Synthesis and Characterization of Citrate and Polymer

Stabilized Lanthanide Trifluoride Nanoparticles

by

Rohan Alvares

A thesis submitted in conformity with the requirements for the degree of Master of Science

Graduate Department of Chemistry

University of Toronto

© Copyright by Rohan Alvares 2009

ii

Synthesis and Characterization of Citrate and Polymer

Stabilized Lanthanide Trifluoride Nanoparticles

Rohan Alvares

Master of Science

Department of Chemistry University of Toronto

2009

Abstract

Citrate-coated gadolinium trifluoride (Cit-GdF3) and poly(acrylic acid)-coated

nanoparticles (PAA-GdF3 NPs) were synthesized, the former reproduced from literature

(though using more refined conditions), the latter through a new, two-step, ligand

exchange method. Diamagnetic nanoparticle analogs (Cit-YF3 NPs) were prepared to

investigate citrate interactions with the nanoparticle surface using NMR. Citrate was

found to bind in numerous conformations, with a total of between 29 – 46 % bound at 0

ºC. Exchange studies revealed short residence lifetimes of one and twelve seconds

respectively for bound and free forms of citrate (0 ºC), perhaps explaining the colloidal

instability of these nanoparticles. PAA-GdF3 NPs were synthesized by first producing

their Cit-GdF3 counterparts, and then exchanging citrate for PAA. The impetus behind

this latter synthesis was the relative enhancement in stability and relaxivity attainable by

these nanoparticles. The displacement of citrate by PAA was verified using diffusion

NMR studies.

iii

Acknowledgements

My sincere thanks goes to Prof. P. M. Macdonald, Prof. R. S. Prosser, Dr. Ferenc

Evanics and Dr. Ronald Soong for teaching me the basics of operating the NMR

spectrometers and helping execute experiments. I would further like to thank Prof.

Macdonald and Prof. Prosser for providing me with sound guidance, giving me free reign

(within limits) to design my own experiments and exercise thought, and also for shaping

a wayward undergraduate into a more complete, thorough, organized and sceptical

scientist.

Although the nanoparticle ligand exchange protocol was developed independently,

with guidance from Professors Macdonald and Prosser, equal credit goes to Evelyn

Cheung for working seamlessly and in conjunction with me in developing it. She has

since undertaken the development of much needed size and dispersity improvements to

the syntheses while I focussed exclusively on finishing the NMR based characterization

of the citrate-coated lanthanide trifluoride nanoparticles.

I would also like to thank my colleagues in the Prosser and Macdonald labs for

their company and for helping to create a safe and organized, yet enjoyable working

environment. Among my colleagues, I would once again like to single out Evelyn

Cheung who soldiering with me through highs and lows in the lab, and with whom I

shared some insightful and stimulating discussions about the nanoparticle project,

princesses and life in general. Lastly, and most importantly, I would like to extend my

sincere gratitude to my family for their unwavering support and understanding.

iv

Table of Contents Abstract................................................................................................................................ii Acknowledgements............................................................................................................iii Table of Contents...............................................................................................................iv List of Figures.....................................................................................................................vi List of Tables.....................................................................................................................vii Abbreviations...................................................................................................................viii 1 Introduction................................................................................................................. 1

1.1 Nanoparticle size - Tumour angiogenesis and clearance pathways.................... 2 1.2 Nanoparticle size – Blood-brain barrier considerations ..................................... 4 1.3 Relaxivity............................................................................................................ 6 1.4 MRI contrast agents............................................................................................ 9

1.4.1 Iron Oxide Nanoparticles............................................................................ 9 1.4.2 Gadolinium chelates (Gd-chelates)........................................................... 10 1.4.3 Albumin-(gadolinium-DTPA) complexes and MS-325 (Gadofosveset).. 10 1.4.4 Viral MRI contrast agents......................................................................... 12 1.4.5 Dextran-(Gd-DTPA)................................................................................. 12 1.4.6 Liposomes................................................................................................. 13 1.4.7 Dendrimers ............................................................................................... 14 1.4.8 Metallofullerenes ...................................................................................... 14 1.4.9 Zeolites ..................................................................................................... 15 1.4.10 Gadolinium-based Nanoparticles.............................................................. 16

1.5 Water Soluble Lanthanide Nanoparticle Synthesis .......................................... 17 1.5.1 Coprecipitation ......................................................................................... 17 1.5.2 Polyol-Mediated Nanoparticle Synthesis ................................................. 18 1.5.3 Microemulsion methods ........................................................................... 19 1.5.4 Hydrothermal synthesis ............................................................................ 21

1.6 Thermodynamics of nanoparticle formation .................................................... 21 1.6.1 Thermodynamics ...................................................................................... 21 1.6.2 La Mer model of nucleation and growth .................................................. 23

1.7 NMR experiments............................................................................................. 24 1.7.1 Pulsed Field Gradient Stimulated Echo (PFGSTE) experiment............... 25 1.7.2 Correlation Spectroscopy (COSY) ........................................................... 27 1.7.3 Exchange Spectroscopy (EXSY).............................................................. 29 1.7.4 Selective Inversion Recovery (SIR) ......................................................... 30

2 Experimental Section................................................................................................ 31 2.1 Materials ........................................................................................................... 31 2.2 Methods ............................................................................................................ 31

2.2.1 Citrate coated lanthanide nanoparticle synthesis...................................... 31 2.2.2 PAA coated lanthanide nanoparticle synthesis......................................... 32 2.2.3 Electron Microscopy................................................................................. 32 2.2.4 Nanoparticle Size Analysis....................................................................... 32 2.2.5 Zeta potential measurements .................................................................... 32 2.2.6 NMR ......................................................................................................... 33

v

3 Results and Discussion ............................................................................................. 35 3.1 Ligand Properties – citrate and poly(acrylic acid) (PAA) ................................ 35 3.2 Nanoparticle synthesis procedures ................................................................... 37

3.2.1 Citrate-coated lanthanide trifluoride nanoparticle synthesis .................... 37 3.2.2 Poly(acrylic acid)-coated lanthanide trifluoride nanoparticle synthesis... 37

3.3 Synthesis Verification and Characterization of Size Polydispersity ................ 38 3.4 PAA25-LnF3 NP - Direct Synthesis Images ...................................................... 40 3.5 Control of Nanoparticles Size and Dispersity .................................................. 43

3.5.1 Rate of Lanthanide Addition .................................................................... 43 3.5.2 Reduction in Total Lanthanide Feedstock ................................................ 46 3.5.3 Lump-sum lanthanide stock additions ...................................................... 47

3.6 NMR Studies .................................................................................................... 49 3.6.1 One-Dimensional Proton (1D 1H) Spectra of Citrate and Cit-YF3 Nanoparticles ............................................................................................................ 50 3.6.2 Correlation Spectroscopy (COSY) ........................................................... 57 3.6.3 Exchange and Population Calculations - Diffusion Studies ..................... 62 3.6.4 Exchange spectroscopy (EXSY)............................................................... 66 3.6.5 Selective Inversion Recovery ................................................................... 68 3.6.6 Verification of PAA ligand exchange....................................................... 72

4 Conclusion ................................................................................................................ 75 5 Supporting Data ........................................................................................................ 77

5.1 Supporting Data (for Figure 3.11) .................................................................... 77 5.2 Supporting Data (for Figure 3.12) .................................................................... 78

6 References................................................................................................................. 79

vi

List of Figures Figure 1.1 Size dependence of contrast agent diffusion in blood vessels.........................3 Figure 1.2 Tissue permeability size dependence...............................................................4 Figure 1.3 Pathways across the blood brain barrier...........................................................6 Figure 1.4 First and second coordination spheres of a gadolinium complex....................7 Figure 1.5 Various contrast agents – chelate, MS-325, virus, dextran..............................13 Figure 1.6 Various contrast agents – liposome, dendrimer, fullerene, zeolite..................16 Figure 1.7 Nanoparticle synthesis - reverse micelle.........................................................20 Figure 1.8 Thermodynamics of nanoparticle formation – critical size..............................23 Figure 1.9 La Mer diagram – colloidal nucleation and growth.........................................24 Figure 1.10 Pulsed field gradient stimulated echo (PFGSTE) pulse sequence................25 Figure 1.11 Multiple quantum suppression – CHIRP based z-filter.................................26 Figure 1.12 Correlation spectroscopy (COSY) NMR pulse sequence..............................27 Figure 1.13 COSY spectrum..............................................................................................28 Figure 1.14 Exchange spectroscopy (EXSY) NMR pulse sequence.................................29 Figure 1.15 Selective inversion recovery (SIR) pulse sequence.......................................30 Figure 3.1 Citric acid and poly(acrylic acid) structures....................................................36 Figure 3.2 PAA-GdF3 nanoparticle STEM images and size dispersions..........................41 Figure 3.3 90/10 Gd/EuF3 energy dispersive X-ray linescan............................................42 Figure 3.4 STEM images – variation in lanthanide rate of addition.................................45 Figure 3.5 STEM images – reduction in total lanthanide feedstock..................................47 Figure 3.6 STEM images – lump-sum addition of lanthanide feedstock..........................48 Figure 3.7 STEM images – Cit-YF3 and PAA-YF3 nanoparticles....................................50 Figure 3.8 NMR spectra – one dimensional proton results...............................................52 Figure 3.9 NMR spectra – one dimensional proton temperature variation.......................56 Figure 3.10 Citrate carboxylate coordination modes.........................................................57 Figure 3.11 NMR spectra – Cit-YF3 nanoparticle COSY spectrum..................................59 Figure 3.12 NMR spectra – 1:1, Y:citrate COSY control spectrum..................................60 Figure 3.13 NMR spectra – 1:10, Y:citrate COSY control spectrum................................61 Figure 3.14 NMR spectra – Diffusion Peak Set choices...................................................64 Figure 3.15 NMR spectra – PFGSTE diffusion decay of Cit-YF3 nanoparticles..............64 Figure 3.16 Logarithmic plot of PFGSTE citrate peak decays..........................................65 Figure 3.17 Example of a simulated diffusion decay........................................................65 Figure 3.18 NMR spectra – EXSY spectra........................................................................67 Figure 3.19 NMR spectra – SIR experiment.....................................................................71 Figure 3.20 CIFIT fits of SIR results.................................................................................71 Figure 3.21 NMR spectra - PFGSTE diffusion decay of PAA-YF3 nanoparticles...........73 Figure 3.22 Logarithmic plot of PFGSTE PAA peak decays............................................73

vii

List of Tables Table 3.1 Nanoparticle size distribution............................................................................44 Table 3.2 Comparison of citrate NMR parameters............................................................54 Table 3.3 Comparison of citrate binding states.................................................................62 Table 3.4 PAA-YF3 diffusion summary............................................................................74 Table SD1 Chemical shift assignments for Cit-YF3 nanoparticles...................................77 Table SD2 Chemical shift assignments for 1:1, Y:citrate control.....................................78

viii

Abbreviations Roman letters

1D1H one dimensional proton (spectrum) AB strong coupling AX weak coupling BBB blood-brain barrier BBTB blood-brain tumour barrier BCNU 1,3-bis(2-chloroethyl)-1-nitrosourea or carmustine CA contrast agent CCMV cowpea chlorotic mottle virus CT computed X-ray tomography Cit-GdF3 NPs citrate-coated gadolinium trifluoride nanoparticle Cit-LnF3 NPs citrate-coated lanthanide trifluoride nanoparticles COSY correlation spectroscopy Dbound diffusion coefficient of the bound state Dfree diffusion coefficient of the free state Dobs observed diffusion coefficient DTPA diethylene triamine pentaacetic acid Eo standard reduction potential EDX energy dispersive X-ray EXSY exchange spectroscopy g amplitude of the gradient in the PFGSTE experiment g(n) free energy of the aggregate gB free energy of a bulk molecule gS free energy of an interfacial molecule G2 dendrimer generation, where the number be 2 or larger Gd-DPTA gadolinium-diethylene triamine pentaacetic acid chelate (Magnequist®) HD hydrodynamic diameter i a number (1 or 2) referring to either the spin lattice or spin-spin relaxation mechanisms k Boltzmann’s constant kCM exchange constants when going from State C to State M kDM exchange constants when going from State D to State M kMC exchange constants when going from State M to State C kMD exchange constants when going from State M to State D LMCA low molecular weight contrast agent MMCA macromolecular contrast agent MPION micrometer-sized paramagnetic iron oxide nanoparticle MRI magnetic resonance imaging MS-325 a small molecule with an albumin binding diphenylcyclohexyl lipophilic group MW molecular weight n number of precursors nB number of bulk molecules nS number of interfacial molecules NMR nuclear magnetic resonance NOESY nuclear Overhauser effect spectroscopy

ix

NP nanoparticle NSF nephrogenic systemic fibrosis O/W oil-in-water pbound proportion of bound population pfree proportion of free population PAA poly(acrylic) acid PAA25 poly(acrylic) acid with 25 acrylic acid monomers PAA-GdF3 NPs poly(acrylic acid)-coated gadolinium trifluoride nanoparticles PAA-LnF3 NPs poly(acrylic acid)-coated lanthanide trifluoride nanoparticles PAMAM polyamidoamine PDI polydispersity index PEG poly(ethylene glycol) PET positron emission tomography PFGSTE pulsed field gradient stimulated echo PGSE pulsed gradient spin echo PTFE poly(tetrafluoroethylene) rH distance between the metal center and the coordinated water molecules ri relaxivity R relaxation rate Rc critical radius Ri

IS inner sphere contribution to relaxation Ri

o diamagnetic contribution to relaxation Ri

obs observed relaxation rate Ri

OS outer sphere contribution to relaxation RES reticuloendothelial system S electron spin quantum number SIR selective inversion recovery SPECT single photon emission computed tomography SPION super-paramagnetic iron oxide nanoparticle STE stimulated echo STEM scanning transmission electron microscope/microscopy q number of bound water molecules T temperature T1 spin lattice relaxation time T1M relaxation time of bound water molecules T1e spin-lattice electron spin relaxation time T2 spin-spin relaxation time T2e spin-spin electron spin relaxation time Tie electron spin relaxation time USPION ultra-small super-paramagnetic iron oxide nanoparticle W/O water-in-oil x precursor concentration Y:Cit ratio of yttrium(III) ions to citrate

x

Greek Letters

∆ mixing time in the PFGSTE experiment ∆t time dependent zero field splitting γ of γI proton magnetogyric ratio / also surface tension δ duration of gradient in the PFGSTE experiment µ chemical potential of a precursor µ

B chemical potential of a bulk precursor µB Bohr magneton τi correlation time τ1 spin-lattice correlation time / or delay in the PFGSTE experiment τ2 spin-spin correlation time / or delay in the PFGSTE experiment τM residence lifetime of bound water molecules or exchange correlation time τR rotational correlation time τV electron relaxation correlation time ωI proton Larmor frequency ωS electron Larmor frequency

1

1 Introduction

A host of new techniques have surfaced in the field of biomedical imaging over

the last century. Examples of some real-time, non-invasive methods include computed

X-ray tomography (CT), magnetic resonance imaging (MRI), optical imaging, positron

emission tomography (PET), single photon emission computed tomography (SPECT) and

ultrasound.1 PET and SPECT are often metabolic in nature. MRI and CT are often more

anatomical with mm or worse resolution. Of these imaging platforms, MRI is one of the

most important tools for biomedical research and diagnostic clinical medicine.2 It is the

technique of choice for imaging of the brain and central nervous system, detecting

tumours and for assessing cardiac function. Often, however, contrast agents may be

needed to enhance local signal of target tissue or improve resolution. Currently (2006),

approximately 35 % of MRI procedures utilize such agents.3

MRI contrast agents can assist in the early detection of cancer. Super-

paramagnetic iron oxide nanoparticles (SPIONs) have been used to detect lesions as

small as 2 – 3 mm in liver tissue,4 while ultra-small super-paramagnetic iron oxide

nanoparticles (USPIONs) have detected lymph node metatheses in the 5 – 10 mm range.5

However, these negative contrast agents suffer from drawbacks (discussed later) and

construction of novel gadolinium based nanoparticles could alleviate these issues.

Herein, the synthesis and verification of poly(acrylic acid)-coated, gadolinium

trifluoride (PAA-GdF3) nanoparticles is reported, along with the physical characterization

of their citrate-coated GdF3 nanoparticle precursors. Background information on the

targeted tissue (malignant tumours), clearance pathways, physical barriers and relaxivity

mechanisms is presented to provide structural considerations that influence in vivo

nanoparticle function. Examples of different MRI contrast agents are reviewed, followed

by discussion of different nanoparticle synthesis procedures and the thermodynamics of

nanoparticle formation. Finally, NMR techniques, employed in the physical

characterization of the nanoparticles, are described.

2

1.1 Nanoparticle size - Tumour angiogenesis and clearance pathways

Size is an important parameter in the design of MRI contrast agents for cancer

imaging. Ideal size, for enhanced blood retention time and nanoparticle to tumour

localization, is dependent upon differences between normal and tumour vasculature

permeability, as well as constraints imposed by the reticuloendothelial (RES) and

excretory systems.6

Tumour vascular permeability is induced by angiogenesis, the process through

which new blood vessels are formed. When tumours exceed 1-2 mm in diameter,

nutrient diffusion is no longer sufficient to nourish the outer cells of a tumour.

Consequently, new blood vessel growth is needed to ensure tumour growth.6 There is a

fundamental structural difference between these and normal vessels. The latter are

comprised of three layers that create a water-tight seal useful for carrying nutrients.

Surrounding the inner endothelial layer, whose cells are held together by tight junctions,

is a tightly adherent baseline membrane. This in turn, is surrounded by pericytes and

smooth muscle cells. In contrast, vessel walls in tumours are incompletely formed and

fragile. Large gaps exist between the endothelial cells and the baseline membrane, and

the pericytes and smooth muscles are only loosely adherent. Consequently, tumour

vessels are hyperpermeable.7

Size plays a key role in the localization of contrast agents to tumour regions.

Consequently, contrast agents are divided into two categories based on size: low

molecular weight contrast agents (LMCAs) have molecular weights less than 1 kDa,

whereas macromolecular contrast agents (MMCAs) have molecular weights typically

greater than 30 kDa (size comparison: 20 kDa ~ 10 nm).6 LMCAs, such as Gd-DTPA,

can localize in tumour cells due to their ability to diffuse faster through hyperpermeable

tumour vessels than normal vessels. However, their non-selective permeable nature (i.e.

larger first pass fractions in normal vasculature) results in a reduced blood circulation

time. Although MMCAs diffuse more slowly through hyperpermeable vessels, they have

longer intravascular retention in normal tissue that results in a greater localized

concentration at tumour sites over time. The differing size-dependent ability of contrast

agents to diffuse though vasculature is shown in Figure 1.1. In general, normal

microvessels are less permeable to molecules whose diameters are greater than 5-10 nm 8

3

because the average effective pore size in normal intact endothelium is ~ 5 nm.9 On the

other hand, tumour vessels have been known to let through molecules as large as 400-600

nm.10 On a cautionary note, hyperpermeability is not restricted to tumour vessels but is

also observed in severe inflammatory regions and where reparative tissues are present.6

Figure 1.1 A representation of the differing ability of (A) small and (B) large macromolecular contrast agents to diffuse through blood vessels (Reprinted from Eur. J.

Radiol. 2006, 60, 353-366. Copyright (2006), with permission from Elsevier).

The renal and hepatic systems impose further limits on size. Particles with a

hydrodynamic diameter (HD) less than 6 nm are removed through glomerular filtration in

the kidneys and undergo renal clearance. As such, they have a short blood circulation

time. However, particles with an HD greater than 8 nm are not readily excreted in this

fashion. Thus, approximately doubling particle size (from ~ 5 to ~ 11 nm) results in a

thirty fold increase in blood-retention time.11, 12 Nanoparticles greater than 100 nm in

size are easily removed by the reticuloendothelial system (RES; liver, spleen and bone

marrow).1 Particles in the size range between these two extremes are not efficiently

cleared by either mechanism and hence exhibit longer circulation times.9, 11

In conclusion, numerous factors need to be taken into consideration when choosing

the optimal size of a nanoparticle or MMCA. The hyperpermeability of tumour

vasculature, size dependent permeability of normal vasculature, and size selective

filtering by the renal and hepatic systems all play a role in determining nanoparticle size.

The general consensus seems to be that particles between 10-100 nm are good for tumour

imaging.1, 9

4

1.2 Nanoparticle size – Blood-brain barrier considerations

The blood-brain barrier (BBB) is noted for its ability to act as a natural,

physiological wall that separates the central nervous system structures (brain and spinal

cord) from direct contact with circulating blood. It is characterized by tight

interendothelial junctions, few pinocytotic vesicles and no fenestrations.13 Drugs

typically cross the BBB through diffusion. Such a process results in an 8-log difference

in permeability, relative to the liver, for an immunoglobulin (size ~ 57 Å in radius, Figure

1.2).13 However, the ability of the blood-brain tumour barrier (BBTB) to act as a

physiological wall can be partly compromised due to the type of vasculature present.

Three kinds may exist: non-fenestrated capillaries (i.e. like normal brain), fenestrated

capillaries (permeable to small but not large molecules, maximum channel width of 5.5

nm14) and capillaries with interendothelial gaps as large as 1 µm.13

Figure 1.2 Permeability of various tissues by molecules of different sizes. The data points at a molecular radius of ~ 57 Å correspond to an immunoglobulin (Reprinted by permission of Duke University Press: Neuro-oncology 2000, 2, 45-59, copyright (2000)).

Size and blood retention time are critical parameters when assessing whether a

particle can localize to brain tumours. Small molecules have proved to have reduced

ability to accumulate in brain tumours due to low blood retention times. The behaviour

5

of 1,3-bis (2-chloroethyl)-1-nitrosourea (BCNU, carmustine) is a case in point. Despite

its small size (<1 nm) and known ability to penetrate the BBTB, BCNU cannot

accumulate in therapeutic quantities in malignant gliomas.15 This is because, due to renal

filtering, it has a short blood half-time. Consequently, it was theorized that particles with

sizes large enough to evade renal filters ( > 5-6 nm), yet small enough to bypass hepatic

filters ( < 10-20 nm) might have better success due to longer blood-circulation times.

Another particle size constraint is imposed by the pore size in malignant gliomas. In

2008, Sarin et al., using Gd-DTPA functionalized dendrimers, estimated the maximum

contrast agent size that can penetrate fenestrations and gaps in the BBTB of RG-2

malignant gliomas to be 11.7 – 11.9 nm in diameter. They concluded that nanoparticles

between ~ 6 – 11.7 nm may be able to be delivered across a minimally comprised

BBTB.16

Two alternative methods to cross the BBB are also available. Disruption of the

tight junctions can allow particles to traverse the BBB. Osmotic opening can be induced

by intracarotid administration of arabinose or mannitol solutions that result in the

interendothelial tight junctions widening by about 40 nm in diameter.17 Disruption can

also be achieved by bradykinin analogues such as RMP-7.18 Alternatively, receptor-

mediated transcytosis can also allow particles to cross the BBB. Nanoparticles coated

with polysorbate 80 adsorb apolipoproteins which can bind to brain capillary endothelial

receptors,19 while those conjugated to transferrin can bind to their corresponding

transferrin brain endothelial receptors,20 both of which allow receptor-mediated

transcytosis to occur.18 Figure 1.3 shows different pathways molecules use to cross the

blood brain barrier, including receptor mediated transcytosis.

6

Figure 1.3 Five different pathways across the blood-brain barrier, (d) depicts receptor mediated transcytosis (Reprinted by permission from Macmillan Publishers Ltd: Nature

Reviews Neuroscience, 2006, 7, 41-53, copyright (2006)).

1.3 Relaxivity

The ability of a MRI contrast agent to induce contrast is captured by a property

called relaxivity (ri). It is defined by the equation,

[ ]CA

Rr i

i = Equation 1.1

where R is the relaxation rate, CA the contrast agent and i an index referring to either the

spin-lattice (a value of 1) or spin-spin (a value of 2) relaxation mechanisms. The

observed relaxation rate, Riobs, is a sum of three contributions: one diamagnetic and two

paramagnetic ones (Equation 1.2). The diamagnetic contribution (Rio) is due to all

parameters that influence relaxation except paramagnetic ones. The first paramagnetic

component, an inner sphere contribution (RiIS), results from coordination and exchange

of water molecules with the metal complex (Figure 1.4). The second, an outer sphere

contribution (RiOS), is due to water molecules influenced by the metal, but not in direct

contact with it (usually nearby diffusing water molecules). Both the paramagnetic

7

contributions are caused by interactions of the electron dipole moment of the metal with

the nuclear dipole moment of the proton (dipolar relaxation mechanism).3

OS

i

IS

i

o

i

obs

i RRRR ++= Equation 1.2

Occasionally in literature a second coordination sphere is mentioned, where relaxation is

caused by water molecules which are usually hydrogen bonded to polar groups on the

complex or nanoparticle.21 Also, one can well imagine a separate contribution due to

exchangeable protons present on the ligand or chelate.

Figure 1.4. A representation of first and second coordination spheres for a gadolinium complex. Additional parameters, such as rH, a, τR and τM are indicated (European

Journal of Inorganic Chemistry, 2000, 399-407 - Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission).

Inner sphere paramagnetic contributions to relaxivity have been described by

Solomon-Bloembergen-Morgan theory. A set of equations for such contributions to spin-

lattice relaxation is presented below. The R1IS is directly proportional to the number of

bound water molecules (q) and the concentration of the paramagnetic contrast agent (CA),

while inversely proportional to the mean residence lifetime of bound water molecules (τm)

and their relaxation time (T1m).3, 21

[ ]( )mm

IS

T

qCAR

τ+=

11 5.55

Equation 1.3

The latter (T1m) is dependent on the spectral density function (Equation 1.4). It is

inversely proportional to the sixth power of the distance between the metal center and the

coordinated water molecules (rH) and dependent upon the correlation time (τi).3, 21

( )( ) ( )

++

+

+=

22

22

21

21

6

222

1 1

7

1

31

1521

τω

τ

τω

τµγ

SIH

BI

m r

SSg

T Equation 1.4

8

Other parameters in the equation include γI, the proton magnetogyric ratio, g, the Lande

factor for the free electron, µB, the Bohr magneton, S, the electron spin quantum number,

ωI and ωS, the respective proton and electron Larmor frequencies and τ1 and τ2 the spin-

lattice and spin-spin correlation times respectively. The last two parameters depend on

three different correlation times (Equation 1.5).3

Rmiei T τττ

1111++= Equation 1.5

Tie is the electron spin relaxation time, τR the rotational correlation time and τm the

exchange correlation time (Figure 1.4).

Proton spin-lattice relaxation is dependent on both T1e and T2e through the

dispersive parts of Equation 1.4. These two parameters are also field-dependent

(Equation 1.6 and 1.7).22

( )[ ]

++

+−+

∆=

−

2222

21

141

4

1314

25Sv

v

Sv

vt

e SSTωτ

τ

ωτ

τ Equation 1.6

( )[ ]

++

++−+

∆=

−

2222

21

241

2

1

53314

50Sv

v

Sv

v

v

t

e SSTωτ

τ

ωτ

ττ Equation 1.7

∆t is the time-dependent zero field splitting and τv is the electron relaxation correlation

time.

The outer sphere relaxation is inversely proportional to the distance of closest

approach, a (Figure 1.4), and the diffusion coefficient, D.

( ) ( )[ ]IS

OSOSJJ

aDCR ωω 37

11 +

= Equation 1.8

COS is a constant. For small Gd-chelates this contribution to relaxivity amounts to forty

percent,21 while ten percent can be attributed for macromolecular contrast agents.23

It has long been recognized that larger contrast agents would have greater

paramagnetic contributions to relaxivity due to slower tumbling times. At field strengths

typically used for MRI (20 – 63 MHz) the rotational correlation time is usually so fast for

small Gd-chelates that it is the dominant contributor to relaxivity. For a small molecule

(600 – 800 Da) a typical value of Tie is approximately 1 ns at 0.5 T (~ 20 MHz), while τR

is about 60 – 80 ps.3 Botta has mentioned studies where slowing down the rotation has

resulted in improvements to relaxivity.21

9

1.4 MRI contrast agents

Certain deficiencies of MRI, e.g. the lack of contrast between tissues and lesions,

have cemented the importance of MRI contrast agents. Current commercial agents

include super and ultra-small paramagnetic iron oxide nanoparticles, used for T2-

weighted imaging, and Gd-chelates, used for T1-weighted imaging. In addition, a variety

of MRI contrast agents have appeared in literature and new ones are still being published.

The need to generate these new contrast agents is driven by the shortcomings of the

current ones. Iron oxide, T2 contrast agents suffer because of their negative contrast

effect and high magnetic susceptibility, causing image aberrations. Disadvantages of the

Gd-chelates include relatively low relaxivities, a lack of targeting specificity and small

size, the latter of which results in non-specific and rapid equilibrium between

intravascular and interstitial compartments.1 In addition, Gd-chelates have been found to

increase the risk of developing nephrogenic systemic fibrosis (NSF), a serious medical

disorder, in patients with acute or chronic severe renal insufficiency and patients with

acute renal insufficiency.24

The dearth of commercially available large positive contrast agents (MW > 30000)

has, in part, led to a focus on the synthesis of large gadolinium based contrast agents.

Designed to carry high gadolinium(III) payloads, dendrimers, micelles and liposomes,

virus vectors, proteins, polysaccharides, polyamino acids, zeolites and metallofullerenes

have all been reported over the last 20 years.6 In addition, several gadolinium containing

nanoparticles have also been synthesized.1 A brief description of these contrast agents,

as well as the commercially available ones, is provided below, with a moderate focus on

their relationship to cancer imaging.

1.4.1 Iron Oxide Nanoparticles

Iron oxide nanoparticles, which dramatically shorten the T2 relaxation time, are

‘negative’ contrast agents. Because of the relationship between size, and biodistribution

and blood half-life, they are divided into three classes according to diameter. These are

micrometer-sized (MPION, a few micrometers), super (SPION, 50-150 nm) and ultra-

small (USPION, <50 nm) super-paramagnetic iron oxide nanoparticles.1, 25 Commercial

iron oxide MRI contrast agents include Feridex, Resovist and Combidex, all of which are

10

either SPIONs or USPIONs. These particles have an iron oxide core (Fe3O4, Fe2O3) with

either a dextran or carboxydextran coating and induce high T2 relaxivities ranging from

65 – 120 mM-1s-1 (1.5 T).1

SPIONs have been used to diagnose liver diseases because they are selectively

phagocytosed by Kupffer cells in the liver, spleen and bone marrow (i.e. RES). As

diseased liver tissue, such as a liver tumour, has a deficiency of Kupffer cells, few

SPIONs are taken up. The localized SPIONs result in a strong contrast between normal

and diseased tissue, enabling detection of the disease.1 In contrast, the smaller USPIONs

have a longer blood circulation time and are used for lymph node imaging.26 Two

disadvantages have been noted for iron oxide nanoparticles. Firstly, contrast is achieved

by inducing a decrease in signal in T2-weighted imaging. Secondly, their high magnetic

susceptibility alters the magnetic field in neighbouring normal tissue which can make

images unclear and reduce the background around lesions. Consequently, most

applications use Gd-chelate contrast agents.1, 25

1.4.2 Gadolinium chelates (Gd-chelates)

As of 2006, gadolinium chelates (Gd-chelates) comprised all of the T1 contrast

agents used for MR imaging. These consisted of four clinically approved agents: the two

anionic contrast agents, Gd(DTPA)2- (Magnevist®, logKGd = 22.5) and Gd(DOTA)-

(Dotarem®, logKGd = 24.7), and the two neutral versions, Gd(DTPA-BPA) (Omniscan®,

logKGd = 16.85) and Gd(HPDO3A) (Prohance®, logKGd = 23.8) (Figure 1.5. A).27 The

relaxivity enhancement induced by these agents is due to inner sphere and outer sphere

contributions (refer to Relaxivity section). The small size of these agents makes them

non-specific and they distribute principally in the intravascular and interstitial space.28

They have been known to collect in the kidneys due to glomerular filtration and are

excreted in this manner.29, 30

1.4.3 Albumin-(gadolinium-DTPA) complexes and MS-325 (Gadofosveset)

Albumin-(gadolinium-DTPA) complexes were originally prepared by Ovid et al.

in the late eighties.31 They typically contained 25-36 covalently attached Gd-DTPA

chelates, giving the macromolecule a molecular weight of roughly 92 kDa (~ 6 nm).

11

Relaxivities of 14.9 mM-1s-1, relative to gadolinium concentration, were reported (0.25T

~ 10 Hz, 37ºC).6, 31

Numerous studies were conducted to demonstrate the MRI capabilities of

albumin-(Gd-DTPA) molecules.6 Among them, enhanced signals were observed in liver,

lung, spleen, kidney and brain tissue due to a T1-effect.32 Furthermore, albumin-(Gd-

DTPA) assisted in characterization of microvessels in breast, sarcoma and prostate

tumors.33-35 However, a few disadvantages led to this contrast agent never reaching

human trials. Prolonged retention of Gd (several weeks) was noted with incomplete and

slow elimination of it. This is due to only 5% of albumin seeping out from the blood

every hour, consequently leading to intravascular retention. Furthermore, it was

observed to accumulate in liver and bone.7, 36 Lastly, albumin is possibly immunogenic,

limiting its ability to act as an in vivo contrast agent.37

An MS-325 albumin conjugate was developed to overcome some of the

limitations associated with the albumin-(Gd-DTPA) macromolecule. MS-325, a small

molecule of molecular weight (MW) 957 Da, consists of a diphenylcyclohexyl lipophilic

group attached to a gadolinium chelate though a phosphodiester linkage (Figure 1.5. B).

The lipophilic group binds strongly to albumin in a reversible, non-covalent manner. The

molecule is injected in its free form and binds to albumin in vivo. Up to 30 MS-325

molecules can attach to a single albumin, leading to the formation of a macromolecule of

about 68 kDa.6

The more rapid clearance time of MS-325, especially compared to albumin-(Gd-

DTPA), led to it becoming the first gadolinium based macromolecular agent to undergo

human trials. The elimination half-lives were observed to be 2-3 hrs in primates and

rabbits, and 25 minutes in rats.38 Subsequent Phase (I, II and III) trials conducted in

humans demonstrated good vasculature and arterial enhancement (obtained through MRI

angiography) and no adverse effects.39-41 Consequently, it was approved for use in the

European Union (EU).30 However, it demonstrated limited potential as a tumour imaging

agent because no signification correlation was detected between it and either

microvascular density (MVD) or tumour grade.42

12

1.4.4 Viral MRI contrast agents

Recently, the cowpea chlorotic mottle virus (CCMV) protein cage (capsid) has

been used as a frame for a viral MMCA (Figure 1.5. C).43 The approximately 180 metal

binding sites on the cage surface, usually for Ca2+, were used to bind Gd3+ ions instead.

The T1 and T2 relaxivities of water protons were 202 and 376 mM-1s-1 respectively (~1.5

T, 62 MHz, 23 ºC), which the authors claim were the highest known at that time (2005).

However, the in vivo stability of the Gd-CCMV interaction needs to be better

characterized and improved as the Gd-CCMV interaction displays a high dissociation

constant compared to chelated gadolinium ions. In addition, high local concentrations of

Ca2+ could potentially compete with the Gd3+ ions. As a comparison, Tb3+ displayed a

100-fold greater affinity for the protein binding sites than Ca2+.44 Also, more data on the

immunogenicity of this contrast agent needs to be collected.6, 43

1.4.5 Dextran-(Gd-DTPA)

Dextran-(Gd-DTPA) is composed of a linear glucose polymer, dextran, to which

is conjugated many Gd-DTPA complexes through hydrolysable bonds.6 Dextrans of

various different sizes can be prepared. One dextran contrast agent contained 15 Gd-

DTPA complexes and possessed a weight of 75 kDa.45 It was found to remain

intravascular for 1 hr, degraded more rapidly than albumin and had a short biological

half-life (43 min). A larger dextran-(Gd-DTPA) contrast agent was produced with

approximately 187 Gd-complexes per dextran, a molecular weight of 165 kDa and a

diameter of 17.6 nm (Figure 1.5. D).46 Its comparatively larger size resulted in it

remaining intravascular for 58 hrs. When injected in rabbits with thigh tumours, it

demonstrated less tumour contrast than a LMCA after 1 hr, more after 24 hrs and was

still visible after 72 hrs. Less contrast was obtained after a short time (1 hr) due to the

greater initial ability of LMCAs to localize at the tumour due to higher vascular

permeability. Of particular note, the distribution and elimination of dextrans is found to

be size and charge dependent.47 Dextran use in contrast agents is advantageous because

of its inexpensive price and recognized safety record (used for 50 years as a synthetic

plasma expander). However, its intrinsic polydispersity makes permeability difficult to

predict6 and an increased incidence of anaphylactic reactions have been reported for

larger dextrans.47

13

Figure 1.5. (A) Structures of (i) Gd(DOTA)-, (ii) Gd(HPDO3A), (iii) Gd(DTPA)2- and (iv) Gd(DTPA-BPA). (B) The chemical structure of MS-325. (C) Reconstruction of the (i) cowpea chlorotic mottle virus (CCMV) protein cage48 and (ii) CCMV Ca2+/Gd3+ binding protein.49 (D) The structure of dextran-(Gd-DTPA), where ND is the number of monomers bound to GD-DTPA. [(A) Chem. Soc. Rev. 2006, 35, 557-571 - Reproduced by permission of The Royal Society of Chemistry. (B) Reprinted from Eur. J. Radiol. 2006, 60, 353-366. Copyright (2006), with permission from Elsevier. (C) Magnetic

Resonance in Medicine 2005, 54, 807-812 - Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission. (D) Reprinted from Acad. Radiol., 2004, 11, 1361-1369, Copyright (2004), with permission from Elsevier.]

1.4.6 Liposomes

Liposomes are spherical vessels that range in size from 20-400 nm in diameter.

They are composed of one of more bilayer phospholipid membranes (lamella) and

contain a hydrophilic interior. If paramagnetic material is inserted into the membrane or

aqueous interior, they can act as MRI contrast agents (Figure 1.6. A).6 Liposomes can

furthermore serve a dual role as drug targeting delivery vehicles.

14

Two liposome-(Gd-DTPA) vesicles, 70 and 400 nm in size, were tested on rats

with hepatic metathesis. The experiments demonstrated significant contrast enhancement

between liver and tumour.50 Further studies, which attached poly(ethylene glycol) (PEG)

to the liposome, found that this molecule reduced uptake by the RES.51 Consequently,

the liposome-PEG vesicles were christened ‘stealth liposomes’.52 More recent work has

focused on liposomes targeted with agents such as antibodies,53 peptides,54 folates,

aptamers and polysaccharides.55 The principal disadvantage of liposomes is their

polydispersity. As this makes the synthesis difficult to reproduce, the future of targeted

liposomes has been stated to be uncertain.6

1.4.7 Dendrimers

Dendrimers comprise a class of repeatedly branched, synthetically produced,

spherical polymers that can be consistently and reproducibly created. Two examples are

polyamidoamine (PAMAM) and diaminobutane core polypropylimine (DAB or PPI), the

former of which is shown in Figure 1.6. (B). Both can be functionalized with a large

number of Gd-chelates giving them potential to act as MRI contrast agents. The size and

molecular weight of dendrimers increase with each subsequent generation, giving them

different pharmacokinetic and pharmocodynamic abilities. Consequently, they can be

used for different imaging applications.6 For example, different generations of PAMAM,

ranging from two to ten (denoted G2 to G10), were postulated to have potential to image

renal, hepatic, tumour and vascular regions. Molecules of sizes 3-6 nm (G2-G4) are

excreted via the kidney and can be used for renal imaging.56 Sizes of 5-8 nm (G4, G5)

can be used for tumour screening, as they can selectively permeate tumour vasculature.6

Finally, sizes of greater than 8 nm (G6 and above) demonstrated good vascular

enhancement,57 while sizes of ~ 15 nm (G10) have potential for hepatic imaging.6

1.4.8 Metallofullerenes

Fullerenes are closed-caged molecules of carbon in the sp2 hybridized state. They

are the third allotrope of carbon, after diamond and graphite.58 Soon after their discovery,

it was found that metals could be trapped in fullerenes giving rise to the then novel

metallofullerene complexes.59 Water soluble, polyhydroxyl, gadolinium endoheral,

metallofullerenes [(Gd@C82(OH)n, Gd-fullerenols] were synthesized and displayed r1 and

15

r2 relaxivities of 81 and 108 mM-1s-1 respectively.60 In vivo studies indicated signal

enhancement in lung, liver, spleen and kidney. Entrapment by the RES, was postulated

to be caused by either particle aggregation or association with plasma components such

as albumin.61, 62 One advantage of the Gd-fullerenols is that the carbon cage protects the

gadolinium ion from leaching into the surrounding area, while another is that it protects

the metal ion from chemical attack.30 However, previous toxicity studies with Ho-

C82(OH)n fullerenes indicated that as much as 10% accumulate in the bone tissue.63 If the

same holds true for the Gd-fullerenols, surface modification will be required to

demonstrate any viability for commercial use. More recently, a gadofulleride, with

carboxylate and hydroxyl functional groups, was prepared and conjugated to antibodies.64

It was found to form aggregates of ~30 nm in diameter, providing insight as to why these

particles localize in the RES.

Aside from the Gd@C82, two other basic gadolinium metallofullerene skeletons

have been produced: Gd@C60 and ScxGd3-x@C80. Usually the latter produces the highest

relaxivities because three gadoliniums are enclosed within its cage. Recently, a surface

modified Gd3N@C80 with hydroxyl and carboxylate functionalities (Figure 1.6. C) was

synthesized that displayed r1 and r2 relaxivities of 207 and 282 mM-1s-1 (2.4T)

respectively, fifty times larger than those of Omniscan and Magnequist.30

1.4.9 Zeolites

Zeolites are aminosilicates that contain a well-defined pore structure and channel

system of molecular length. They are also chemically and thermally resistant. One type

of zeolite, zeolite Y, is composed of eight sodalite cages connected by oxygen bridges

(Figure 1.6. D). At the center is a cavity, called a supercage, that has an internal diameter

of 11.8 Å. The diameter accessible to small molecules is 7.4 Å. Gd(III) ions can be

nestled within the supercage and are held firm by the strong electrostatic interaction

between them and the negative aminosilicate.65 Negligible leaching of gadolinium was

observed from the supercage.66 The size of the zeolite was found to lie between 80-100

nm in diameter. Relaxivities (r1) ranging from 11.4 to 37.7 mM-1s-1 have been reported

(~1.5T, 37ºC).67

16

Figure 1.6. (A) A stealth liposome capable of acting as an MRI contrast agent. (B) A representation of a PAMAM dendrimer, where n is the generation number. Gd-chelates can be attached to the amino ends. (C) An image of a Gd3N@C80 metallofullerene. (D) A zeolite Y structure composed of eight sodalite cages (truncated octahedrons) connected by oxygen bridges. Within the supercage is nestled a Gd(III) atom. Water is shown to permeate through an accessible hole. [(A) and (B) Reprinted from Eur. J. Radiol. 2006, 60, 353-366. Copyright (2006), with permission from Elsevier. (C) Reprinted in part with permission from Bioconjug. Chem. 2009, 20, 1186-1193. Copyright 2009 American Chemical Society. (D) Chemistry-A European Journal 2005, 11, 4799-4807. Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission].

1.4.10 Gadolinium-based Nanoparticles

Some recent effort has been directed towards the synthesis of gadolinium

nanoparticles in hopes of producing better T1 contrast agents. Gadolinium oxide

(Gd2O3),68-70 fluoride (GdF3)

71 and phosphate (GdPO4)72 nanoparticles have all been

synthesized. The Gd2O3 nanoparticles had dextran, PEG and polysiloxane PEG coatings

with sizes ranging from 3 – 26 nm in hydrodynamic diameter (HD). Two types of GdF3

nanoparticles were produced. One was coated with citrate and had an average size of 129

nm, while the other was coated with 2-aminoethyl phosphate (AEP), doped with LaF3

17

(20%), and had an average size of 51 nm in HD. The GdPO4 nanoparticles, synthesized

by a hydrothermal process, were coated with dextran and had an average HD of 23 nm.

Nanoparticle relaxivity has been reported in numerous ways: relaxivity based on the

number of particles, that based on surface area, and that based on the concentration of

whole atoms have all been reported. Using the latter measure, relaxivities comparable to

(GdF3 core, r1 = 3.1 11 −−smM , 14.2 T)71 and a little over three times (GdPO4 core, r1 =

13.9 11 −−smM , 7 T)72 that of gadolinium chelates (Gd-DTPA, r1 = 4.1 11 −−

smM , 7 T)68

have been reported.

Poly(acrylic acid)-coated, gadolinium trifluoride nanoparticles (PAA-GdF3 NPs)

were herein synthesized based on the above GdF3 nanoparticle protocol. Firstly, citrate-

coated, GdF3 nanoparticles were synthesized and then the citrate ligand was exchanged

for a PAA ligand. Citrate-coated yttrium trifluoride nanoparticles were produced in a

similar fashion.

1.5 Water Soluble Lanthanide Nanoparticle Synthesis

Several different nanoparticle syntheses have been reported that have produced

water-soluble lanthanide nanoparticles. These include coprecipitation,71 a polyol

synthesis73 and a microemulsion method.74

1.5.1 Coprecipitation

In coprecipitation syntheses, nucleation, growth, Ostwald ripening and/or

agglomeration can occur simultaneously. Metal coprecipitation has commonly been

achieved through reduction by use of agents such as borohydride and hydrazine (strong

agents). As the free energy change must be negative, precipitation of any metal with a

standard reduction potential (Eº) more positive than -0.481 or -0.23 V (the Eº for

borohydride and hydrazine respectively) should be possible.

Many first-, second- and third- row transition metals, as well as post-transition

and some non-metals are ideal candidates for a reduction synthesis.75 One such example

involves the production of gold nanoparticles. In a well-known synthesis, nanoparticles

were formed from auric acid and sodium citrate, the latter of which served the dual role

of capping and reducing agent.76 However, as the gadolinium (Gd3+) ion in acidic and

18

basic solution has a Eº of -2.28 and -2.82 V respectively, coprecipitation of this

lanthanide through reduction is not possible using these two strong reducing agents.

Furthermore, the reduction of metal ions with a potential more negative than -0.481 V is

either very difficult or not possible due to the instability of the cations in aqueous

media.75

Nanoparticles produced though coprecipitation can also be formed though

insoluble metal precursors, such as metal oxides. During such a synthesis, a capping

ligand is often needed to prevent agglomeration.75 Production of lanthanide

nanoparticles through precipitation of insoluble lanthanide oxides, fluorides and

phosphates are possible. Recently, water soluble, citrate-coated, gadolinium trifluoride

nanoparticles were synthesized. The protocol specified the addition of gadolinium nitrate

(Gd(NO3)3 to a neutralized solution of citrate and sodium fluoride, yielding roughly

spherical, polydisperse nanoparticles with an average size of 129 nm.71 The chief

disadvantage of coprecipitation is that nucleation and growth occur at once, yielding

nanoparticles of different sizes.

1.5.2 Polyol-Mediated Nanoparticle Synthesis

In the polyol method, nanoparticle precursors are heated to a high temperature

(150-300 ºC) in a high boiling point polyol solvent (e.g. glycerol, diethylene glycol or

glycerine).77 The polyol serves the role of both solvent and capping agent.73 This

synthesis has been noted for its versatility and efficiency in the preparation of nanoscale

materials. A host of different metals such as gold, platinum, and copper have yielded

micron to submicron particles.78 In addition, metal oxide,79 phosphate,80 sulphide81 and

halenogenide73 forms have also been produced. Monodisperse and non-agglomerated

nanoscale materials with sizes ranging from the mesoscale (>100 nm) to the nanoscale

(~5 nm) have been achieved.77

Several advantages of the polyol synthesis have been presented. Firstly, the

polyol has sufficient polarity to solubilize inorganic salts. Secondly, more crystalline

nanoparticles can be produced as nucleation and growth of the nanoscale materials can

occur at high temperatures, as the polyols have high boiling points. Next, particle

agglomeration is prevented and growth of the nanomaterial limited because of the

19

relative affinity of the polyol for the metal precursors. Finally, the synthesis is easy and

suitable for production of large quantities of meso/nano-material.77

Recently, lanthanum doped nanoparticles have been produced by polyol-mediated

syntheses. The precursors were lanthanide chlorides and ammonium fluoride, whereas

the reaction was conducted in one of three polyols: glycol, diethylene glycol and glycerol.

Ammonium fluoride was used as the fluoride source as opposed to sodium fluoride

because the latter is not as soluble in the polyol.73 The lanthanide nanoparticles produced

were spherical and had an average size of 5-7 nm regardless of the type of polyol

employed. The nanoparticles exhibited good dispersity in both water and ethanol.73

1.5.3 Microemulsion methods

Micro-emulsions can result when combinations of oil, water, surfactant and

cosurfactant are mixed. In these systems, the solution is optically isotropic however the

molecules are not randomly orientated as they should be in a solution. Instead, two

general types of drops can form. In oil-in-water (O/W) drops, the oil in located within

the boundary of the surfactants coating the drop, while the reverse is true for water-in-oil

(W/O, i.e. reverse micelle) drops. The latter is depicted in Figure 1.7. (A). In an equal

volume by volume mixture of oil and water, O/W drops will form if the surfactant is

more soluble in water that in oil. W/O drops will form if the reverse is true.75

Microemulsions have been commonly used as micro- and nano-reactors in

nanoparticle syntheses.75 Reverse micelles in particular have been used to mix two

components which react to form nanoparticles. Due to their small size, reverse micelles

are subject to Brownian motion. Inevitably, the collisions between two reverse micelles

with different internal compositions (Figure 1.7. (B) i and ii) sometimes lead to the

formation of a short-lived dimer (Figure 1.7. (B) iii) whose lifetime is ~100 ns. The

contents of the two reverse micelles mix before decoalescing.82 Over time this process

results in equilibrium being achieved across the entire mixture.75

Advantages to using microemulsions to synthesize nanoparticles include control

of nanoparticle size (though control of the size of the reverse micelle), and the inherent

ability to produce more monodisperse nanoparticles. Reverse micelle size can be

controlled by varying the amount of water to surfactant ratio.75 However,

microemulsions can also be relatively complex. Reaction rates and equilibria have been

20

found to be different from those in bulk solution and solvated ions can effect phase

equilibria as well as reverse micelle stability, size and shape.75

Figure 1.7. A) Model of a reverse micelle. B) The coalescence (iii) of two drops (i and ii) containing different chemicals, the result of which can produce nanoparticles. [(A) Reprinted by permission from Macmillan Publishers Ltd: Nature, 1943, 152, 102-103, copyright (1943). (B) Reprinted in part with permission from Chem. Rev. 2004, 104, 3893-3946. Copyright 2004 American Chemical Society]

Microemulsions have been used to synthesize nickel nanoparticles through metal

reduction with hydrazine.83 Similarly, core shell metal nanoparticles have been produced

with reactive metal cores (e.g. Fe) being coated with inert metals (e.g. Au, Si).84, 85 Of

greater interest, YF3 nanoparticles with relatively monodisperse sizes have recently been

synthesized. Both amorphous and crystalline nanoparticles were produced. The

diameter of amorphous spheres was found to be controllable between 6 and 50 nm.

Slight variation in reaction conditions led to the production of monocrystalline,

monodisperse, hexagonal and triangular crystals with diameters tuneable between 25 and

350 nm. Greater crystallinity was suggested to be achieved by a slower growth process

caused by direct addition of ammonium hydrogen difluoride (NH4HF2) to YCl3

containing microemulsions in the latter synthesis.74

21

1.5.4 Hydrothermal synthesis

High temperature, high pressure conditions may be achieved by conducting the

reaction in a solvent well above its boiling point in a sealed vessel. Such reaction

conditions are referred to as solvothermal processing, or in the case of water,

hydrothermal processing. If the temperature is increased sufficiently, a supercritical

liquid can be created displaying unique properties such as high viscosity, no surface-

tension, and a high ability to dissolve compounds that ordinarily display low solubilities

at ambient temperature. However, a synthesis need not be performed under supercritical

conditions to be considered solvothermal or hydrothermal.75 Advantages include high

crystallinity of the nanoparticles, and increased solubility and reactivity of the metal salts

and complexes at elevated temperatures.75 Although no known water soluble lanthanide

nanoparticles have been produced via this method, syntheses have been conducted86 with

similar conditions to those that produced water soluble, citrate coated, lanthanide

trifluoride nanaparticles.71 Consequently, particle insolubility in the hydrothermal

synthesis might have been due to their exceedingly large size (on the supra-micrometer

scale86).

1.6 Thermodynamics of nanoparticle formation

A coprecipitation synthesis method is characterized by nucleation, growth,

coarsening and/or agglomeration occurring simultaneously. Often, the incidence or time

lengths of some of these processes are undesirable because they result in unwanted

nanoparticle attributes, such as size polydispersity. Hence, an understanding of

nucleation and growth processes is crucial because it allows one to predict and control

various aspects of the synthesis. Equations describing nanoparticle nucleation from a

thermodynamic perspective are presented herein, along with the La Mer nucleation and

growth model of colloids.87

1.6.1 Thermodynamics

The preparation of hydrosols in solution involves the presence of two phases: the

free precursor and the bulk aggregate. These two phases have three main chemical

potentials associated with them: that of the free precursor, the bulk hydrosol and the

22

surface of the hydrosol. Hydrosol formation will be spontaneous if there is a net

reduction in the chemical potential of the system and vice versa.

A more elaborate description of nucleation can be described with a series of

equations.88 The change in free energy that occurs as a result of aggregation is equal to

the difference between the free energy of the aggregate, g(n), and that of the precursors in

solution, nµ,

µnngG −=∆ )( Equation 1.9

where n is the number of molecules (or precursors) present and µ the chemical potential

of each molecule. As µ depends on precursor concentration, x, and the temperature, T,

Equation 1.9 can be modified to give,

)ln()( xkTnngG +−=∆ Θµ Equation 1.10

where k is Boltzmann’s constant.

The aggregate term, g(n), can be further broken down into bulk and surface

energy terms,

SSBBgngnng +=)( Equation 1.11

where nB and nS are the number of bulk and interfacial molecules respectively, and gB

and gS the free energy associated with each bulk and interfacial molecule respectively.

The bulk free energy term can be related to its chemical potential, µB, through,

BBBngn µ= Equation 1.12

while the surface or interfacial energy term can be given as a function of its surface

tension, γ, and a geometric factor, bgf.

3/2nbgn gf

SS ⋅⋅= γ Equation 1.13

Equation 1.12 and 1.13 can be inserted into the general equation (Equation 1.10) to give,

( ) 3/2ln nbxkTnG gf

B ⋅⋅++−=∆ Θ γµµ Equation 1.14

Since for a saturated concentration, the bulk term equals,

satB xkT ln+= Θµµ Equation 1.15

the free energy (Equation 1.14) can be rearranged to give,

3/2ln nbx

xnkTG gfsat

⋅⋅+

−=∆ γ Equation 1.16

For a spherical particle this equation can be revised to give an alternative form that

includes a radial term, R.

γπ 24ln Rx

xnkTG

sat+

−=∆ Equation 1.17

23

Since the bulk free energy term rises faster with radius than the interfacial free energy

term, there exists a critical radius (Rc) above which nucleation is spontaneous because

∆G decreases (Figure 1.8).

Figure 1.8. A depiction of the spontaneity of nuclei formation. Initially, there is an increase in ∆G when the concentration, x, is below the saturation concentration, xsat. Nucleation is not spontaneous during this period. Eventually a critical concentration is reached, nc (corresponding to Rc), above which stable nuclei are formed. (Polyelectrolytes and Nanoparticles, 2007, pg 48, Chapter 3, Koetz, J., Kosmella, S., Fig. 3.1, reprinted with kind permission of Springer Science+Business Media).

If the formed nanoparticles or colloids are polydisperse in size, an additional

process, called Ostwald ripening, may occur that affects particle size. Since small

particles have a greater concentration of surrounding solute molecules than larger ones,

diffusion of solute molecules from small to larger particles will take place. This results

in large particles growing and small particles shrinking.

1.6.2 La Mer model of nucleation and growth

About sixty years ago, a model was proposed to describe nucleation and growth

process that occur during colloidal formation (Figure 1.9).87 Initially, concentration of

the solute is increased over time until nucleation occurs at the critical limiting

supersaturation. This is followed by a nucleation period during which new nuclei are

formed and growth of existing nuclei take place. The consumption of solute, which is

incorporated into new and growing nuclei, results in a decrease in concentration to a

24

point where no new nuclei are formed but where growth of existing nuclei can still occur.

This concentration is called the nucleation concentration. Finally, growth of existing

nuclei ceases to occur once the concentration of solute drops to its solubility level. A

limitation of the model is that it does not take into account capping agents that are often

used to stabilize and arrest the growth of the nanoparticles.

Figure 1.9. A colloidal nucleation and growth model as proposed by La Mer and Dinegar. (Polyelectrolytes and Nanoparticles, 2007, pg 49, Chapter 3, Koetz, J., Kosmella, S., Fig. 3.2, reprinted with kind permission of Springer Science+Business Media).

1.7 NMR experiments

The ligand serves many purposes in gadolinium trifluoride nanoparticles. It acts

as a capping agent, provides steric and electrostatic stabilization, affects relaxivity

directly though outer sphere and indirectly though inner sphere effects, and can assist in

evasion of the cells filtering systems (e.g. polyethylene glycol, PEG). Hence

determination of the exchange rate and stability constant is important towards

nanoparticle function and toxicity.

A number of different NMR experiments were used to probe the interaction of the

ligands with the YF3 nanoparticles. One dimensional proton (1D1H) and correlation

spectroscopy (COSY) experiments were used to characterize the coupled citrate peaks.

25

Diffusion coefficients, which provided ligand exchange information, were extracted via

pulsed field gradient stimulated echo (PFGSTE) sequences. Finally, the exchange

network was mapped by using exchange spectroscopy (EXSY) and rate constants

determined using selective inversion recovery (SIR) experiments. Sequences and

explanations for PFGSTE, COSY, EXSY and SIR experiments are provided below.

1.7.1 Pulsed Field Gradient Stimulated Echo (PFGSTE) experiment

Diffusion coefficients can be obtained using pulsed gradient spin echo (PGSE) or

pulsed field gradient stimulated echo (PFGSTE) NMR experiments. The latter utilizes

the pulse sequence shown in Figure 1.10, where τ1 and τ2 are delays between the pulses, δ

and g the duration and magnitude of the gradient respectively, and ∆ the diffusion time

(∆ = τ2 + τ1).

Figure 1.10. The pulse sequence of the PGSTE experiment, originally formulated by Tanner et al (Reprinted from Biochimica Et Biophysica Acta-Biomembranes, 2007, 1768, 1805-1814 - Copyright (2007), with permission from Elsevier).

The PFGSTE can be understood by looking at the stimulated echo (STE) and

gradient parts separately. In the STE, the chemical shifts are refocused but not the J-

couplings. The gradients add an important feature to this sequence. The first one causes

a dephasing of the magnetization proportional to the amplitude and duration of the

gradient, the position of the nuclei along the z-axis, and the magnetogyric ratio, γ. In the

absence of diffusion, the second gradient refocuses the magnetization resulting in no

attenuation of signal due to diffusion processes. However, diffusion of molecules causes

incomplete rephasing and consequently loss of signal. This relationship is captured by

the following expression,

26

[ ]( )3/expexp2

exp 222

1

1

2

2 δδγττ

−∆−

−

−= Dg

TTII o Equation 1.18

where I is the observed intensity and Io the initial intensity. The advantage of this

sequence over the spin echo (SE) is that the spins are aligned along the z-axis during

interval τ1 and are consequently dependent on T1 as opposed to T2 relaxation. For

systems where the T1 > T2, this proves beneficial because signal reduction due to

relaxation is reduced leaving more magnetization to be manipulated by diffusion

parameters.89 Of note, up to five echoes can result for a system in thermal equilibrium

which experiences three radiofrequency pulses. The stimulated echo can be selected by

setting the first and third delays between the pulses (of duration τ2) equal to each other.

A sixteen phase pulse sequence has been advanced to eliminate the other unwanted

echoes.90

The presence of zero quantum coherences, which severely distort the citrate

spectrum in a PFGSTE experiment, necessitated the incorporation of a zero quantum

filter between the second and third 90º pulses (Figure 1.11). The filter consisted of a

swept-frequency, adiabatic, 180º CHIRP pulse of duration τf applied at the same time as a

gradient, Gf. The latter dephased the magnetization along the z-axis, resulting in it

experiencing the swept-frequency pulse at different times, with subsequent echo

formation occurring at 2ατf (where α ranged from 0 to 1 along the length of the NMR

tube). Thus, if an echo occurred at one position along the z-axis, the other positions

would have dephased magnetizations. If the range of frequencies caused by the gradient

is wide enough, the unwanted zero quantum magnetization cancels out.91

Figure 1.11. The CHIRP based z-filter. The two black bars represent the second and third 90º pulses in the PFGSTE sequence. GHS, a homospoil gradient, is incorporated along with the zero quantum filter to remove zero and non-zero quantum coherences that lead to antiphase peaks (Angewandte Chemie-International Edition 2003, 42, 3938-3941 - Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission).

27

1.7.2 Correlation Spectroscopy (COSY)

The COSY experiment (Figure 1.12) can be used to elucidate J-coupling

connections. It is a homonuclear, two-dimensional experiment typically used when

observing 1H nuclei.92 Cross peaks in the spectrum indicate coupling, while diagonal

peaks provide no significantly different information than that which can be obtained

through a one-dimensional spectrum.

Figure 1.12. The pulse sequence used in a COSY experiment where both pulses are 90ºx in phase. The white triangle represents the acquisition period.

The essence of this experiment is explained through a product operator approach for a

two spin (I and S) coupled system. For the I spin, only two out of the four signals present

before the acquisition period are observable. These are inphase,

( ) ( ) XIIS IttJ 11 sincos Ωπ Equation 1.19

and antiphase,

( ) ( ) YZIIS SIttJ 2sinsin 11 Ω− π Equation 1.20

components. JIS is the J-coupling constant between spin I and S, t1 the time period

between the two 90º pulses, and ΩI the frequency difference between the off resonance

peak (of spin I) and the resonance frequency. The inphase term gives a signal in the

direct dimension (ω2) at ΩI and is modulated in the indirect dimension (ω1) by the

term ( )11sin tΩ . The antiphase component gives a signal at ΩS in the direct dimension but

is again modulated by ( )1sin tIΩ in the indirect dimension. Consequently, the inphase

term results in a diagonal peak at ΩI while the antiphase term results in a cross peak at ΩS

(direct dimension) and ΩI (indirect dimension). After Fourier transformation, the inphase

term (Equation 1.19) yields four double absorption mode lineshapes,

28

( ) ( )

( ) ( )

( ) ( )

( ) ( )ISSSISII

ISSSISII

ISSSISII

ISSSISII

JAJA

JAJA

JAJA

JAJA

ππ

ππ

ππ

ππ

−Ω−Ω+

+Ω−Ω+

−Ω+Ω+

+Ω+Ω+

41414

141

Equation 1.21

Similarly, after Fourier transformation four double absorption mode lineshapes are also

obtained for the antiphase term (Equation 1.20),

( ) ( )

( ) ( )

( ) ( )

( ) ( )ISSSISII

ISSSISII

ISSSISII

ISSSISII

JAJA

JAJA

JAJA

JAJA

ππ

ππ

ππ

ππ

−Ω−Ω+

+Ω−Ω−

−Ω+Ω−

+Ω+Ω+

41414

141

Equation 1.22

These signals give rise to diagonal and cross peaks obtained after analysis of the I spin.

Complementary results are obtained after analysis of the S spin, giving the characteristic

COSY spectrum (Figure 1.13) for a two spin system.92

Figure 1.13. An example of coupling in a COSY experiment between spins at frequencies of IΩ and SΩ . The black peaks are positive peaks, while the grey peaks are negative peaks.

29

1.7.3 Exchange Spectroscopy (EXSY)

Exchange spectroscopy can be conducted to elucidate exchange networks. The

pulse sequence is the same as that used in a two-dimensional nuclear Overhauser effect

spectroscopy (NOESY) experiment (Figure 1.14). The latter spectrum is similar to the

COSY and is employed to identify spins that are in close proximity ( < 5 Å) to each other.

The difference between the two experiments is that COSY cross peaks result for

coherence transfer though coupling, whereas NOESY cross peaks are due to cross

relaxation.

A brief description of the NOESY experiment is provided using a product

operator approach. During the evolution period, the spins are frequency labelled

according to their characteristic resonance frequencies. After the second 90º pulse, four

product operator terms are obtained of which only the Iz term,

( ( ) ( ) ZIIS IttJ 11 coscos Ω− π ) Equation 1.23

is relevant for this process. Cross relaxation can occur during the mixing period, τ,

giving rise to the signals,

( )( ) ( ) ( ) ZZZSZZI IRItIRt ⋅++⋅⋅Ω+−Ω− τστστ 11 cos1cos Equation 1.24

where Rz is the self-relaxation and σ the cross relaxation rate constants. The other terms

have the same definitions as in the above presented COSY. The first term is modulated

by ΩI in ω1 resulting in a diagonal peak, while the second is modulated by ΩS in ω1

giving a cross peak. The third term, an axial term, is not modulated in ω1, consequently

giving rise to a signal at a frequency of zero in the indirect dimension. These peaks can

be eliminated by incorporating a two phase (x, -x) cycle. The third 90º pulse is employed

to detect magnetization. In much the same manner, the frequency modulated z-

magnetization created after the second 90º pulse is affected by exchange processes that

also give rise to cross peaks.

Figure 1.14. A schematic of a two-dimensional NOESY pulse sequence.

30

1.7.4 Selective Inversion Recovery (SIR)

The type of NMR experiment chosen to calculate exchange rate constants

depends on the exchange regime under investigation. Line shape analysis is used in the

intermediate exchange regime, where the exchange rate is comparable to the difference

between chemical shifts (usually ~ 10 – 103 Hz). Offset saturation methods can be used

for faster exchange processes (usually > 103 Hz), while selective inversion recovery

experiments can be employed for slower exchanges (usually 0.1 – 10 Hz).93

The SIR is a T1 based experiment developed by Forsen and Hoffman.94 It is

based on perturbing the system away from equilibrium, by applying a selective 180º

pulse, and then watching the effect on all peaks in the spectrum over time (Figure 1.15).

The upper limit of its capabilities is dependent on the chemical shift difference between

peaks (i.e. must have a distinct peak to invert), while the lower boundary is related to the

relative T1 relaxation rate (for the inverted peak) to exchange rate ratio. The latter must

be fast enough to cause a change in the magnetizations of the non-inverted exchangeable

peaks. The T1 relaxation rate limits the experiment length (for one transient) because of

signal loss due to relaxation.

Figure 1.15. A schematic of the selective inversion recovery pulse sequence.

31

2 Experimental Section

2.1 Materials

Sodium fluoride (99%), Y(NO3)3·6H2O (99.9%), Gd(NO3)3·6H2O (99.9%) and

poly(acrylic acid) (PAA, MW = 1800 g/mol) were purchased from Sigma Aldrich

(Mississauga, ON, Canada). Citric acid (99.5%), aqueous ammonium hydroxide (28-

30%) and pH indicator strips (pH range 5-10) were acquired from EMD Chemicals Inc.

(Gibbstown, NJ, USA). All chemicals were used as received.

2.2 Methods

2.2.1 Citrate coated lanthanide nanoparticle synthesis

Citrate-coated lanthanide trifluoride (Cit-LnF3) nanoparticles (NPs) were

produced through a slight modification of the van Veggel protocol.71 A solution

containing citric acid (0.41 g, 2.13 mmol) and sodium fluoride (0.13 g, 3.00 mmol) in

distilled water (25 ml) was neutralized using concentrated ammonium hydroxide. The

solution was then heated in a round bottom flask, on an IKAMAG® RET basic safety

magnetic stirrer (Wilmington, NC, USA), to a temperature of 75 ºC using an oil bath.

Concurrently, the magnetic stirrer was set to 375 rpm and a one inch magnetic vane was

employed. An aqueous lanthanide solution (2.13 mmol of Ln(NO3)3, 2 ml of distilled

water) was titrated into the round bottom flask at an average rate of 2 ml/hr, using a 200

µl micropipette, in 10 µl increments. Initially, a slow rate was used (each drop added was

allowed to re-dissolve) but as the reaction turned cloudy (~ 20 min) the additions were

made at regular intervals of fifteen seconds. After ~ 30 min, the additions were made at

shorter, but still regular intervals of ten seconds. The resulting mixture was left to react

for a further two hours. Ethanol was then added to the mixture to precipitate the

nanoparticles, which were subsequently isolated though centrifugation (7500 rpm, 5 min.,

supernatant decanted). This was followed by resuspension in water, precipitation with

ethanol and centrifugation once again (7500 rpm, 5 min., supernatant decanted) . The

nanoparticles were then dried in a dessicator under house vacuum.

32

2.2.2 PAA coated lanthanide nanoparticle synthesis

Citrate coated LnF3 nanoparticles were first synthesized according to the above

protocol. PAA was substituted for citrate by manual addition of 2 ml of a 128 mM PAA

solution at a rate of 1 drop/second using a 2 ml syringe with a 26G1/2 needle. The

exchange was allowed to proceed for 12 hours at 75ºC. Identically to the above Cit-LnF3

NP procedure, nanoparticles were isolated though two ethanol precipitation steps.

2.2.3 Electron Microscopy

Nanoparticles were verified though use of a Hitachi H-7000 scanning

transmission electron microscope (STEM) operating at an accelerating voltage of 200 kV.

The grids were prepared by coating 200 mesh copper grids (Sigma Aldrich, Mississauga,

ON, Canada) with a solution of 0.5% formvar in 1,2-ethylene dichloride. A drop of

lanthanide nanoparticle solution (~10 mg/ml) was placed on the grid for a minute, after

which it was drawn off by capillary action using a Kimwipe and air-dried. Dark field

images were then acquired.

2.2.4 Nanoparticle Size Analysis

Nanoparticle sizes were measured using ImageJ. Upper and lower threshold

limits (Image>Adjust>Threshold) and the scale (Analyze>SetScale..) were set using

options in the program. Particles sizes were typically determined (Analyze>Analyze

Particles..) with the following processing conditions: size between 100/314 – infinity nm2,

and a circularity between 0.8 – 1.00. Using the generated data, size distributions were

plotted and average size and a polydispersity index value was computed for each

synthesized nanoparticle batch.

2.2.5 Zeta potential measurements

Zeta, ζ, potential measurements were conducted using a Zetasizer 3000HS

(Malvern Instruments). The instrument was flushed with 15 ml of deionized water,

before 5 ml of diluted nanoparticle solution was injected into it. Five replicates were

taken for each nanoparticle sample, the average of which was reported as the ζ potential.

33

2.2.6 NMR

2.2.6.1 NMR Calibration Standards

A 90:10, D2O:H2O sample was prepared to calibrate the gradient strength of the

500 and 600 MHz spectrometers. In addition, an ethylene glycol standard (Varian Inc.,

Palo Alto, CA) was used to calibrate the temperature between 0 and 45ºC.

2.2.6.2 NMR Sample Preparation

A citrate control (35 mM citric acid, 20 mM NaOH, pH 9.0), Cit-YF3 NP (10

mg/ml NP, 10 mM NaOH, 90:10 D2O:H2O, pH 8.5), 1:1 Y:Cit control (23.9 mM citric

acid, 25.3 mM Y(NO3)3, 106 mM NaOH, 90:10 D2O:H2O, pH 8.5), 1:10 Y:Cit control

(21.7 mM citric acid, 2.1 mM Y(NO3)3, 31.9 mM NaOH, 90:10 D2O:H2O, pH 8.5) and

PAA-YF3 NP (19.3 mg/ml NP, 10 mM NaOH, 95:5 D2O:H2O, pH 8.5) samples were

prepared in 5 mm Economy NMR tubes (Wilmad-Labglass, Vineland, NJ). The pH was

tested using pH indicator strips (EMD, Gibbstown, NJ, USA).

2.2.6.3 NMR Experiments

One-dimensional proton NMR, COSY, PFGSTE, EXSY and SIR (CRYO)

experiments were conducted on a Varian Unity 600 spectrometer using either an HFCN

quad probe (Varian Inc., Palo Alto, CA) or a HCN cold-probe (Varian Inc, Palo Alto,

CA). The pulse sequences for the various experiments are provided in the Introduction

section. 1H NMR experiments were performed at 600.34 MHz. Typically, 90º pulse

lengths of 10.5 µs and 8 µs were used for the HFCN and cold-probe respectively, while

an exponential multiplication equivalent to 1 Hz line broadening was employed before

Fourier transformation. A sufficient recycle delay (usually 5 x T1) was selected to allow

for essentially full relaxation in the 1D1H, PFGSTE, EXSY and SIR experiments.

Parameters specific to each of the different NMR experiments are provided below.

2.2.6.3.1 Correlation Spectroscopy (COSY)

A standard 1H magnitude only COSY was acquired with a sweep width of 5500

Hz. The number of increments taken during the evolution period was 512, resulting in a

10.7 Hz resolution in the indirect dimension. Eight transients were taken per increment.

34

2.2.6.3.2 Pulsed Field Gradient Stimulated Echo (PFGSTE) Diffusion Experiments

A pulsed field gradient stimulated echo (PFGSTE) experiment95, 96 was modified

to incorporate a CHIRP based z-filter,91 between the first and second 90º pulses, to

reduce the effects of undesired coherences. The duration of the CHIRP pulse was

typically set to 25 ms, while the simultaneously applied gradient had the same duration

and an amplitude of 0.76 T/m. This gradient was calibrated as per the recommendations

of Thrippleton and Keeler.91 In addition, a sixteen step phase cycle scheme was

employed to filter out unwanted echoes.90

General parameters of the experiment are as follows. The PFGSTE gradients

were applied along the z direction and turned on for durations of 3 ms. The spin echo

delay was typically set to between 5 – 10 ms and the T1 delay between 100 – 400 ms, the

spectral width was 4 kHz and the data size 4-K. Finally, the gradient amplitude was

arrayed between 15 values, chosen such that a ninety percent decay in peak intensity was

achieved.

2.2.6.3.3 Exchange Spectroscopy (EXSY)

A standard Varian EXSY pulse sequence, which included a pre-programmed

CHIRP and homospoil gradient, was employed. The parameters of the CHIRP were once

again calibrated according to the methods of Thrippleton and Keeler.91 The duration of

the CHIRP and the homospoil gradient was 50 and 2.5 ms respectively. The strength of

the CHIRP gradient was 0.67 T/m while that of the homospoil was 0.89 T/m. Other

experimental parameters include a spectral width of 5.5 kHz, 1.5-K data points, eight

transients and 256 increments, the latter of which yielded 21 Hz resolution in the indirect

dimension. Four 2-D EXSY spectra were collected with mixing times of 100, 200, 400

and 800 ms.

2.2.6.3.4 Selective inversion recovery (SIR)

Selective inversion recover experiments were conducted using a soft 180º E-

BURP-1 pulse,97 of length 25.3 ms, centered on the resonance to be inverted. The

spectral width was 10 kHz and the data size 20-K. Twenty exchange time delay values

ranging from 1 ms to 5 s were employed. Each arrayed delay value was signal averaged

over 512 transients. Standard non-selective inversion recovery experiments were used to

estimate spin-lattice relaxation rates for the various observed states.

35

3 Results and Discussion

Macromolecular, lanthanide contrast agents, with potential MRI-based cancer

detection capabilities, have been investigated. Citrate-coated gadolinium trifluoride

nanoparticles (Cit-GdF3 NPs) were synthesized and their citrate surface binding and

exchange properties characterized. Although these nanoparticles have been produced

before,71 more refined reaction conditions are presented that allow for greater control of

important factors such as the size and dispersity of the nanoparticles. Furthermore, a new

two-step synthesis protocol is advanced, and verified, whereby poly(acrylic acid)-coated

gadolinium trifluoride nanoparticles (PAA-GdF3 NPs) are synthesized from Cit-GdF3 NP

precursors (A similar ligand exchange and verification has been achieved with quantum

dots98). The tremendous versatility of this procedure is that any ligand can bind to the

surface of the nanoparticle so long as it can adhere more strongly than citrate. This

circumvents the need to venture into potentially difficult and tricky direct synthesis

methods, while yielding the benefits afforded by new ligands.

Note: All references to polydispersity, dispersity or dispersion, henceforth mentioned,

refer to nanoparticle size distributions unless qualified otherwise.

3.1 Ligand Properties – citrate and poly(acrylic acid) (PAA)

Two different ligands were used to coat the lanthanide trifluoride nanoparticles:

citrate and poly(acrylic acid) (Figure 3.1). The motivation behind the use of a polymer

was driven by the deficiencies of citrate: primarily its relatively low binding affinity for

the surface of the nanoparticle, which leads to higher in vivo toxicity and lower stability,

and the comparatively low relaxivity of this nanoparticle-ligand conjugate. Binding

affinity, absence of cross-linking ability between nanoparticles, and ease of

functionalization were considerations taken into account when selecting the polymer type

and length. The chosen PAA, a 25-mer (PAA25), has a total of 25 carboxylic acid groups,

as each acrylate monomer possesses one such group. Since the number of carboxylic

acids represents the number of theoretical binding sites, PAA25 may be expected to bind

with greater affinity to the surface of the nanoparticle than citrate (3 carboxylates, 1

hydroxyl). In addition, the acidic nature of the polymer (pKa ~ 4.58) facilitates a

36

stronger electrostatic interaction, at neutral conditions, with the positively charged

nanoparticle surface.

A short polymer size was chosen to prevent cross-linking of nanoparticles, where

an acceptable PAA upper size length was restricted to a value less than the nanoparticle

diameter. This constraint required a quantitative estimate of polymer size. Several

different measures of length, such as the mean-squared end-to-end length (Flory radius),

mean-squared radius of gyration and hydrodynamic radius, can be employed. However,

as a short polymer in the fully deprotonated state is likely to exist in its fully extended

conformation, the contour length, which is the physically possible maximum length,

might be the most appropriate measurement. For PAA25 this is calculated to be ~ 7 nm

(assuming a carbon covalent radius of 0.762 Å99). As the synthesized nanoparticles are

on average 50 nm or greater in diameter, PAA25 is, by and large, unlikely to cause cross-

linking.

The presence of carboxylate groups is advantageous because they facilitate the

conjugation of targeting molecules to the surface of the nanoparticle. For example, folic

acid, whose receptor is overexpressed in forty percent of human cancers,100 can be

attached to PAA though a water soluble synthesis using N-(3-Dimethylaminopropyl)-N’-

ethylcarbodiimide hydrochloride (EDAC), N-hydroxyl succinimide (NHS) and an

ethylene diamine linker.101

Figure 3.1. The structures of (a) citric acid and (b) poly(acrylic acid). The Greek letters α and β are used to help discriminate the α-hydroxyl, α-carboxylate and the two β-carboxylates in citric acid. The letter n represents the number of monomers comprising the polymer.

37

3.2 Nanoparticle synthesis procedures

3.2.1 Citrate-coated lanthanide trifluoride nanoparticle synthesis

The citrate-coated lanthanide trifluoride nanoparticle (Cit-LnF3 NP) synthesis

involved the dropwise addition of a Gd(NO3)3 stock into a neutralized solution containing

NaF and citrate (T = 75 ºC, stirring ~ 375 rpm on a IKAMAG® RET basic safety

magnetic stirrer). Upon addition of the first drop (~ 10 µl), a white precipitate (GdF3

aggregate) initially formed before dissolving over a period of a few seconds. Each

subsequent drop was added only after dissolution of the previous one. After addition of

about 0.15 ml, the solution displayed a tinge of white, likely indicating the formation of

nuclei large enough and in sufficient numbers to scatter light. The mixture turned cloudy

and opaque after ~ 0.6 ml of lanthanide stock was added and the disappearance of the

next drop could no longer be observed. At the end of the reaction the mixture remained

an opaque white (If left to stand for an extended time (a few hours) in the round-bottom

flask, a white precipitate was often observed, perhaps indicating that the nanoparticles

were not colloidally stable under natural reaction conditions. Although an alternative

explanation could be that large, insoluble non-nanoparticle material also formed, this was

not observed in sufficient frequency (evaluated using STEM) to completely attribute the

precipitate to this reason). Finally, addition of a non-solvent (e.g. acetone or ethanol)

resulted in immediate precipitation of the nanoparticles from solution.

3.2.2 Poly(acrylic acid)-coated lanthanide trifluoride nanoparticle synthesis

Following previous work,102 a direct approach was initially taken when attempting

to synthesize poly(acrylic acid)-coated gadolinium trifluoride nanoparticles (PAA25-GdF3

NPs). Into a neutralized solution of PAA25 and NaF, a stock solution of Gd(NO3)3 was

added dropwise (T = 75 ºC, with stirring). Although one variation of the synthesis

yielded remarkably monodisperse particles (Figure 3.2), synthesis reproducibility proved

elusive and an indirect, two-step approach was henceforth taken to produce PAA25-GdF3

nanoparticles. This firstly involved the production of citrate-coated gadolinium

trifluoride nanoparticles (Cit-GdF3 NPs),71 followed by exchange of citrate for PAA25

(Refer to experimental for specific details). Observations during and at the end of the

reaction were similar to those encountered for Cit-LnF3 NPs.

38

3.3 Synthesis Verification and Characterization of Size Polydispersity

Nanoparticle formation, and consequently synthesis success, was primarily

verified using annular darkfield scanning transmission electron microscopy (STEM). In

addition to images, energy dispersive X-ray (EDX) linescans were taken to verify

nanoparticle composition. Figure 3.3 depicts one obtained for nanoparticles composed of

europium and gadolinium. EDX analysis indicates a nanoparticle core composed of the

elements gadolinium, europium, fluorine and likely sodium. Weaker signals (oxygen and

carbon) corresponding to the citrate ligand are also observed. Unfortunately, the same

analysis could not be obtained for subsequent Cit-GdF3 and PAA-GdF3 nanoparticles

because the EDX feature is currently out of operation (and has been for the last few

months).

Sizes were extracted from dispersed nanoparticle images, which had high uniform

contrast, using ImageJ, a computer program. As size was often polydisperse, the count

was biased toward large sizes which appeared brighter in the images than much smaller

ones. The number of nanoparticles counted varied between 22 – 170, which is much

lower than the recommended number (>1000) for statistically significant analysis103 (this

low number was due to the prohibitive cost of imaging). However, the dispersion

appeared to mesh satisfactorily with visual conclusions, and as only rough trends in

nanoparticle synthesis were sought, these counts were deemed sufficient to ascertain

them.

Polydispersity indices (PDIs) were chosen to evaluate the breadth of the

distribution over alternative methods such as standard deviation and variance. As PDIs

were not commonly encountered when discussing nanoparticle size distribution in

literature, a brief attempt will be made to justify their use. They were primarily

employed because the calculated standard deviations are large numbers (in the ten

thousands for NPs) which are cumbersome to deal with, whereas the PDI presents a

neater way of presenting the same measure. In fact, the standard deviation can be

quantitatively related to the PDI as will be shown.

The polydispersity index is most commonly encountered when describing

polymer molar mass dispersity and is stated to be,

39

N

W

M

MPDI = Equation 3.1

where MW and MN are the weight average molar mass and the number average molar

mass respectively.104 Similar equations can also be used to represent nanoparticle size

polydispersity. The PDI can be represented by the ratio,

N

W

D

DPDI = Equation 3.2

where DN and DW are defined as,

∑

∑=

xx

xxx

NN

DN

D Equation 3.3

∑

∑=

xxx

xxx

WDN

DN

D

2

Equation 3.4

and where Nx is the number of molecules of length x and Dx the diameter of molecules of

length x. The relationship between the PDI and the standard deviation (σ) can be shown

to equal,

12

−=

N

W

N D

D

D

σ Equation 3.5

For a perfectly monodisperse sample, the PDI equals 1.00.104 Increasing sample

polydispersity leads to larger values. Typical PDI values for the synthesized

nanoparticles ranged from 1.02 to 1.23.

40

3.4 PAA25-LnF3 NP - Direct Synthesis Images

One synthesis yielded relatively monodisperse particles (Figure 3.2) that

displayed T1 and T2 relaxivities of 38 and 80 Hz·(mg/ml)-1 (1.5 T, 20 ºC), the former of

which is about six times larger than that observed for commercial Gd chelates (e.g. Gd-

DTPA, 6 Hz·(mg/ml)-1, 1.5 T, 37 ºC).105 The size of the nanoparticles ranged from 80 –

158 nm, with a mean size of 112 nm and a polydispersity index (PDI) of 1.02. Although

this synthesis proved irreproducible, the high relaxivities obtained served as the impetus

behind the creation of a new, two-step PAA25-LnF3 nanoparticle synthesis protocol.

On average, all nanoparticles (PAA-LnF3 and Cit-LnF3 NPs) were roughly

spherical, with an irregular surface, regardless of lanthanide composition or ligand

coating (Figure 3.2). This irregular surface may be due to either fast growth of the lattice

or a hierarchal structure. In the former, nanoparticles may mature with defects in the

lattice resulting in irregular expansion, whereas in the latter, building blocks may first

form smaller components, which then nucleate to give the final nano-aggregate. It is

possible that high resolution transmission electron microscopy (HR-TEM) might provide

sufficient resolution to discriminate between these growth mechanisms. Regardless, the

larger nanoparticle surface to volume ratio, due to surface irregularity, is beneficial as

ninety percent of relaxivity is attributed to inner sphere effects.23

41

0

10

20

30

40

50

60

70

80

25 125 225 325 425 525 625 725 825 925 1025

Nanoparticle Size Range (nm)

Nan

op

art

icle

Ab

un

dan

ce

(%

)

Figure 3.2. STEM images of relatively monodisperse PAA25-GdF3 NPs with an associated size dispersion profile. Evidence of finer structure, in a PAA25-YF3 nanoparticle, is visible in the central image.

42

Figure 3.3. An energy dispersive X-ray linescan of 90/10 Gd/EuF3 nanoparticles. In the bar graphs, the x-axis gives the distance along the drawn line in the STEM image, while the y-axis is a measure of the X-ray signal observed. Significant signals from elements comprising the core of the nanoparticle (europium, gadolinium, fluorine and sodium) are present. Weaker signals from carbon and oxygen, likely from citrate, are also observed. A large background carbon signal is due to the formvar coating. Titanium and barium were selected as controls, as they were not present in the nanoparticle, and their very weak signals were as expected.

43

3.5 Control of Nanoparticles Size and Dispersity

Nanoparticle biodistribution is closely linked to particle size. As a case in point,

whereas small gold nanorods (~ 10 nm) were found to collect in liver, spleen, kidney,

testis, thymus, heart, lung and brain, larger particles (50 and 250 nm) were only found to

localize in the liver and spleen.106 Hence, control of nanoparticle size and polydispersity

is crucial to desired function.

Cursory experiments were attempted to ascertain rough trends in the synthesis of

Cit-GdF3 nanoparticles. The rate of lanthanide addition and amount of lanthanide added

were varied to examine effects on size and dispersity. Lump-sum additions of lanthanide

were experimented with to observe the same. Unless otherwise stated, all reaction

conditions were kept the same as in the protocol stated in the Experimental section.

3.5.1 Rate of Lanthanide Addition

Based on the nucleation and growth model (Figure 1.9), it was expected that a fast

rate of addition would yield smaller, more polydisperse nanoparticles if it results in a

longer nucleation period. In such a period, the concentration of the free lanthanide

trifluoride (LnF3) building block will not drop below the nucleation concentration

between additions of the lanthanide nitrate. An increase in the duration of the nucleation

period will cause two effects. Firstly, it will lead to a greater number of nuclei forming.

Secondly, it may induce an increase in polydispersity because nucleation and growth

occur simultaneously during this period. Consequently, for a fast addition, the smaller

size is due to the same number of building blocks being spread over a greater number of

nuclei.

Gadolinium addition was varied between slow (2 ml in 1 hr), intermediate (2 ml

in 40 min) and fast (2 ml in 7 min) rates of additions (Figure 3.4). The slow addition

yielded a larger average size, while the intermediate and fast additions produced

nanoparticles of similar size. The size range of the slow addition was roughly twice as

large as that of the intermediate and fast additions, while the PDI showed so discernable

trend across the series (Table 3.1, Figure 3.4). In the slow addition, the large distribution

likely indicated that nucleation occurred for a longer period than in the intermediate and

fast additions perhaps due to fluctuation of the building block concentration about the

44

nucleation concentration during gadolinium addition. This might indicate that

gadolinium addition, even in the case of a slow rate, is still occurring fast enough to cause

multiple nucleation events. The relatively similar results for the intermediate and fast

additions might demonstrate that there is a certain limit to the rate of addition above

which increasing the rate does not yield significant differences in the size range. It is

important to note, that slow and fast are relatively terms, whose relativity depend on the

rate at which the building block is consumed (which is unknown). What was termed

intermediate might, in fact, be fast, and what was thought to be slow was clearly fast

enough to cause continuous nucleation.

Table 3.1. Nanoparticle size distribution characteristics Size (nm) Parameter Varied Parameter value Minimum Maximum Range Average PDI

Slow (1 hr) 279 1049 770 670 1.13 Intermediate (40 min) 72 471 399 230 1.19

Gadolinium rate of addition

Fast (7 min) 71 391 320 234 1.11 Total gadolinium 1.065 mmol* 322 702 380 596 1.05

1 ml 114 349 235 221 1.19 Lump-sum addition 1.5 ml 86 718 632 422 1.19

Cit-YF3 90 222 132 140 1.10 Yttrium nanoparticles PAA-YF3 20 215 195 103 1.23 * The total lanthanide added was half what is normally added (i.e. 2.13 mmol)

45

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Figure 3.4. STEM images of syntheses with (a) slow (2 ml in 1 hr), (b) intermediate (2 ml in 40 min) and (c) fast (2 ml in 7 min) gadolinium nitrate rates of addition. Their corresponding size dispersion profiles are shown.

46

3.5.2 Reduction in Total Lanthanide Feedstock

The amount of gadolinium feedstock and the rate of addition were both halved (1

ml in 1 hr), compared to the previously mentioned slow addition, in the expectation of

affecting nanoparticle size and increasing monodispersity. A remarkable increase in the

latter property was observed with roughly seventy percent of nanoparticles falling within

a 50 nm range (Figure 3.5). The more monodisperse results obtained here mesh with the

slow rate of addition (2 ml in 1 hr) results obtained in the previous section, where it was

postulated, that decreasing the rate of addition might result in reduction or elimination of

multiple nucleation events. The slower rate of addition appears to have reduced these

events leading to more monodisperse nanoparticles of fairly large size. It is possible that

if the same reaction conditions were used, except with an even slower rate of addition, a

further improvement in monodispersity may be triggered. This is similar to what is

predicted from the La Mer diagram, and what some published nanoparticle syntheses

hope to or have achieved,107 where effective separation of the nucleation and growth

processes lead to a burst of nucleation, followed by diffusion controlled growth.

The change in the lanthanide to fluoride ratio poses a potentially troubling

problem as it might reduce the affinity of a ligand for the nanoparticle surface. In the

original synthesis, this ratio was 1:2.3 resulting in a fluoride deficiency towards the end

of the reaction. A distribution of LnF3, LnF2+, LnF2+ and Ln3+ species may thus form,

perhaps coating the nanoparticle with a more positive charge than otherwise (Zeta

potential measurements presented later). However, as in this experiment the ratio was

1:4.6, lanthanide trifluorides are still forming towards the end of the addition possibly

reducing the effective positive charge on the lanthanide surface. In the future, it would be

interesting to verify the presence or absence of this affinity effect though use of NMR

experiments (pulsed field gradient stimulated echo, selective inversion recovery) which

can probe ligand exchange. Additionally, zeta potential measurements can provide

complementary data.

47

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Na

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Figure 3.5. STEM images of the same synthesis, at different magnifications (scale on the lower right of the images), where half the usual amount of lanthanide feedstock was added. Although the nanoparticle may appear to be aggregated in some of the images, this is actually due to improper focusing. The corresponding size distribution is also presented.

3.5.3 Lump-sum lanthanide stock additions

In attempts to reduce the average nanoparticle size, it was hypothesized that a

large lump-sum addition of the lanthanide nitrate stock would produce a larger number of

nuclei. As stated previously, a greater number of nucleation centers for the same given

amount of building block reduces average nanoparticle size. Also, if the resulting

nucleation period was still short enough then no effective change in polydispersity would

occur as nuclei would all form at approximately the same time.

Two separate reactions were conducted where 1.00 and 1.50 ml of the 2.00 ml

lanthanide stock was added in lump-sum additions. This was followed by slow addition

of the remaining stock. The 1.00 ml lump-sum addition resulted in nanoparticles of a

smaller average size and range (Figure 3.6, Table 3.1), while the 1.5 ml addition

48

produced larger, more polydisperse nanoparticles. The observed increase in

polydispersity of the latter was expected due to the longer nucleation period, however the

increase in average size compared to the 1.00 ml lump-sum addition was not.

0

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Figure 3.6. STEM images of (a) 1.00 and (b) 1.5 ml lump-sum additions of lanthanide feedstock. Their associated size dispersion profiles are provided.

In conclusion, three methods have been covered that have shown potential to

control nanoparticle size: lump-sum addition, rate of addition, and the amount of

lanthanide added. Controlling the rate of addition and the amount of lanthanide appear

key to producing monodisperse nanoparticles via a coprecipitation method. Other

mechanical ways, such as stir-rate variation can also be used to control size. From the

synthesis conducted, the general impression is that control of average size is easy but that

of mondodispersity much harder. Also, as numerous ways: seeding growth, reflux

ripening, size-selective precipitation and electrophoresis, have been used in the synthesis

of monodisperse gold nanoparticles,108 it is possible that some of these methods can also

achieve the same for lanthanide nanoparticles, without undue effort.

49

3.6 NMR Studies

Due to the importance of ligand interactions with the nanoparticle, a major part of

this work involved the characterization of citrate binding to the nanoparticle surface. As

the paramagnetic gadolinium induces severe broadening and consequent loss of peaks,

diamagnetic lanthanide substitutes were used to form the core of the nanoparticle.

Lanthanum (La), lutetium (Lu) and yttrium (Y) with ionic radii of 1.22, 1.03 and 1.08 Å

respectively, all are able candidates. Although the latter is not strictly a lanthanide, it

nevertheless displays properties very similar to these elements and is often grouped with

them. Since the chemical properties of the lanthanides are usually dictated by ionic

radius, yttrium was chosen because it has the one most similar to gadolinium (1.11 Å).109

Cit-YF3 and PAA-YF3 nanoparticles were synthesized in the same manner as their

gadolinium counterparts, except for the lanthanide employed. Nanoparticle presence was

verified for all samples used for NMR studies. The second step (titration of the polymer

into the Cit-YF3 NP solution) of the two-step synthesis procedure for PAA-YF3 NPs was

found to result in imperceptible changes in nanoparticle size and is consequently

expected to only result in direct substitution of PAA for citrate. Images and size

distributions of yttrium nanoparticles are given in Figure 3.7 while average sizes, ranges

and PDI values are provided in Table 3.1. The averaged (four values) zeta (ζ) potential

was found to be -3.6 mV for Cit-YF3 NPs indicating a negative coating on the

nanoparticle surface. However, this was still considerably lower than that of their

paramagnetic analogs (Cit-GdF3 NPs [-73 mV] and PAA-LnF3 NPs [-37 mV]).110

To ensure the absence of excess citrate, which could influence later calculations

of the population of free or bound (to the nanoparticle surface) ligand, multiple

precipitations were conducted with ethanol and the amount of citrate in the nanoparticle

precipitate quantified after each step using an acetonitrile standard. This particular

standard was chosen because it was miscible with water, displayed peaks shifted away

from citrate, was unlikely to bind to the nanoparticle, and had a strong single resonance in

the proton spectrum. After each precipitation step, a measured weight of nanoparticle

was collected and acidified (pH < 1) in an NMR tube to deprotonate citrate (pKa1 = 3.220,

pKa2 = 4.837, pKa3 = 6.393 at 0 ºC111) thereby reducing its affinity for the nanoparticle

surface. Consequently, peaks due to bound fractions disappeared yielding only the four

50

free citrate resonances which could be more accurately quantified. Acidification, for

quantification purposes, is not a necessary step but can lead to more accurate estimates of

the total citrate population. One ethanol precipitation step was found to lead to 28 %

citrate by weight, while two resulted in 24 %. This likely indicates that very little excess

citrate is present even after just one precipitation step.

0

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Figure 3.7 (a) Cit-YF3 and (b) PAA-YF3 nanoparticles with their respective size distributions.

3.6.1 One-Dimensional Proton (1D 1H) Spectra of Citrate and Cit-YF3

Nanoparticles

One-dimensional proton (1D 1H) spectra were acquired for a citrate control and

Cit-YF3 nanoparticles for preliminary visualization and comparison of the systems. In

addition, all resonances in the Cit-YF3 spectrum were verified to unambiguously belong

to citrate. Finally, a variable temperature series of 1D 1H spectra were used to evaluate

the presence of exchange and the effect of temperature hysteresis.

51

Similar to literature,112 the control displayed characteristic AB peaks arising from

the strong, two-bond coupling of methylene protons (Figure 3.8. a) which result because

of the chiral α-carbon and slow rotation about the α-carbon, β-carbon bond. For such a

system, several equations can be used to equate frequency differences to parameters like

the J-coupling constant for protons A and B (JAB), and the chemical shift difference (δ)

between the two proton resonances (νA and νB).113 The former is described by the

equation,

4321 νννν −=−=ABJ Equation 3.6

where ν1 and ν2 are the outer and inner frequencies of the downfield doublet respectively,

and ν3 and ν4 the inner and outer frequencies of the upfield doublet respectively. The

latter is given by the relationship,

( )( )[ ] 2/13241 ννννδ −−= Equation 3.7

Finally, the ratio between the inner and outer frequencies (doublet ratio) can also be

equated to the observed frequencies,

32

41

νν

νν

−

−==

resonancesouterofIntensity

resonancesinnerofIntensityratiodoublet

Equation 3.8

For free citrate (citrate control), JAB was 15.0 Hz, δ 48.7 Hz, and the doublet ratio 1.61

(600 MHz, pH = 8.5, 0 ºC). The doublet ratio is important because it can provide a

quantitative indication of the type of coupling (strong vs weak) and, as such, was used to

derive some of the inferences stated here. For a strongly coupled system (AB), such as

citrate, this ratio is greater than one, while for its weakly coupled counterpart (AX) it is

equal to one (i.e. the resonances in each doublet are of the same intensity).

52

Figure 3.8. One dimensional spectra of (a) citrate (pH ~ 8.5, 600 MHz), with assigned proton resonances, (b) Cit-YF3 NP (pH ~ 8.5, 600 MHz) and (c) acidified Cit-YF3 NP (pH < 1, 500 MHz). All samples were taken at 0 ºC. The chemical shift and J-coupling difference between the citrate peaks in (a) and (c) is due to the pH difference between the samples.

The Cit-YF3 NPs displayed a multitude of peaks possibly corresponding to free

and bound citrate states (Figure 3.8. b). Of these peaks, the four largest in the spectrum

(2.22, 2.19, 2.11, 2.08 ppm) were inferred to be citrate in the free state because their

doublet ratios (1.38 and 1.21) were closest to that of the citrate control (1.61). Although,

53

they seem to be quite different, it is argued, and proved later, that this is due to

underlying bound states that skew the ratios. The rest of the peaks were taken to

correspond to bound states. The best separated of these were two doublets, present at

2.68 and 2.59 ppm, that appeared to move from AB to AX systems. Their doublet ratios

at 1.11 and 1.09 respectively, provided clear evidence of such a movement. The change

in the ratios may be driven by a change in chemical shift induced by binding

(carboxylates or hydroxyl) adjacent to one proton of the methylene, but not the other.

The large upfield shift of the free citrate peaks in the nanoparticle sample (~ 0.3 ppm)

was unexpected as it should have been identical to that observed in the citrate control.

Similarly, the upfield shift of the bound citrates states were likewise unexpected as

binding to the positive lanthanide surface should have induced, though an electron

withdrawing effect, a downfield shift in the methylene resonances. It is possible that the

nanoparticles (10 mg/ml) may have affected the chemical shift of the water reference

peak.

The one dimensional proton spectrum of an acidified nanoparticle sample was

acquired, as a control, to confirm that all peaks in the spectrum were due to citrate. In

some nanoparticle syntheses, citrate is often used as a metal reducing agent where the end

result produces metal nanoparticles and citrate thermal decomposition products. The one

dimensional proton spectrum of these products bears a resemblance to that of Cit-YF3

NPs.114 Consequently, an experiment was conducted to eliminate this possibility. It was

hypothesized that in an acidified (pH < 1) nanoparticle sample, if the peaks were due to

free and bound states, protonation would cause disappearance of bound peaks.

Conversely, if due to thermal decomposition then these peaks would still be observed.

Spectral evidence (Figure 3.8. c) indicates that the former hypothesis is true, and

consequently renders the above conclusions and conjectures still meaningful.

Spectral analysis is complicated by the pH and temperature dependence of citrate

chemical shifts and J-couplings. These parameters vary over a pH range related to

changing protonated, carboxylate fractions. Chemical shifts, for example, were found to

vary by ~ 0.4 ppm between a pH range of 2 – 7.5.112 A similar dependence on

temperature is possible, due to slight changes (~ 0.1 pH units) in the pKa values of citrate

carboxylic acids over 0 – 50 ºC.111 The pH dependence of citrate NMR parameters in

54

acidified and basic samples was found to be comparable to literature values (Table 3.2,

note that the temperature is different between experimental and literature data). Further

complications are posed by the pH-dependent chemical shift nature of water, to which the

citrate peaks are referenced. Thus, NMR results can only be compared across samples

under identical acidic/basic and thermal conditions. It should be noted that basicity was

tested, in all samples, with pH paper with an error of ~ 0.25 units.

Table 3.2 Comparison of different parameters in acidified Cit-YF3 NP and citrate control samples with literature values pH < 1 pH ~ 8.5

Acidified NP sample

(500 MHz, 0 ºC) Literature (400

MHz, 24 ºC) Citrate Control

(600 MHz, 0 ºC) Literature (400

MHz, 24 ºC) Doublet ratio 1.41 Unknown 1.61 Unknown

JAB (Hz) 15.0 15.2 15.9 15.9 δ (Hz) 48.7 Unknown 88 Unknown ∆ (Hz) 90.5 74.5 60 57.5

δcit (ppm) 2.437 2.42 2.860 2.81

Doublet ratio, JAB and δ are as defined in the discussion above ∆ is the frequency difference between the midpoints of the two doublets

δcit is the chemical shift of the midpoint of the citrate resonances Literature values from Moore et al.

112

Variable temperature (VT) one dimensional spectra were obtained to offer

evidence of exchange between different citrate states in the nanoparticle sample (Figure

3.9). It was expected that an increase in temperature would cause an increase in

exchange between states, which would result in exchange broadening at higher

temperatures. The most telling example is provided by the peaks circled in red (region i),

where at 0 ºC two doublets are present, at 25 ºC loss of double structure occurs and at 45

ºC only one broad peak is observed. Although it appears that exchange is occurring

between the two doublets in region i, this is not true as the chemical shift difference

between these doublets is constant (~ 0.085 ppm) as temperature increases. Instead,

exchange seems to occur with the major peaks (possibly the free citrate state) since the

chemical shift difference between them decreases by about 0.03 ppm. The blue and

green circled areas, regions ii and iii respectively, also display loss of peaks. It should be

noted that in the absence of exchange a sharpening of peaks should be observed for free

55

citrate (four major peaks) as temperature is increased. This results because, for a small

molecule (i.e. fast motion regime), increasing temperature causes a decrease in the

rotational correlation time, increasing the T2 relaxation time, which causes a narrowing of

peaks. Consequently, loss of fine structure in region ii is possibly due to exchange with

bound states.

Temperature hysteresis was briefly examined in the same variable temperature

series as above (Figure 3.9). Although, the same general features were present in the

spectra, changes were observed especially in region i at 0 ºC, where one of the two

doublets was either absent or present in much lower intensity. Thus, either the system

experiences slight hysteresis effects or the sample was not given sufficient time (20 min.)

to equilibrate.

56

Figure 3.9. Temperature dependence, between 0 – 45 ºC, of the citrate peaks in citrate-coated YF3 NPs. Other spectra, 25 ºC and 0 ºC, show the effect due to temperature hysteresis. The peak present at 3.2 ppm in the 0 ºC spectrum is due to residual ethanol from the precipitation step.

57

3.6.2 Correlation Spectroscopy (COSY)

Several COSY spectra were acquired to complement the information provided by

the one dimensional spectra. A COSY spectrum of Cit-YF3 NPs was used to elucidate

the observed complexity in the proton 1D spectrum. Control spectra with low (1:10) and

intermediate (1:1) yttrium:citrate (Y:Cit) ratios were acquired to eliminate the possibility

of citrate binding to free lanthanides. Unfortunately, high Y:Cit ratios of 4:1 or even 2:1

could not be collected due to lanthanide hydroxide precipitation at basic pH (~ 8.5).

The COSY spectrum of the Cit-YF3 NPs provided a wealth of information about

the relationships of peaks in the one dimensional spectrum. At least ten different

methylene states were observed with even the slightest peaks in the one dimensional

spectrum accounted for (Figure 3.11, chemical shift assignments in the Supporting Data

Table SD1). These peaks likely indicate numerous citrate binding geometries to the

nanoparticle surface, and may resemble some of the binding modes present in lanthanide

coordination polymers (Figure 3.10). The citrate has seven sites capable of coordinating

to the metal: one from the α-hydroxyl, two from the α-carboxylate and four from the two

β-carboxylates (Figure 3.1). In the lanthanide coordination polymers, the greatest

number of citrate functional groups that coordinated to the lanthanide was three. These

were the α-hydroxyl, α-carboxylate and one β-carboxylate, where each donated one

oxygen to a particular metal site.115

Figure 3.10. Different carboxylate coordination modes to positively charged metal centers. (Reprinted from J. Mol. Struct. 2008, 877, 115-122. Copyright (2008), with permission from Elsevier).

The COSY spectrum of the intermediate Y:Cit (1:1) (Figure 3.12) and the low

Y:Cit (1:10) (Figure 3.13) ratios displayed similarity in peaks with that of the

nanoparticle sample. However, the presence and intensity of the peaks differed between

58

the 1:1 control and nanoparticle spectra, indicating that not all the bound states present in

the nanoparticle are present in this sample, and if present, they can be in different

amounts (Table 3.3). On the other hand, the citrate states present in the 1:10 control bore

a remarkable similarity to those of the nanoparticle, although the intensity of the peaks

are different as observed in the one dimensional spectra. In summary, the COSY control

results do not conclusively confirm that the citrate peaks in the Cit-YF3 NPs are due

solely to binding to the nanoparticle, although the widely different one dimensional

spectra may indicate that some states are more favoured due to constraints imposed by

nanoparticle surface structure.

59

Figure 3.11. One dimensional and COSY spectra of the Cit-YF3 nanoparticles (0 ºC, pH ~ 8.5). The different states are labelled in capital letters, where each letter designates a particular state across all the COSY spectra.

60

Figure 3.12. One dimensional and COSY spectra of the 1:1, Y:Cit control (0 ºC, pH ~ 8.5). The different states are labelled in capital letters, where each letter designates a particular state across all the COSY spectra.

61

Figure 3.13. (a) An expansion of the one dimensional proton spectra, (b) the one dimensional and COSY spectra of the 1:10, Y:Cit control (0 ºC, pH ~ 8.5). Once again each letter designates a specific state across all the COSY spectra.

62

Table 3.3. Comparison of citrate states between samples State Comparison 1:1, Y:Cit ratio 1:10, Y:Cit ratio

*Common states C, G, K, L, O, Q C, D, G, K, M ψAbsent states A, B, E, F φOnly in NP D, I, M, P ςAbsent from NP A, B, E, F, H, J, N ΘUnknown H, I, J, L, N, O, P, Q

*States common between both the Cit-YF3 NP and the indicated control ψStates absent from both the Cit-YF3 NP and the indicated control φStates present only in the Cit-YF3 NP sample ςStates present only in the indicated control ΘState comparison impossible due to noise

3.6.3 Exchange and Population Calculations - Diffusion Studies

A pulsed field gradient stimulated echo (PFGSTE) experiment was conducted on

Cit-YF3 NPs to determine free and bound populations, and extract information about

exchange between populations. As most peaks in the one dimensional spectrum could

not be isolated, the diffusion coefficient of four different regions, denoted Peak Set 1 – 4

(Figure 3.14), were calculated. The diffusion decay is depicted in Figure 3.15, while a

logarithmic plot of the integrals in provided in Figure 3.16. The decays appear to be

monoexponential perhaps indicating fast exchange on the diffusion time scale (~ 100 ms).

Consequently, the observed diffusion coefficient (Dobs) is reduced due to binding to the

nanoparticle and will be smaller than that of free citrate, yet larger than that of the

nanoparticle. Fast exchange is described by the weighted equation,

boundboundfreefreeobs DpDpD += Equation 3.9

where pfree and pbound are the proportions of the free and bound populations respectively,

and Dfree and Dbound are the diffusion coefficients of the free and bound populations

respectively. As the diffusion coefficient of free citrate (~1.88 x 10-10 m2/s) is a lot larger

than the diffusion coefficient of the nanoparticle (~1.39 x 10-12 m2/s), Equation 3.9 can be

reduced to,

free

obs

freeD

Dp = Equation 3.10

63

The observed diffusion coefficients of Peak Set 1, 2, 3 and 4 were 101020.1 −× , 101017.1 −× ,

101047.1 −× and 101012.1 −× m2/s respectively. Using Equation 3.10, the pbound (1 - pfree)

was estimated to be 0.42, 0.43, 0.29 and 0.46 respectively. However, these values do not

reflect what is expected for a system in fast exchange, where the observed diffusion

coefficients of all Peak Sets should be identical. Furthermore, assuming fast exchange on

the diffusion time scale (~ 100 ms) is at odds with residence lifetimes determined from

selective inversion recovery experiments (presented later). Consequently, exchange

maybe in the intermediate regime on the diffusion time scale. Two methods are capable

of providing further information about this possibility. Firstly, a diffusion simulation

may be conducted using the exchange times obtained from the selective inversion

recovery experiment to see if the experimental curves match these modeled ones.

Secondly, diffusion experiments, at variable diffusion times, can clear up this situation as

they provide different times scales where diffusion may be fast on one time scale but not

so on the others. Figure 3.17 provides such an example where slow or fast exchange on

the diffusion time scales is governed by the length of the selected diffusion time. Since

exchange was found to be slow on the chemical shift time scale (~5 ms) but likely

intermediate on the diffusion time scale (100 ms), varying the diffusion times between 5

to 200 ms will provide sufficient information not only about exchange but also

conclusively proof that the observed citrate peaks, in the one dimensional spectrum, are

in fact bound to the nanoparticle.

64

Figure 3.14. The arbitrarily defined regions in the one dimensional spectrum.

Figure 3.15. The diffusion decay of the nanoparticle peaks. Experimental parameters in the PFGSTE experiment were: τ1 = 100 ms, τ2 = 8 ms, δ = 5 ms and ∆ = 108 ms.

65

4.00

4.50

5.00

5.50

6.00

6.50

7.00

7.50

0.0E+00 2.0E+09 4.0E+09 6.0E+09 8.0E+09 1.0E+10 1.2E+10 1.4E+10 1.6E+10 1.8E+10

k (-m2/s)

ln (

I)

Peak Set 1

Peak Set 2

Peak Set 3

Peak Set 4

Figure 3.16. A logarithmic plot of the integrals from the PFGSTE diffusion experiment conducted on Cit-YF3 nanoparticles, where the slope equals the diffusion coefficient.

Figure 3.17. An example of a simulated diffusion decay, where A and B are two states, D the diffusion coefficient, and τ2 the mixing period in the PFGSTE experiment (Note, that the mixing period was denoted as τ1 in this thesis, and that q2

τ2 is equal k in the diffusion experiments conducted herein). The rate constant kA and kB were assigned values of 10 and 66.6 Hz and the magnetizations, MAo and MBo, values of 0.4 and 0.6 (Chembiochem 2005, 6, 1550-1565 - Copyright Wiley-VCH Verlag GmbH & Co. KGaA. Reproduced with permission).

66

3.6.4 Exchange spectroscopy (EXSY)

Exchange spectroscopy (EXSY) was used to map the citrate exchange network in

the yttrium nanoparticle system. The appearance of new cross peaks (relative to the

COSY) indicated exchange between free citrate and at least four different states (Figure

3.18). These cross peaks increased in intensity as the mixing time was increased from

100 to 800 ms. Well resolved exchange cross peaks appeared for states C and D with

state M (refer to the COSY for assignments, Figure 3.11) and were used to form a three

site, two process exchange network,

where kCM and kDM are exchange constants when going from State C to State M and State

D to State M respectively, and kMC and kMD refer to exchange constants in the reverse

directions. As the first evidence of exchange between states D and M is visible at 100 ms,

this indicates that exchange is occurring at a sufficient rate to be observed on this time

scale. Evidence of exchange between states C and M are visible after 400 ms indicating

that this exchange process is slower. The other states undergoing exchange (indicated by

black arrows in the spectrum) were ignored because they were too convoluted and

consequently not amenable to selective inversion recovery analysis.

67

Figure 3.18. EXSY spectra at mixing times of 100 ms, 200 ms, 400 ms and 800 ms. The appearance of well resolved exchange peaks in (d) are indicated by the grey and black boxes, while other convoluted exchange peaks are indicated by black arrows.

68

3.6.5 Selective Inversion Recovery

A selective inversion recovery (SIR) experiment was used to extract rate constants

for the three-site, two process exchange system. Following construction of the exchange

network, the relevant rate equations were derived for the system.

[ ] [ ] [ ]MkCkdt

CdMCCM +−= Equation 3.11

[ ] [ ] [ ]MkDkdt

DdMDDM +−= Equation 3.12

[ ] [ ] [ ] [ ] [ ]DkCkMkMkdt

MdDMCMMDMC ++−−= Equation 3.13

Since the populations of the three sites are nonequivalent, all the above rate constants

need to be included. Equations 3.11 – 3.13 can be equivalently expressed in matrix form

as follows,

−−

−

−

=

M

D

C

kkkk

kk

kk

M

D

C

dt

d

MDMCDMCM

MDDM

MCCM

0

0

Equation 3.14

where C, D and M are the observed time dependent magnetizations.

According to the principle of detailed balance, the forward and reverse rates

between two sites are equal, leading to the equations,

[ ] [ ][ ][ ]C

M

k

kK

MkCk

MC

CM

C

MCCM

==

=

Equation 3.15

and,

[ ] [ ][ ][ ]D

M

k

kK

MkDk

MD

DM

D

MDDM

==

=

Equation 3.16

The two rate constants, kMC and kMD, can be substituted into the matrix (Equation 3.14) to

yield,

[ ][ ]

[ ][ ]

[ ][ ]

[ ][ ]

−−

−

−

=

M

D

C

M

Dk

M

Ckkk

M

Dkk

M

Ckk

M

D

C

dt

d

MDMC

DMCM

MD

DM

CM

CM

0

0

Equation 3.17

69

Note, that although the state populations are given as equilibrium concentrations, they

can equivalently be represented as final or infinity magnetizations. The kinetic matrix

(K), in Equation 3.14, can thus be simplified to two individual rate processes,

−

−

=

∞

∞

∞

∞

M

C

M

C

kR CM

01

000

01

Equation 3.18

and,

−

−=

∞

∞

∞

∞

M

D

M

DkS DM

10

10

000

Equation 3.19

where K = R + S. Including the relaxing matrix, J, completes the description of the

factors that affect the change in magnetization seen in the SIR experiment.

=

M

D

C

T

T

T

J

1

1

1

100

01

0

001

Equation 3.20

The selective inversion recovery experiment, conducted on the Cit-YF3 NP

aqueous sample, yielded an arrayed spectral series (Figure 3.19). The integrals of states

C and D were doubled to adjust for their corresponding doublets contained within the

collection of peaks centered at 1.8 ppm. It was assumed that the integral of M was equal

to the area between 2.35 – 1.9 ppm despite the knowledge that there were bound states

buried within this range. However, this assumption is likely reasonable, within error,

given that the intensities of the free state in the COSY experiments dominates over any

bound state. The interdependence of the three decays necessitates that all equations be

solved simultaneously, and so, the CIFIT program116 was used to fit the data. Two files,

one containing the mechanism and the other the data, were loaded into the program and

the relaxation times and exchange rates were alternatively varied or fixed. The initial and

70

infinity magnetizations were held constant, despite the recommendations in the CIFIT

manual,117 because they were otherwise found to result in poor fits. Figure 3.20 shows

the fits for the decays. The rate constants kCM, kDM, kMC and kMD were found to be 0.75,

0.82, 0.029 and 0.052 Hz respectively. Consequently, assuming a three site system, a

citrate molecule would be expected to reside on the surface of the nanoparticle for about

one second while in aqueous solution for about twelve seconds. The latter is likely an

upper bound as exchange with bound states other than C or D were not taken into account.

71

Figure 3.19. One-dimensional selective inversion recovery of a nanoparticle sample. The peaks centered at 2.25 ppm (State M and other bound states) were inverted and twenty data points were collected with exchange times of 1, 10, 30, 70, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1250, 1500, 1750, 2000, 2500 and 5000 ms. Although the decay of state C and D is not evident in the spectra, Figure 3.20 demonstrates the time dependence of these peaks.

0.0000

0.0100

0.0200

0.0300

0.0400

0.0500

0.0600

0.0700

0.0800

0.0900

0.1000

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Time (s)

Sta

te C

an

d D

Mag

nit

iza

tio

ns

-1.000

-0.800

-0.600

-0.400

-0.200

0.000

0.200

0.400

0.600

0.800

1.000

Sta

te M

Mag

nit

iza

tio

n

State C

State D

State M

Figure 3.20. Fits of the changes in magnetization, for the three state system, accomplished by using the CIFIT fitting program.

72

3.6.6 Verification of PAA ligand exchange

As the PAA was titrated into an aqueous dispersion of Cit-GdF3 nanoparticles

during PAA-GdF3 NP synthesis, it was necessary to verify that ligand exchange actually

occurred. This was accomplished using diffusion studies on Cit-YF3 and PAA-YF3

nanoparticles. The first sample was used as a control to verify citrate binding, while the

second was used to demonstrate the displacement of citrate by PAA. As before,

monoexponential decays were observed for all signals, and hence the observed diffusion

coefficients were taken to be weighted averages of their free and bound counterparts. A

sample decay is provided for the PAA-YF3 NPs (Figure 3.21) along with the fit (Figure

3.22) used to calculate the diffusion coefficients. For the Cit-YF3 NP sample, citrate was

found to be bound to the nanoparticle as its diffusion coefficient at 101075.3 −× m2/s was

found to be smaller that that of the sodium citrate control, 101058.4 −× m2/s. However, in

the PAA-YF3 nanoparticles it was completely displaced by PAA (Table 3.4). This is

indicated by the virtually identical diffusion coefficients of the sodium citrate control and

that of citrate in the PAA-YF3 NP sample ( 101053.4 −× m2/s). Further proof is provided

by reversion of the bound citrate peaks to the four familiar, strong coupling resonances of

free citrate. In contrast, PAA possessed an observed diffusion coefficient of 101025.1 −×

m2/s which was slower than that for the free PAA control, 101041.1 −× m2/s. These results

indicate that whereas some citrate was initially bound to Cit-YF3 NPs, none were found

to be attached to PAA-YF3 NPs. Instead, this ligand was replaced by PAA. It should be

noted that although bound populations were calculated, these may be underestimated, due

to differential signal filtration of broad and narrow magnetizations in the PFGSTE

experiment. To obtain accurate values, the τ1 and τ2 times in the experiment need to be

varied and the observed diffusion coefficients extrapolated back to zero time.

73

Figure 3.21. The diffusion decays observed in the PAA25-YF3 NP sample (25 ºC). Experimental parameters in the PFGSTE experiment were: τ1 = 250 ms, τ2 = 69 ms, δ = 5 ms and ∆ = 319 ms.

1.00

2.00

3.00

4.00

5.00

6.00

7.00

0.0E+00 2.0E+09 4.0E+09 6.0E+09 8.0E+09 1.0E+10 1.2E+10

k (-m2/s)

ln (

I)

Water

Y/Citrate

Y/PAA

Figure 3.22 A logarithmic plot of the integrals from the PFGSTE diffusion experiment conducted on PAA25-YF3 nanoparticles, where the slope provides a direct indication of the diffusion coefficient.

74

Table 3.4 Relevant diffusion information for a PAA25-YF3 NP sample and controls

NMR Sample NMR signal Diffusion coefficient

(m2/s) Hydrodynamic

radius (nm) Pfree Pbound

Sodium Citrate Free citrate 4.58E-10 0.52

Poly(acrylic acid) Free PAA 1.41E-10 1.61

Cit-YF3 NPs Obvs. citrate 3.75E-10 0.61 0.85 0.15

Obvs. citrate 4.53E-10 0.51 1.01 0.00 PAA25-YF3 NPs

Obvs. PAA 1.25E-10 1.86 0.87 0.13

75

4 Conclusion

Lanthanide trifluoride nanoparticles were synthesized for use as MRI contrast

agents with the ultimate goal of cancer detection and therapy in mind. One such

synthesis, reproduced from literature, involved the production of water-soluble, Cit-GdF3

nanoparticles. As in vivo stability and toxicity is important, ligand affinity and exchange

were characterized though use of a combination of NMR experiments on Cit-YF3 mimics.

One dimensional and COSY spectra indicated a number of different citrate binding

orientations. Diffusion experiments suggested that citrate was in intermediate exchange

on the diffusion time scale (~ 100 ms) and that between 29 – 46 % of citrate was bound to

the nanoparticle surface at 0 ºC. Selective inversion recovery experiments indicated

residence lifetimes of one and twelve seconds for bound and free citrate respectively.

The relatively small residence time of citrate on the nanoparticle surface provided insight

into the low colloidal stability in aqueous media, where visible precipitate was observed

in a 10 mg/ml sample at 0 ºC after only a few hours.

Low stability and relaxivity of Cit-GdF3 nanoparticles led to the synthesis of

PAA-GdF3 nanoparticles which displayed relaxivities six times greater than commercial

chelates. However, the synthesis turned out to be irreproducible. In an attempt to re-

generate these high relaxivities, a new two-step, ligand exchange protocol was developed

where Cit-YF3 NP were first synthesized followed by exchange of citrate for poly(acrylic

acid). This latter step was verified using diffusion studies, which indicated the complete

displacement of citrate by PAA. The main strength of this protocol is that any ligand,

that has a higher affinity than citrate, can be used to coat the nanoparticle.

Due to biodistribution considerations, several variations of the synthesis were

conducted to exert control over nanoparticle size and polydispersity. Results indicated

that although control of average size is easy, achieving monodispersity is much harder.

Nevertheless, one synthesis potentially indicated that it may be possible to set reaction

conditions such that nucleation and growth processes occur separately thereby improving

monodispersity.

Future short term research term goals entail following up experiments on more

monodisperse synthesis, and concluding citrate binding and exchange studies (especially

76

variation of diffusion time in diffusion experiments). Longer ones may involve

improving colloidal stability through use of a poly(phosphonate) or a polymer net.

77

5 Supporting Data

5.1 Supporting Data (for Figure 3.11)

Table SD1. COSY peak assignments for Cit-YF3 NPs

StateChemical Shift

(ppm)Chemical shift

range (ppm) AB/AX system AssignmentTentative

AssignmentC 2.675 2.693-2.666 AB 2.675C 2.648 2.666-2.630 AB 2.652D 2.586 2.604-2.577 Almost AB 2.588D 2.559 2.577-2.532 Almost AB 2.567G 2.344 2.349G 2.326 2.33K 2.29 2.298K 2.272 2.277L 2.254 2.290-2.165 2.257L 2.228 2.290-2.165 2.228M 2.192 2.210-2.183 2.196M 2.174 2.183-2.156 2.171M 2.084 2.111-2.075 2.088O 2.084 2.088M 2.066 2.075-2.039 2.064O 2.066 2.064PQ 2.066 2.064PQ 2.048 2.048L 2.048 2.064-1.995 2.048L 2.022 2.064-1.995 2.022O 1.977 1.978O 1.95 1.946PQ 1.833 1.835D 1.826 1.860-1.771 Almost AB 1.824PQ 1.807 1.813D 1.798 1.860-1.771 Almost AB 1.803C 1.78 1.816-1.735 AB 1.787PQ 1.78 1.787C 1.753 1.816-1.735 AB 1.762G 1.735 1.739K 1.735 1.739G 1.717 1.719K 1.708 1.719NOTE: Only chemical shifts used in the assignments (by comparing those from the COSY with those in the 1D spectrum Only visible peaks could be assigned. Some of the minor states did not exhibit any significant peaks, and thus could not be assigned.

78

5.2 Supporting Data (for Figure 3.12)

Table SD2. COSY peak assignments for Y:Cit 1:1 control

State COSY Chemical

Shift (ppm) 1D 1H

Assignment Tentative 1D 1H

Assignment Notes C 2.657 2.657 1D spectrum referenced to 2.657 C 2.639 2.635 E 2.460 2.457 E 2.433 2.429 F 2.407 2.405 F 2.389 2.384 G 2.362 Faint H 2.353 2.347 One broad peak G 2.344 2.347 Faint H 2.335 2.325 One broad peak I 2.317 2.325 Problem with right peak K 2.308 2.310 Problem with left peak I 2.290 2.280 Problem with right peak K 2.290 2.280 Problem with left peak L 2.236 2.224 H 2.210 2.199 One broad peak L 2.210 2.199 N 2.210 2.199 Problem with right peak N 2.201 2.199 Problem with right peak H 2.192 2.180 One broad peak N 2.192 2.180 Problem with right peak O 2.102 2.099 E 2.084 2.072 O 2.075 2.072 P 2.075 2.072 I 2.066 2.072 Problem with right peak L 2.066 2.072 E 2.057 2.051 P 2.057 2.051 L 2.039 2.035 I 2.030 2.035 Problem with right peak N 2.004 1.988 Problem with right peak O 1.995 1.988 O 1.968 1.965 P 1.825 1.820 C 1.807 1.802 P 1.807 1.802 C 1.789 1.782 G 1.744 1.747 Faint K 1.744 1.747 Problem with left peak G 1.726 1.724 Faint K 1.726 1.724 Problem with left peak F 1.663 1.662 F 1.645 1.640

79

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