, that are expected vary slowly with temperature than is exponential

factor. The value of Vs will normally be a function of temperature.

1.1.8.2 Surface Barrier Vs as a function of temperature and Oxygen

pressure

The variation of Vs as a function of temperature will be due to the variation of

oxygen adsorption with temperature and possibly to the variation of the occu-

pancy of other surface states with temperature. We will emphasize on oxygen

adsorption and its variation with temperature.

Let us describe in a simple model the adsorption of oxygen, assuming equi-

librium chemisorption (no reducing agent present) and using the reaction steps:

e− + O2 O−2 (1.25)

O2 2O (1.26)

e− + O O− (1.27)

All reactants in these equations are considered adsorbed. We will assume

that the physisorption of oxygen follows Henry’s Law; that is the amount of

uncharged oxygen adsorbed is constant and proportional to the oxygen pressure

in the atmosphere.

16


First, let us compare the predictions of this case to the qualitative features

of Lennard-Jones model. For simplicity and for this comparison we will consider

only adsorption as O−2 . We can write eqn. (1.25), that the net rate of O−

2

adsorption is

dO−2

dt= kadsns[O2]− kdes[O

−2 ] (1.28)

where from eqn. (1.14)

ns = NDe− q2N2

s [O−2 ]2

2εεokTNi

Comparing this to eqn. (1.22), we immediately see that ∆EA =q2[O−2 ]

2

2εεoNiand

the activation energy for adsorption increases rapidly with the amount adsorbed.

The energy of adsorption in the simplest case (no local bonding) is the difference

in the electrochemical potential for electrons (Fermi energies) between solid and

the adsorbed oxygen. From Fig. 1.4 we note that with no surface charge (O−2 = 0)

the difference is large, and the difference goes to zero as we approach equilibrium

adsorption. Thus the model for ionosorption immediately gives rationale for the

increase in ∆EA and the decrease in ∆Hchem with increasing θ, as discussed in

the Lennard-Jones model.

EO−2

FE

EO−2

FE

sqV

E cs

(a) (b)

µ

Figure 1.4: Sketch showing variation of EF −Eo−2 with qVs; (a) qVs = 0, flatbandcase, [O−

2 ] = 0; (b) equilibrium adsorption. Image adapted from Madou & Morrison(1995)

17


We calculate the total adsorption. Here we analyze the eqns. (1.25), (1.26)

and (1.27) at equilibrium. We do not imply that there is actually an appreciable

amount of atomic oxygen on the surface, as would appear from eqn. (1.26); at

equilibrium the reaction route is unimportant. Thus the concentrations of O−2

and O− will not depend on the exact route by which they are formed. We apply

the mass action law to eqn. (1.26) to obtain:

[O−2 ]

[O2]= e−

(EO2−EF )

kT (1.29)

[O]2 = [O2]e−∆G

kT (1.30)

[O−]

[O]= e−

(EO−EF )

kT (1.31)

where EO2 and EO are the energy levels of the oxygen molecule and the oxygen

atom respectively, and ∆G is the free energy change associated with the reaction

in eqn. (1.26). The brackets indicate surface concentration. As discussed above

we assume [O2] is proportional to oxygen pressure. PO2 .

Firstly in the case of sensors, the species O− is more reactive than O−2 and

more sensitive to the presence of organic vapours or reducing agents in general.

Secondly total surface charge as a function of parameters such as temperature

and partial oxygen pressure, as it determines the resistance to be measured as

discussed under.

Manipulating eqns. (1.29) to (1.31), we find

[O−2 ]

[O−]= [O2]

1/2e−(EO2

−EO−∆G/2)

kT (1.32)

so we can conclude that the relative concentration of O− decreases as the

oxygen pressure increases, but increases as temperature increases.

Total adsorption of negatively charged oxygen is more complicated to obtain

than the above ratio in eqn.(1.32), because the surface barrier changes if the

ionosorption [O−] + [O−2 ] changes. As the surface barrier changes, the differences

EO − EF and EO2 − EF change as indicated in Fig. 1.4. Fig. 1.4a shows the

band model and the surface barrier with no adsorption; Fig. 1.4b indicates the

structure after adsorption of the negatively charged species. Clearly the values

18


of the exponents in the eqns. (1.29) to (1.31) depend on Vs. We will assume that

there is a surface charge No present on the surface due to surface states before

adsorption occurs. Then,

Ns = No + [O−2 ] + [O−] (1.33)

or also,

Ns = No + [O−2 ](1 + α1/2BP

−1/2O2

) (1.34)

Where PO2 is the oxygen pressure (we assume PO2 = α[O2]) and B is a constant

(independent of pressure), namely the exponential factor in eqn. (1.32). From

Fig. 1.4 we see that

EF = Ecs − µ− qVs (1.35)

where µ is the energy difference between the conduction band in the bulk and

the Fermi energy. We can substitute the value for [O−2 ] from eqn. (1.29) into eqn.

(1.34), and using eqn. (1.35) and eqn. (1.11) (with Ni = ND), obtain Ns as a

function of PO2 :

ln{ (Ns −No)α

(1 + α1/2BP−1/2O2

)PO2

} = −{EO2 − Ecs + µ +q2N2

s

2εεoND

} (1.36)

If δEF < kT as adsorption occurs, where we obtain δEF from eqn. (1.35)

δVs < kT , we can write

δEF =δN2

s q2

2εεoND

< kT (1.37)

This limit represents a very low oxygen pressure where very little is adsorbed;

δNs is small. Then the right hand side of eqn. (1.36) can be considered constant,

and the surface charge will vary with PO2 by a simple law controlled by whether

O− or O−2 is the dominant species:

Ns −No = A(PO2 + CP1/2O2

) (1.38)

where A and C are constants (Nanis & Advani (1982)).

If the oxygen pressure becomes high so that the Fermi energy changes signif-

icantly from its zero-pressure value, or δNs in eqn. (1.37) becomes significant,

19


then the right hand side of eqn. (1.36), varying with N2s , begins to dominate.

The value of N2s begins to vary as logarithm of Ns − No or logarithm of the

pressure. The surface coverage Ns becomes essentially constant, independent

of pressure. Physically this simply means that the band bending stops further

adsorption. The energy levels of the surface states have approached the Fermi

energy (Fig.1.4), and a substantial increase in the amount adsorbed cannot oc-

cur because the resulting increase in Vs would raise the levels effectively above

EF and the ions would lose their electrons (according to Fermi statistics). As a

consequence Ns saturates.

In nutshell, for equilibrium oxygen adsorption on n-type semiconductors, the

surface will normally be saturated with oxygen at any pressure approaching at-

mospheric, and Ns will be independent of the oxygen pressure.

1.1.8.3 Resistance of a pressed pellet in the presence of a reducing

gas

For sensors we are usually not dealing with equilibrium but with steady state.

Oxygen is being adsorbed at a particular rate, but is being removed at the same

rate (Strassler & Reis (1983) Boyle & Jones (1977)) by reaction with a reducing

agent R. The mathematics then becomes quite different (Morrison (1987)). The

oxygen adsorption could be represented by set of equations, such as

e− + O2

k1(Kad)−−−−−−⇀↽−−−−−−k−1(Kdes)

O−2 (1.39)

e− + O−2

k2−→ 2 O− (1.40)

and the reaction of the gas to be detected can be represented by reactions

such as

R + O−2

k3−→ RO2 + e− (1.41)

R + O− k4−→ RO + e− (1.42)

where the k ’s are the rate constants for the reaction (Morrison (1987)). The

reaction (1.41) does not occur at a significant rate, based on the normally high

reactivity of O− relative to the reactivity of O−2 . Reaction (1.39) may be assumed

20


as reversible at the temperatures usually used in the gas sensing, but reaction

(1.40) is irreversible because the reverse reaction would be second order in the

concentration of adsorbed O− while the rate of the reaction of R with O− can

be high. If reaction (1.40) were reversible and fast compared to reaction (1.42),

the gas sensor would not be sensitive to R, for the oxygen concentration at the

surface would be constant.

Another possible reaction could be considered, namely the direct injection of

electrons from the reducing agent R, affecting Vs. Such injection is not considered

likely (Tischer et al. (1980)), because on n-type semiconductors, especially in air

where the humidity neutralizes acidic or basic sites, there are no obvious activat-

ing sites for organics. This is not to preclude adsorption of the reducing agent by

local bonding, although the model will include the simplifying assumption that

if this is the case the overall process still gives a rate of reaction proportional to

the reducing agent pressure.

Consider the rates of reactions above to be first order in each reactant where

the concentration of the reactant e− is given by ns in eqn. (1.14), and we will

assume in accordance with our discussion that k3 is negligible-that is, O−2 is not

very reactive. The mass action law then gives

[O−2 ] = k1ns[O2]/(k−1 + k2ns) (1.43)

[O−] = 2nsk2k1ns[O2]

k−1 + k2ns

(k4[R])−1 (1.44)

Where the brackets represent concentrations (or if R reacts in (1.42) directly

from the gas phase, [R] is the arrival rate proportional to PR, the partial pressure).

The total surface charge Ns is the sum of the oxygen concentrations, as in eqn.

(1.33). Solving for ns, we obtain

{2k1k2[O2]/k4[R]}n2s + {k1[O2]− k2Ns}ns = k−1Ns (1.45)

As described by eqn. (1.14), ns varies exponentially with Ns, so in eqn.

(1.45) Ns varies logarithmically with ns and can be considered a constant during

moderate variations of [R] and ns. With eqn. (1.45) we can determine ns as

a function of [R], or the resistance R as a function of PR, the pressure of the

21


reducing agent. We assume PR is proportional to [R] and, from eqns. (1.13) and

(1.20), R (=1/G) is inversely proportional to ns:

R = α/ns; PR = γ[R] (1.46)

Where α and γ are proportionality constants. On differentiating eqn. (1.45)

by substituting eqn. (1.46) and find that the slope S of the log/log plot is pre-

dicted to be

S =d(logR)

d(logPR)= −1

2(1 +

1

1− 2aR/b) (1.47)

where a = k−1Ns and b = α(k1[O2]− k2Ns).

With PR small, so R is large, S approaches −12. Thus the resistance varies as

the inverse square root of the pressure of reducing agent:

R ∝ P−1/2R (1.48)

With PR large, the predicted slope depends on the sign of b. With b negative,

S → −1 as R → 0; with b positive S → 0 as R → b/a. The last case can be

clarified by solving eqn. (1.45) directly by assuming high [R], in which we obtain

R = (b/a)(1 + k1k2[O2]α2/2b2k4[R]) (1.49)

For the solution that is positive over all [R]. Thus as [R] → ∞, or PR → ∞,

R → b/a (Madou & Morrison (1995)) .

1.2 Nanocrystalline materials

Nanotechnology deals with small structures or small-sized materials. The typical

dimension spans from sub-nanometer to several hundred nanometers. A nanome-

ter (nm) is one billionth of a meter, or 10−9 m. One nanometer is approximately

the length equivalent to 10 hydrogen or 5 silicon atoms aligned in a line. Small

features permit more functionality in a given space, but nanotechnology is not

only a simple continuation of miniaturization from micron meter scale down to

nanometer scale. Materials in the micrometer scale mostly exhibit physical prop-

erties the same as that of bulk form; however, materials in the nanometer scale

may exhibit physical properties distinctively different from that of bulk. Mate-

rials in this size range exhibit some remarkable specific properties; a transition

22


from atoms or molecules to bulk form takes place in this size range. For example,

crystals in the nanometer scale have a low melting point (the difference can be

as large as 1000oC) and reduced lattice constants, since the number of surface

atoms or ions becomes a significant fraction of the total number of atoms or ions

and the surface energy plays a significant role in the thermal stability. Crystal

structures stable at elevated temperatures are stable at much lower temperatures

in nanometer sizes, so ferroelectrics and ferromagnetics may lose their ferroelec-

tricity and ferromagnetism when the materials are shrunk to the nanometer scale.

Bulk semiconductors become insulators when the characteristic dimension is suf-

ficiently small (in a couple of nanometers). Although bulk gold does not exhibit

catalytic properties, Au nanocrystal demonstrates to be an excellent low temper-

ature catalyst.

Currently there are a lot of different opinions about what exactly is nan-

otechnology. For example, some people consider the study of microstructures of

materials using electron microscopy and the growth and characterization of thin

films as nanotechnology. Other people consider a bottom-up approach in materi-

als synthesis and fabrication, such as self-assembly or bio-mineralization to form

hierarchical structures is nanotechnology. Drug delivery, e.g. by putting drugs in-

side carbon nanotubes, is considered as nanotechnology. Micro-electromechanical

systems (MEMS) and lab-on-a-chip are considered as nanotechnology. More fu-

turistic or science fiction-like opinions are that nanotechnology means something

very ambitious and startlingly new, such as miniature submarines in the blood-

stream, smart self-replication nanorobots monitoring our body, space elevators

made of nanotubes and the colonization of space. There are many other def-

initions that people working in nanotechnology use to define the field. These

definitions are true to certain specific research fields, but none of them covers the

full spectrum of nanotechnology. The many diverse definitions of nanotechnology

reflect the fact that nanotechnology covers a broad spectrum of research field and

requires true interdisciplinary and multidisciplinary efforts.

In order to explore novel physical properties and phenomena and realize po-

tential applications of nanostructures and nanomaterials, the ability to fabricate

and process nanomaterials and nanostructures is the first corner stone in nan-

otechnology. Nanostructured materials are those with at least one dimension

falling in nanometer scale, and include nanoparticles (including quantum dots,

23


when exhibiting quantum effects), nanorods and nanowires, thin films, and bulk

materials made of nanoscale building blocks or consisting of nanoscale structures.

Many technologies have been explored to fabricate nanostructures and nanoma-

terials. These technical approaches can be grouped in several ways. One way is

to group them according to the growth media:

1 Vapour phase growth, including laser reaction pyrolysis for nanoparticles

synthesis and atomic layer deposition (ALD) for thin film deposition.

2 Liquid phase growth, including colloidal processing for the formation of

nanoparticles and self assembly of mono-layers.

3 Solid phase formation, including phase segregation to make metallic parti-

cles in glass matrix and two-photon induced polymerization for the fabri-

cation of three-dimensional photonic crystals.

4 Hybrid growth, including vapor-liquid-solid (VLS) growth of nanowires.

Another way is to group the techniques according to the form of products:

1 Nanoparticles by means of colloidal processing, flame combustion and phase

segregation.

2 Nanorods or nanowires by template-based electroplating, solution liquid-

solid growth (SLS), and spontaneous anisotropic growth.

3 Thin films by molecular beam epitaxy (MBE) and atomic layer deposition

(ALD).

4 Nanostructured bulk materials, for example, photonic band gap crystals by

self-assembly of nanosized particles.

There are many other ways to group different fabrication and processing tech-

niques such as top-down and bottom-up approaches, spontaneous and forced

processes. Top-down is in general an extension of lithography. The concept and

practice of a bottom-up approach in material science and chemistry are not new

either. Synthesis of large polymer molecules is a typical bottom-up approach, in

which individual building blocks (monomers) are assembled to a large molecule or

polymerized into bulk material. Crystal growth is another bottom-up approach,

24


where growth species either atoms, or ions or molecules orderly assemble into

desired crystal structure on the growth surface (Cao (2004)).

1.2.1 Bottom-Up and Top-Down Approaches

Obviously there are two approaches to the synthesis of nanomaterials and the

fabrication of nanostructures: top-down and bottom-up. Attrition or milling is

a typical top-down method in making nanoparticles, whereas the colloidal dis-

persion is a good example of bottom-up approach in the synthesis of nanoparti-

cles. Lithography may be considered as a hybrid approach, since the growth of

thin films is bottom-up whereas etching is top-down, while nanolithography and

nanomanipulation are commonly a bottom-up approach. Both approaches play

very important roles in modern industry and most likely in nanotechnology as

well. There are advantages and disadvantages in both approaches. Among others,

the biggest problem with top-down approach is the imperfection of the surface

structure. It is well known that the conventional top-down techniques such as

lithography can cause significant crystallographic damage to the processed pat-

terns, and additional defects may be introduced even during the etching steps.

For example, nanowires made by lithography are not smooth and may contain a

lot of impurities and structural defects on surface. Such imperfections would have

a significant impact on physical properties and surface chemistry of nanostruc-

tures and nanomaterials, since the surface over volume ratio in nanostructures

and nanomaterials is very large. The surface imperfection would result in a re-

duced conductivity due to inelastic surface scattering, which in turn would lead

to the generation of excessive heat and thus impose extra challenges to the device

design and fabrication. Regardless of the surface imperfections and other defects

that top-down approaches may introduce, they will continue to play an impor-

tant role in the synthesis and fabrication of nanostructures and nanomaterials.

Bottom-up approach is often emphasized in nanotechnology literature, though

bottom-up is nothing new in materials synthesis. Typical material synthesis is to

build atom by atom on a very large scale, and has been in industrial use for over

a century. Examples include the production of salt and nitrate in chemical indus-

try, the growth of single crystals and deposition of films in electronic industry.

For most materials, there is no difference in physical properties of materials re-

25


gardless of the synthesis routes, provided that chemical composition, crystallinity,

and microstructure of the material in question are identical. Of course, differ-

ent synthesis and processing approaches often result in appreciable differences

in chemical composition, crystallinity, and microstructure of the material due to

kinetic reasons. Consequently, the material exhibits different physical properties.

Bottom-up approach refers to the build-up of a material from the bottom: atom-

by-atom, molecule-by-molecule, or cluster-by-cluster. In organic chemistry and/

or polymer science, we know polymers are synthesized by connecting individual

monomers together. In crystal growth, growth species, such as atoms, ions and

molecules, after impinging onto the growth surface, assemble into crystal struc-

ture one after another. Although the bottom-up approach is nothing new, it

plays an important role in the fabrication and processing of nanostructures and

nanomaterials. There are several reasons for this. When structures fall into a

nanometer scale, there is little choice for a top-down approach. All the tools we

have possessed are too big to deal with such tiny subjects.

Bottom-up approach also promises a better chance to obtain nanostructures

with less defects, more homogeneous chemical composition, and better short and

long range ordering. This is because the bottom-up approach is driven mainly

by the reduction of Gibbs free energy, so that nanostructures and nanomaterials

such produced are in a state closer to a thermodynamic equilibrium state. On the

contrary, top-down approach most likely introduces internal stress, in addition to

surface defects and contaminations (Cao (2004)).

As discussed in Sec. 1.1 gas-sensing is the surface phenomenon and we should

understand the chemistry and thermodynamics of the surfaces of nanocrystalline

materials.

1.2.2 Physical Chemistry of Solid Surfaces

Nanostructures and nanomaterials possess a large fraction of surface atoms per

unit volume. The ratio of surface atoms to interior atoms changes dramatically if

one successively divides a macroscopic object into smaller parts. This dramatic

increase in the ratio of surface atoms to interior atoms in nanostructures and

nanomaterials might illustrate why changes in the size range of nanometers are

expected to lead to great changes in the physical and chemical properties of

26


the materials. The total surface energy increases with the overall surface area,

which is in turn strongly dependent on the dimension of material. Due to the

vast surface area, all nanostructured materials possess a huge surface energy and,

thus, are thermodynamically unstable or metastable. One of the great challenges

in fabrication and processing of nanomaterials is to overcome the surface energy,

and to prevent the nanostructures or nanomaterials from growth in size, driven

by the reduction of overall surface energy. In order to produce and stabilize

nanostructures and nanomaterials, it is essential to have a good understanding

of surface energy of solid surfaces.

1.2.2.1 Surface Energy

Atoms or molecules on a solid surface possess fewer nearest neighbors or coor-

dination numbers, and thus have dangling or unsatisfied bonds exposed to the

surface. Because of the dangling bonds on the surface, surface atoms or molecules

are under an inwardly directed force and the bond distance between the surface

atoms or molecules and the sub-surface atoms or molecules, is smaller than that

between interior atoms or molecules. When solid particles are very small, such a

decrease in bond length between the surface atoms and interior atoms becomes

significant and the lattice constants of the entire solid particles show an appre-

ciable reduction. The extra energy possessed by the surface atoms is described

as surface energy, surface free energy or surface tension. Surface energy,γ, by

definition, is the energy required to create a unit area of new surface:

γ = (δG

δA)ni,T,P (1.50)

where A is the surface area. Let us consider separating a rectangular solid

material into two pieces as illustrated in Fig. 1.5. On the newly created surfaces,

each atom is located in an asymmetric environment and will move towards the

interior due to breaking of bonds at the surface. An extra force is required to

pull the surface atoms back to its original position. Such a surface is ideal and

also called singular surface. For each atom on such a singular surface, the energy

required to get it back to its original position will be equal to the number of

broken bonds, Nb, multiplying by half of the bond strength, ε. Therefore, the

surface energy is given by:

γ =1

2Nbερa (1.51)

27


where ρa is the surface atomic density, the number of atoms per unit area on

the new surface.

Figure 1.5: Schematic showing two new surfaces being created by breaking arectangular solid into two pieces. Image adapted from Cao (2004)

This relation only gives a rough estimation of the true surface energy of a

solid surface, and is only applicable to solids with rigid structure where no surface

relaxation occurs. When there is an appreciable surface relaxation, such as the

surface atoms moving inwardly, or there is a surface restructuring, surface energy

will be lower than that estimated by the above equation.

Thermodynamics tells us that any material or system is stable only when it is

in a state with the lowest Gibbs free energy. Therefore, there is a strong tendency

for a solid or a liquid to minimize the total surface energy. There are a number of

mechanisms to reduce the overall surface energy. The various mechanisms can be

grouped into atomic or surface level, individual structures and the overall system.

For a given surface with a fixed surface area, the surface energy can be reduced

through:

(i) surface relaxation, the surface atoms or ions shift inwardly which occur

more readily in liquid phase than in solid surface due to rigid structure in

solids,

(ii) surface restructuring through combining surface dangling bonds into strained

28


new chemical bonds,

(iii) surface adsorption through chemical or physical adsorption of terminal

chemical species onto the surface by forming chemical bonds or weak at-

traction forces such as electrostatic or van der Waals forces, and

(iv) composition segregation or impurity enrichment on the surface through

solid-state diffusion.

At the overall system level, mechanisms for the reduction of overall surface

energy include:

(i) combining individual nanostructures together to form large structures so as

to reduce the overall surface area, if large enough activation is available for

such a process to proceed, and

(ii) agglomeration of individual nanostructures without altering the individual

nanostructures.

Specific mechanisms of combining individual nanostructures into large struc-

tures include:

(a) sintering, in which individual structures merge together and

(b) Ostwald ripening, in which relatively large structures grow at the expense

of smaller ones.

In general, sintering is negligible at low temperatures including room temper-

ature, and becomes important only when materials are heated to elevated tem-

peratures, typically 70% of the melting point of the material. Ostwald ripening

occurs at a wide range of temperatures, and proceeds at relatively low tempera-

tures when nanostructures are dispersed and have an appreciable solubility in a

solvent (Cao (2004)).

Before we formulate the current problem and provide a suitable solution ex-

tensive literature survey becomes mandatory. In the following section we will

discuss the work that has been reported by other authors.

29


1.3 Literature Survey

Over the past few decades solid state gas sensors based on metal oxides have be-

come the predominant solid-state devices for gas alarms used in domestic, com-

mercial and industrial premises. Large variety of ceramic thick-film and thin film

devices have developed over the years. Numerous materials have been reported

to be important for metal oxide sensors design including both single and multi

component oxides as published in review articles by Gopel & Schierbaum (1995),

Moseley (1997), Eranna et al. (2004), Korotcenkov (2007) and Pasierb & Rekas

(2009). It has been established that the materials in different structural states can

be used in resistive type gas sensors. These states include amorphous-like state,

glass-state, nanocrystalline state, polycrystalline state and single crystalline state.

Each state has its own unique properties and characteristics that can affect sensor

performance. However, in practice nanocrystalline and polycrystalline materials

have found the greatest application in solid state gas sensors (Schierbaum et al.

(1992), Watson et al. (1993), Schierbaum (1995) and Kohl (2001)).

Nanocrystalline and polycrystalline materials have the optimal combination

of critical properties for sensor application including high surface area due to

small crystallite size, low cost design technology and stability of both structural

and electro-physical properties.

Typically amorphous-like and glassy materials are not stable enough for gas

sensing applications, especially at high temperature (Tsiulyanu et al. (2004)).

Single crystalline and epitaxial materials have maximum stability and therefore

the use of materials in these states for gas sensors may improve the temporal

stability of the sensor. Unlike polycrystalline material, devices based on epitaxial

and single crystalline materials will not be plagued with the problem of instability

of grain size. However the high cost and technological challenges associated with

their deposition limit their general use in gas sensors.

One-dimensional structures, which are single crystalline materials, can be syn-

thesized using inexpensive and simple technology (Dai et al. (2002)). Wide use

of one-dimensional structures is however impeded by the great difficulties re-

quired for their separation and manipulation (Deb et al. (2007) and Huang &

Choi (2007)). During the synthesis process of one-dimension structures one may

observe a considerable diversity in their geometric parameters. In polycrystalline

30


and nanocrystalline material we work with averaged grain size, while using one-

dimensional structures each sensor is characteristic of its specific geometry of

the one-dimensional crystal. Therefore, the reproducibility of performance pa-

rameters for sensors based on one-dimensional structures would depend on the

uniformity of those structures. Unfortunately, the problem of separation, sizing

and manipulations of one-dimensional structures has not been resolved yet. To

achieve uniform sizing and orientation, new advanced technologies will need to

be implemented, and these would be expensive and not accessible for wide use.

There are a few interesting proposals for controlling one-dimensional structures

(Heo et al. (2004)), but they require further improvement for practical implemen-

tation. Thus the gas sensors based on 1-dimensional structures are not readily

available commercially. Furthermore, the sensors based on one-dimensional struc-

tures would be much more expensive than that of polycrystalline devices. So, it

can be simply inferred from above arguments that in the near future, polycrys-

talline materials would remain the dominant materials for solid-state gas sensors.

Nano- and polycrystalline materials are very complicated objects for study,

because the electro-conductivity of these materials depends on number factors

(Xu et al. (1991) and Baraton & Merhari (2004)). It is necessary to understand

the role of morphology and crystallographic structure in gas sensing effects, quite

a few authors have tried to establish the relation between structural parameters

and sensing response. One cannot find a volume of good works in this context.

Xu et al. (1991), Yamazoe (1991), Sakai et al. (2001b) and Abe et al. (2005) have

tried to find the correlation between grain size of metal oxides and response of

gas sensors . Shimizu et al. (1998), Hyodo et al. (2002) and Hyodo et al. (2003)

studied the influence of porosity of the material on the sensing response. Cirera

et al. (2000), Dieguez et al. (2000), Arbiol et al. (2002) and Mandayo et al. (2003)

studied the dependence of sensing response on the structural properties of metal

oxide. Korotcenkov et al. (2000), Korotcenkov et al. (2004b) and Korotcenkov

et al. (2005) have published number of papers on the thin film gas sensors and

laid an emphasis on widening the horizon for structural engineering of metal oxide

films for solid-state gas sensors.

While a lot of reviews and books have described the working principle of metal

oxide gas sensors in detail by Madou & Morrison (1995), Gopel & Schierbaum

(1995) , Moseley (1997), Kohl (2001), Barsan & Weimar (2001), Eranna et al.

31


(2004), Korotcenkov (2007) and Pasierb & Rekas (2009). The present study is

moreover concentrated over the issue to establish the correlation between the

particle-size and gas-sensing response. The introduction of dopant changes the

catalytic activity and the chemical composition. Additives or dopants could in-

fluence grain size, shape of crystallites, bulk and surface stoichiometry, properties

of inter-crystalline barriers and bulk electro-physical properties (Yamazoe (1991),

Lundstrom (1996), Williams (1999) and Barsan & Weimar (2003)).

1.3.1 Structural parameters of metal oxides controlling

gas-sensing characteristics

As discussed in the section 1.1 that the sensor operation is based upon the change

in the resistance (or conductance) of the gas-sensing material induced by the sur-

rounding gas. The changes are caused by the various processes which take place

both at the surface and in the bulk of gas-sensing material (Lantto & Rantala

(1991) and Barsan et al. (1999)). Fig 1.6 represents the possible parameters which

control the gas sensing properties pictorially.

Research has confirmed that all the processes indicated in Fig. 1.6 including

adsorption/ desorption, catalysis, reduction/ reoxidation, and diffusion are rele-

vant in gas sensors and influenced by structural parameters of the sensor material.

As gas sensing is a complex mechanism and it depends on numerous factors, it

is clear that we have to consider the influence of many structural parameters

of metal oxide matrix on gas sensors response. These parameters are enumer-

ated as thickness, grain size, porosity, grain faceting, agglomeration, film texture,

surface geometry, sensor geometry, surface disordering, bulk stoichiometry, grain

network, active surface area and size of necks.

Number of research groups Williams (1999), Barsan & Weimar (2001) and

Brinzari et al. (2001) have shown that the even a slight change in the above men-

tioned parameters influence sensing characteristics drastically. In the following

subsections the influence of these parameters on the gas sensing phenomenon as

reported in the literature will be discussed briefly.

32


Processes controlling the sensor response

Bulk oxygen diffusion in grains

Intergrain oxygen diffusion

Surface ReactionsDiffusion

Adsorption/ Desorption

Reduction/ Reoxidation

Gas diffusion inside metal oxide matrix

Catalysis

adsorbed speciesSurface diffusion of

adsorption species and bulkCharge transfer between

Chemical reactions(Composition transformation)

Figure 1.6: Pictorial diagram illustrating the processes controlling the sensorresponse. Image adapted from Korotcenkov (2007)

33


1.3.2 The role of sensor geometry and contacts

To measure the response of the semiconductor metal oxide material in the pres-

ence of the gas, many sensor geometries have been reported in the literature.

Some of the researchers have adapted the compressed pellet (may or may not be

sintered) with metal electrodes on each face (McAleer et al. (1987) and Blaser

et al. (1999)). In one of the electrode assembly Caldararu et al. (1996) reported

tin dioxide powder between two concentric tantalum cylinders as electrodes. Most

of the sensing devices have the sensing material deposited as thin (e.g. sputtered,

vacuum-evaporated, or deposited as a result of chemical reactions) or thick (e.g.

screen-printed) film on a substrate, number of planar shapes of electrode assem-

blies have been reported such as rectangular, inter-digital, circular etc. (Williams

& Pratt (1995),Vilanova et al. (1998), Capone et al. (2001), Schmidt-Zhang &

Guth (2004) and Toohey (2005)). Both the electrodes can be fabricated together

on the surface before or after the sensing film is deposited. This provides the

great flexibility in the fabrication process, as it needs not to be compatible with

the sensing material.

At first approximation the geometric parameters such as length (L) and width

(W) do not influence sensor response. The ratio L/W influences only sheet con-

ductivity of gas sensor (Gs). Appropriate adjustment of these design parameters

can achieve acceptable value of gas sensor resistance suitable for further electronic

processing.

However in practical situations are some what different, first of all, purely

geometric effect arises because the film conductance does not change instantly

or uniformly when the gas ambient changes; the gas must diffuse through the

film, reacting with the particle surfaces as it does so. This leads to variations

in the local film conductance. Vilanova et al. (1998) with the help of numerical

simulation indicated, for an instance, that where a sensor is highly sensitive to the

test gas, the sensitivity increased with the electrode spacing when the electrodes

were underneath the film, but decreased with the spacing when the electrodes

were deposited over the film. Moreover they also concluded that if electrode

spacing was reduced to less than the film thickness, it was possible to detect a

less-reactive gas. Williams & Pratt (1995) have also investigated in one of their

works that if two or more pairs of contacts with different separations are made on

34


the sensor, then the conductance measured between any two pairs under a given

set of conditions will be related by a known function, even though the individual

values, will of course change with the test gas concentration.

For thin films, above-mentioned effect does not hold good. However, even

in this case the electrode distance may be a strong influencing factor. Capone

et al. (2001) have shown that the decrease of the distance between inter-digitated

electrodes from 400 µm to 200 µm may enhance the CO response in ceramic type

SnO2 based sensors. Even greater differences in sensor parameters could appear

when the distance between measurement electrodes in a sensor becomes less than

some critical value.

1.3.3 The influence of thickness of film

For designing sensor three main approaches have been followed: (1) ceramics; (2)

thick film; and (3) thin film (Schierbaum et al. (1992)). Therefore, while analyzing

the influence of thickness on sensor parameters, for thick film and ceramic sensors

the grain size does not depend on the thickness; rather it is determined by the

conditions of synthesis and the thermal treatment parameters. The situation for

thin film sensors is fundamentally different. The grain size is determined directly

by the thickness of the deposited film. Brinzari et al. (2002) and Korotcenkov

et al. (2005) have studied the regularities of SnO2 and In2O3 films deposited

by spray pyrolysis with respect to structural properties. For In2O3 based ozone

sensors they found that change in film thickness have lead to change in both the

magnitude of the sensors response and optimum operating temperature. When

the film thickness have been increased from 20 nm to 400 nm, the response to

ozone have dropped by more than a factor 100. This fall in the sensitivity was

attributed to increase in grain size and decrease of gas permeability within the

film. This effect was observed for oxidizing gases.

Regarding the detection of reducing gases, especially hydrogen, the effect of

thickness on the sensitivity have been observed otherwise. In other words thick

films are better sensors for hydrogen and other reducing gases. Thin In2O3 films,

deposited from dilute solutions, has lower sensitivity to hydrogen than thick film.

Similar effect was observed by Sakai et al. (2001b) for SnO2 films deposited by

spin-coating technique. This effect was explained by Williams & Pratt (1995),

35


Shimizu et al. (1998) and Sakai et al. (2001a) and diffusion-reactive model of gas

sensitivity was developed. Shimizu et al. (1998) have claimed that increased sen-

sitivity H2 in thick films arises because H2 has a much higher diffusion coefficient

than oxygen.

Dependence of sensitivity of sensors fabricated by thick film technology on

thickness is an important aspect to study. Different authors have observed dif-

ferent dependencies with thickness. Park & Mackenzie (1996) have observed an

increase in thickness to reducing gases with increased film thickness, while Bruno

et al. (1994) have observed a loss in sensitivity with thickness, and Montmeat

et al. (2004) have observed that sensor response would reach a maximum at some

thickness. Such disagreements demonstrate that gas sensitivity of metal oxides

is dependent on number of factors, which are hard to control.

Other important aspect of the influence of film thickness on gas sensor per-

formance pertains to response and recovery times. The effect of film thickness on

the time constants of sensor response to ozone and hydrogen was studied by Man-

dayo et al. (2003). They have shown that the time constants of sensor response

increase as the film thickness increases. The effect is more pronounced with ox-

idizing gas than for reducing gases. Response and recovery times for reducing

gas exposure on In2O3-based thin film gas sensors increased nearly 10-fold as the

film thickness changed from 20 to 400 nm.

Thus, the gas sensor designers should decrease thickness to improve the sens-

ing characteristics of metal oxide based gas sensors. Thin films no doubt have fast

response and recovery, as shown empirically; there is not any diffusion limitation

in response kinetics in thin film devices as studied by Korotcenkov et al. (2004b).

Similar effects were also observed by Chang et al. (2002) in ZnO based gas

sensors fabricated by magnetron sputtering. Hyodo et al. (2002) concluded that

the response for thin film gas sensor with thickness d < 100 nm is faster than gas

sensor with thickness d > 100 nm. Kim et al. (2000) have also obtained similar

results with In2O3-based ozone sensors.

However, the diminution of film thickness has limitations controlled by the

properties of material itself and technical difficulty in forming a continuous layer.

Besides slow response and recovery processes in thick film and ceramic type sen-

sors, above certain thickness ceramic and thick films tends to crack. Korotcenkov

et al. (2004a) and Sakai et al. (2001a) have studied that as a result of crack-

36


ing, the film conductivity and gas permeability could have changed. The process

of cracking is hard to control, because the magnitude of the sensor parameters

change is dependent on the depth and length of the cracks.

1.3.4 Grain size influence

Barsan et al. (1999), Xu et al. (1991) and Rothschild & Komem (2004) have

studied the effect of microstructure on the electro-physical properties of the poly-

crystalline materials, moreover acceptable models based on grains and necks have

been formulated to rationalize the phenomenon. It has been established that the

grain size and the width of the necks are the main parameters that control gas-

sensing properties in metal oxide films as well. Barsan et al. (1999),Xu et al.

(1991), Rothschild & Komem (2004), Timmer et al. (2005), Yamazoe (1991),

Gas’kov & Rumyantseva (2001) and Schierbaum et al. (1991) have tried to fit the

impact of grains size and necks size on sensor response in the frame of modern

gas sensor models. Usually it is explained through so-called dimension effect a

comparison of the grains size (d) or neck width (x) with the Debye length (LD)

LD =

√εkT

2πe2N(1.52)

where k is Boltzmann constant, T is the absolute temperature, ε is the dielec-

tric constant of the material and N is the concentration of charge carriers.

In brief, for explanation of the “dimension effect“ on the gas-sensing effect it is

possible to provide the following argument given by Barsan & Weimar (2001) and

Schierbaum et al. (1991). For large crystallites with grain diameter d >> 2Ls,

where Ls is the width of surface space charge (Ls = LD

√eV 2

s /kT ), and for a

small width of necks (d < Ls), the conductance of both the film and ceramics

usually is limited by Schottky barriers (Vs) at the grain boundary. In this case

the sensitivity is practically independent of d.

In the case of d ∼ 2Ls every conducting channel in necks between grains

is overlapped. If the number of long necks is much larger the inter-grain con-

tacts, they control the conductivity of the gas-sensing material and define the

size dependence of the gas sensitivity.

If d < 2Ls, every grain is fully involved in space-charge layer and the electron

transport is affected by the charge at the adsorbed species. Ogawa et al. (1982)

37


have demonstrated that when the grain size becomes comparable to twice the

Debye length, a space-charge region can develop in the whole crystallite. The

later case is the most desirable, since it allows achievement of maximum sensor

response.

Yamazoe (1991) have suggested that it is necessary to note that the applica-

bility of grains or necks models depends strongly on the technological routes used

for metal oxides synthesis or deposition and sintering conditions. Usually the ap-

pearance of necks is a result of high temperature annealing (Tan > 700− 800oC).

Taking into account processes which take place at inter-grain interfaces during

high temperature annealing, one can assume that the forming of long necks in

inter-grain space is a consequence of mass transport from one grain to another.

According to Xu et al. (1991) for metal oxide samples after high temperature

annealing, the neck size (X) is proportional to grain size (d) with proportionality

constant (X/d) of 0.8 ± 0.1. However it is necessary to note that this constant

depends on the sintering parameters and may be different. For thin metal oxide

film and ceramics, which were not subjected to high temperature treatments, the

gas sensing matrix is formed from separately grown grains. Therefore, in such

metal oxides, the necks between grains are very short or are absent. It means

that for description of their gas sensing properties we can use the grains model.

According to this model, between the grains there are Schottky type contacts

with the height of potential barrier depending on the surrounding atmosphere.

In the frame of such approach the grain boundary space charge or band bending

on inter-grain interfaces are the main parameters controlling the conductivity of

nanocrystalline metal oxides. Bose et al. (2006) studied that SnO2 films with

grain size equaling 5-14 nm that at low operating temperatures (25 − 300oC),

both the grain and grain boundary contribute to the conductivity. At higher

operating temperatures (above 300oC), the grain boundary contribution for the

conductivity is dominant. The same results were obtained by Kaur et al. (2005)

for SnO2 films (d ∼ 0.6−1.0µm) during interaction with H2S and NH3 at 250oC.

The impedance of these films was mainly contributed by the potential barriers at

grain boundaries. Labeau et al. (1995) have found that the main contribution to

the sensor impedance of polycrystalline SnO2 sensors during interaction with CO

and C2H5OH arises from grain boundaries, although a small contribution from

the bulk was observed at high frequencies.

38


Brinzari et al. (2001), Korotcenkov et al. (2005), Xu et al. (1991), Timmer

et al. (2005), Wang et al. (1995), Yamazoe (1991) and Suzuki & Yamazaki (1990)

have experimentally confirmed the grain size influence on the sensor response and

they observed a drastic increase in sensitivity for SnO2 based gas sensor with grain

size smaller than a Debye length. Shimizu & Egashira (1999) established that

the sensitivity of sensors based on tin oxide nanoparticles dramatically increased

when the particle size was reduced down to 6 nm. Below this critical grain size,

sensor sensitivity rapidly decreased. Ogawa et al. (1982) calculated Debye length

of SnO2 to be LD = 3nm at 250oC, the highest sensitivity was actually reached

when the particle diameter corresponded to 2 nm. Wang et al. (1995) consid-

ered grain size effects in particulate semiconductor gas sensors, which contained

mixture of necks and grain boundary contacts, and concluded that the gas sen-

sitivity would increase sharply for particle diameter below about 35 nm. Ansari

et al. (1997) indicated that devices fabricated out of SnO2 particles of 20 nm

were around 10 times more sensitive than the sensors fabricated from 25 to 45

nm particles. Lu et al. (2000) found that SnO2-based sensor response to CO

increases drastically if particle diameter becomes smaller than 10 nm.

Because the Debye length depends on the concentration of free charge carriers

(Eqn. 1.52), it becomes clear that the observed variation of threshold value of

crystallites size is valid. Moreover, this parameter must be dependent on the

materials properties and doping. The decrease of free charge carriers concen-

tration through stoichiometry improvement induced by annealing or doping, can

considerably increase this threshold value as observed by Xu et al. (1991).

Besides, the surface reactivity of particles is known to rapidly increase with

the increase of the surface-to-bulk ratio because the strong curvature of the par-

ticle surface generates a larger density of defects, which are the most reactive

surface sites. This high reactivity has largely been taken advantage in cataly-

sis, where ultrafine particles have been used for decades. Panchapakesan et al.

(2001) and Baraton & Merhari (2001) found that when properly processed during

the fabrication of chemical semiconductors sensors, these nanoparticles are suffi-

ciently reactive to make the use of catalytic additives redundant and to decrease

the working temperature of the sensors without any loss of sensitivity.

However, the decrease in grain size cannot be unlimited. At some critical

dimension, the number of free electrons in the grain could become zero even

39


at Vs = 0. This leads to grain resistance that would not be dependant on the

changes in the surrounding atmosphere. For charge carrier concentration in metal

oxides of 10 − 21cm−3, the critical crystallite size is 1 nm. Korotcenkov et al.

(2005) suggested that the use of finely dispersed small crystallites can also have a

deleterious effect on temporal stability of the sensor. Rao et al. (2002) have shown

that an excessive decrease of grain size leads to a loss of structural stability, and

as a consequence, to change both surface and catalytic properties of the material.

Shek et al. (1999) found that SnO2 with grain size of 1-4 nm, grain growth

process begins at temperatures ∼ 200 − 400oC. In contrast, SnO2 crystallites

with average sizes ranging from 1.7− 4.0µm were stable up to 1050oC.

According to Thompson (2000), Mazzone (2002) and Sun (2007), there are

two main reasons for grain growth during thermal treatments. This instability

may arise from a known defect in the bulk state, such as faulty stoichiometry, or

due to the finite size of the grains. A quantum mechanical study of the stability

of SnO2 nanocrystalline grains has shown that the increase of both the grain size

in the range 0.3-4.0 nm, and the oxygen content in SnO2 increased the stability of

SnO2 grains. Well known fact is that the size has a strong influence on the melting

temperature, Goldstein et al. (1992), Christenson (2001) and Zhang et al. (2000)

have confirmed that the decrease of the grain size is accompanied by lowering of

the melting temperature of the semiconductor nanocrystals as well.

The principle of surface and interface energy minimization explains only the

appearance of a driving force for the recrystallization of polycrystalline films

during the annealing. The principle cannot account for the threshold nature of

the grain size changes observed by Korotcenkov et al. (2005), i.e. the presence

of a threshold temperature (Tst) below which the crystallites with the fixed size

remain stable, and the absence of t1/2 type grain size dependencies on time during

thermal annealing.

It thus becomes clear that the presence of a finely dispersed fraction with

a grain size smaller than 2-5 nm will lead to some structural instability of the

metal oxide matrix at moderate operating temperatures (T < 600oC). This is

true even for films with average grain sizes greater than 100 nm. Therefore, future

design methods of nano-scale devices, which assure grain size stabilization during

long term operation at high temperature, will gain priority over the design of

methods producing nano-scaled materials with minimal grain size. Synthesis of

40


metal oxide powder and deposition of thin films with a small dispersion of grain

size is an effective method for improvement of temporal stability of solid-state

gas sensors. The grain sizes in thin films during the annealing process changes

considerably less than in thick films. Therefore, unless there is a dire need to

improve detection limits, it is not advisable to design sensors with excessively

reduced grain sizes. This problem is utterly serious for operation in ambience of

reducing gases.

1.3.5 Role of grain networks, porosity and the area of

inter-grain contacts

From an analysis of numerous parameters which can affect chemo-resistance sen-

sors, in order to achieve maximum gas sensitivity it is necessary either to increase

the role of surface conductivity or to increase the contribution of inter-crystalline

barriers by decreasing the contact area or the width of the neck as discussed in

section 1.3.4.

Hyodo et al. (2003), Park & Mackenzie (1996), Lee & Kang (2005), Basu et al.

(2001), Ahmad et al. (2003), Jin et al. (2005) and Tesfamichael et al. (2007) have

confirmed that the simplest method to achieve the maximum sensitivity is to in-

crease the films porosity. In porous material the volume of the layer is accessible

to the gases and therefore the active surface is much higher than the geomet-

ric area and response is higher as discussed by Barsan et al. (1999). A greater

porosity results in a smaller number of contacts with the necks that are not being

overlapped under interaction with surrounding gases. Sberveglieri et al. (1991)

and Sberveglieri (1992) synthesized SnO2 films by RGTO technique and results

suggested that the films had high sensitivity, in spite of the high level of agglom-

eration. Further it was observed that films with minimal contact area between

agglomerates exhibited maximum sensitivity. Increased porosity also decreased

the probability of forming so called capsulated zones in the volume of the gas

sensing layer. Capsulated zones are isolated from contact with atmosphere, and

therefore their resistance does not depend on the surrounding gas as suggested

by McAleer et al. (1987).

Specific surface area parameter is of great importance in the gas sensing phe-

nomenon, as it is the combination of parameters such as porosity and grain size.

41


From the data of specific surface areas the average grain size may be calculated

using the following equation:

dSA =6

ρA(1.53)

where dSA is the average grain size of spherical particles, A is the surface area

of the powder, and ρ is the theoretical density of SnO2. Moreover, in many cases

the specific surface area shows better correlation with gas response than does

crystallite size. Yamazoe (1991), Ahmad et al. (2003), Rumyantseva et al. (2005)

and Li et al. (1999) have proved that materials with high specific surface area

can be used to develop high sensitivity gas sensors.

Specific surface area data can be estimated by using classical multipoint

Brunauer, Emmett and Teller (BET) adsorption techniques and grain size can be

calculated by using X-ray diffraction (XRD) data. The difference between grain

sizes determined by those methods may be used for porosity characterization as

suggested by Rumyantseva et al. (2005).

Thus as the porosity and active surface area of the gas-sensing material in-

creases, the sensor response increases as well. Matko et al. (2002) concluded

that the porosity and specific surface area are the basic factors that influence

the solid/gas interactions and ultimately the material performance as a gas sen-

sor. Park & Mackenzie (1996) and Jin et al. (1998) have confirmed that gas

sensor with higher porosity have faster response. Park & Mackenzie (1996) have

also established the relation between film porosity and refractive index. If the

porosity is higher, the refractive index is smaller. Pores in a metal oxide can be

considered as a component with refractive index equal to 1.0. Therefore, films

with smaller refractive index along with invariability of the main parameters must

have a higher degree of porosity and better gas permeability.

de Souza Brito et al. (1995) reported interesting results for SnO2 films synthe-

sized using sol-gel method they published that pore size increased during sintering

within the temperature range from 200 to 700oC due to increase in grain growth.

Moreover it was established that in the low temperature regime (Ts < 400oC) the

average micropore diameter increased slightly. In the high temperature region

(Ts > 400oC) pore diameter and crystallite size increased significantly and as a

consequence surface area reduced drastically.

Shimizu et al. (1998) have also proposed that the control of porosity can

42


provide a mean to influence selectivity. They proposed that the limited diffusion

of O2 into the interior region of a thick film should lead to a lowered oxygen

partial pressure inside the gas sensing matrix. This then leads to decreased

coverage of chemisorbed oxygen on the metal oxide surface. This should result

in an increased relative sensitivity to H2. Porosity control becomes one of the

methods of influence on gas sensor selectivity to H2 and gases where H2 is one of

the products of dissociation. Gas molecules with larger diameters than H2 and

O2 would not be able to penetrate into the interior region of a metal oxide film.

Therefore, the gas sensors would not be sensitive to these gases. Hyodo et al.

(2002) also confirmed the similar effect. However this method can be used only

with thick and ceramic gas sensors, where the thickness of the gas-sensing matrix

exceeds 10−100µm. Further the decrease of pore size would often be accompanied

by an increase in response and recovery times, i.e. by a degradation of important

sensor performance parameters. Sensors, having minimal active surface area had

maximum recovery and response times when the crystallite size in all sensors was

the same.

1.3.6 Effect of Agglomeration

Agglomeration and aggregation of matter are ubiquitous phenomena expected

to occur everywhere in nature. Barsan et al. (1999) and Korotcenkov et al.

(2005) have found that most of the times metal oxide such as SnO2 films are

agglomerated. Role of agglomeration in gas-sensing effects has not been studied

in detail yet. Problem of agglomeration occurs not only in thin film but also in

ceramic and thick film sensors. Chabanis et al. (2003) established that in many

cases synthesized powder agglomerates into big particles. They found that the

agglomerates formed during ceramic synthesis are more porous and have larger

size than those formed in thin film technology.

Gadomski et al. (2005) suggested that agglomerates formed in ceramics and

thin film metal oxides correspond to two basically expected types of emerging

aggregations. In ceramic materials there is diffusion-controlled aggregation, for

which the attachment of particles and clusters occurs instantaneously at first

contact. In the case of thin films we have so-called chemical-reaction controlled

aggregation. For the latter case, the first touch sticking principle does not hold,

43


and the microstructural evolution has to follow certain attachment-detachment

sequences of events. Anderson & Lekkerkerker (2002) suggested that the incor-

poration takes place in the form of a particle or some cluster of particles with

different types of microstructures. Chemical-reaction controlled aggregations are

compact compared to their diffusive counterparts, which look more irregular viz.

fractal, meaning more branched.

1.4 Formulation of the current problem

From the literature review we found that small volume of work is available in the

subject of Dependance of sensing response over the particle size of nanocrystalline

tin oxide. The available literature in this context helped a lot to give a final

shape to the present problem and its plausible solution. In order to find out

the correlation between the particle size and sensing response we followed the

methodology enlisted below:

1. The designing and instrumentation of sensor testing set up is utterly im-

portant and it has to be done even before starting the experiments with the

material synthesis methods. In 2nd Chapter the designing and construction

of sensor testing unit is explained. This unit includes sensor testing cham-

ber, sensor heater, sensor holder and data acquisition system. All these

components of the unit have been developed indigenously in the lab before

starting the material synthesis process.

2. From the literature we found that the advent of nanocrystalline materials

has given an impetus to the sensor research technology. So, in 3rd chap-

ter an attempt has been made to synthesize SnO2 powder with nanosized

particles. After going through many books and research papers we found

that by using Bottom-up approach; stable, homogeneous and less defec-

tive nanostructures can be obtained. A technique known as liquid phase

growth has been adopted to synthesize the desired SnO2 nanostructures.

As the theme of the thesis is to establish a correlation between particle size

and sensing response, so it is mandatory to develop the tin oxide parti-

cles with different dimensions. In order to develop the particles of different

sizes we have to manoeuvre the synthesis parameters. In this chapter we

44

1.4 Formulation of the current problem

also developed the nanostructured SnO2 by altering the precipitate separa-

tion technique.Although this variation didn’t effect the particle size but it

affected the sensor response.

3. Bottom-up approach is mainly driven by reduction of Gibbs free energy, to

produce nanomaterials in a state closer to a thermodynamic equilibrium.

In 4th Chapter we have followed this dictum. While synthesizing nanomate-

rials by liquid phase growth we have used heat treatment at different stages

of the process. First of all variation in the reaction temperature was done

and then sintering temperature was also changed to find out how crucial is

the thermal treatment in synthesis process. The material characterization

results clearly indicated that all the synthesized powders have different par-

ticle sizes. The sensors fabricated indicated their response to be dependent

on the particle size.

4. In the previous chapter we developed a sensor with small particle size and

high sensing response. To find out the vulnerability of this sensor in harsh

environments, they were subjected to different fluences of O7+ ions. Un-

doubtedly the irradiation effected the material a great deal although it

didn’t effect particle size a lot but it showed its influence on the sensing

response. The response increased with the fluence rates.

5. In 6th chapter an attempt to synthesize a new selective material has been

made. The co-precipitation of zinc-tin was done in the liquid phase to syn-

thesize the composite oxide. Then this composite oxide was annealed at

different temperatures. Interestingly, we observed that annealing temper-

ature had detrimental effect on the phases in the powder. In turn these

phases effected the sensing response

These problems have been dealt with in this thesis and resolved upto certain

extent. All these issues are elaborately discussed in the following chapters.

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Chapter 1 Introduction and Literature...

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