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Spectroscopic Analysis of Neurotransmitters: ATheoretical and Experimental Raman StudyMatthew AlonzoUniversity of Texas at El Paso, alonzo_matthew@yahoo.com

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SPECTROSCOPIC ANALYSIS OF NEUROTRANSMITTERS:

A THEORETICAL AND EXPERIMENTAL RAMAN STUDY

MATTHEW ALONZO

Master’s Program in Physics

APPROVED:

Felicia S. Manciu, Ph.D., Chair

Marian Manciu, Ph.D.

Giulio Francia, Ph.D.

Deidra R. Hodges, Ph.D.

Charles Ambler, Ph.D. Dean of the Graduate School

.

Copyright ©

by

Matthew Alonzo

2017

Dedication

I dedicate this work to my mother, father, brothers, and Luz for being the most significant people

in my life. Thank you for your unwavering support, faith, and unconditional love.

I love you.

SPECTROSCOPIC ANALYSIS OF NEUROTRANSMITTERS:

A THEORETICAL AND EXPERIMENTAL RAMAN STUDY

by

MATTHEW ALONZO, B.S.

THESIS

Presented to the Faculty of the Graduate School of

The University of Texas at El Paso

in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

Department of Physics

THE UNIVERSITY OF TEXAS AT EL PASO

May 2017

v

Acknowledgements

I would like to thank the Gates Millennium Scholarship Program funded by the Bill and

Melinda Gates Foundation for supporting me in my educational pursuits. I am humbled and

grateful for your belief in me to succeed. I am also appreciative and greatly indebted to my advisor

Dr. Felicia S. Manciu and a great professor of mine, Dr. Marian Manciu. They both have an

incredible work ethic and sincerely care about the success and well-being of their students. I would

also like to acknowledge the Department of Physics faculty, staff, and graduate student population

at the University of Texas at El Paso (UTEP) for being great mentors, peers, and for making the

ascertainment of this degree an enjoyable experience.

vi

Abstract

Surface-enhanced Raman spectroscopy (SERS) was applied to investigate the feasibility

in the detection and monitoring of the dopamine (DA) neurotransmitter adsorbed onto silver

nanoparticles (Ag NPs) at 10-11 molar, a concentration far below physiological levels. In addition,

density functional theory (DFT) calculations were obtained with the Gaussian-09 analytical suite

software to generate the theoretical molecular configuration of DA in its neutral, cationic, anionic,

and dopaminequinone states for the conversion of computer-simulated Raman spectra.

Comparison of theoretical and experimental results show good agreement and imply the presence

of dopamine in all of its molecular forms in the experimental setting. The dominant dopamine

Raman bands at 750 cm-1 and 795 cm-1 suggest the adsorption of dopaminequinone onto the silver

nanoparticle surface. The results of this experiment give good insight into the applicability of using

Raman spectroscopy for the biodetection of neurotransmitters.

vii

Table of Contents

Dedication ................................................................................................................................ iii

Acknowledgements ....................................................................................................................v

Abstract .................................................................................................................................... vi

Table of Contents .................................................................................................................... vii

List of Tables ........................................................................................................................... ix

List of Figures ............................................................................................................................x

Chapter 1: Background and Motivation .....................................................................................11.1 Experimental Motivation ............................................................................................11.2 Biomedical Optics .......................................................................................................21.3 Dopamine ....................................................................................................................31.4 Currently Developed Methods of Dopamine Detection .............................................61.5 Noble Metal Nanoparticles and SERS Substrates ......................................................8

Chapter 2: Methodology ............................................................................................................92.1 The Theory of Raman Scattering and Modern Raman Spectroscopy .........................92.2 Classical Electromagnetic Treatment of the Raman Effect ......................................102.3 Quantum Mechanical Model for Raman Scattering .................................................122.4 Confocal Raman Spectroscopy .................................................................................132.5 Surface-Enhanced Raman Spectroscopy ..................................................................142.6 Competing Physical Processes ..................................................................................152.7 Density Functional Theory .......................................................................................162.8 Sample Preparation and Instrumentation ..................................................................172.9 Density Functional Quantum Calculations ...............................................................19

Chapter 3: Results ....................................................................................................................213.1 Experimental and Computational Raman Data .........................................................213.2 Conclusions ...............................................................................................................31

viii

References ................................................................................................................................33

Vita .........................................................................................................................................39

ix

List of Tables

Table 2.1 The reagents used to synthesize silver nanoparticles are listed in addition to their initial

concentrations. .............................................................................................................................. 17

Table 3.1 Comparison of theoretically simulated and experimentally measured Raman vibrational

assignments of DA. ....................................................................................................................... 23

x

List of Figures

Figure 1.1 The molecular structure of dopamine (C5H11NO2) shows a catechol structure attached

to an ethylamine. ............................................................................................................................. 3

Figure 1.2 The primary synthesis pathway of dopamine begins with the conversion from tyrosine

to L-DOPA with tyrosine hydroxylase. .......................................................................................... 4

Figure 2.1 The Rayleigh, Stokes, and anti-Stokes processes show the energy interaction of a

quantum of light with the vibrational energy levels of a molecule. ............................................. 13

Figure 2.2 The Raman apparatus used in the experiment is displayed alongside the software used

for data acquisition. ...................................................................................................................... 19

Figure 3.1 The Raman spectra for DA0 from computational analysis (above) is compared with

experimentally derived bands (below) for solid dopamine powder. ............................................. 22

Figure 3.2 The redox conversion of dopamine to dopaminequinone. ......................................... 24

Figure 3.3 The optimized structural representation of dopaminquinone is shown near a silver dimer

(above) along with the simulated Raman spectrum of the molecule in a SERS environment

(middle) and experimentally measured data (below). .................................................................. 25

Figure 3.4 Dopamine in the presence of a silver dimer is presented. .......................................... 27

Figure 3.5 The anionic molecular form of DA- is shown (above) along with the experimentally

measured (middle) and theoretically calculated (below) SERS Raman spectra. ......................... 29

Figure 3.6 The cationic structural representation of DA+ is shown (above) along with the

experimental (middle) and simulated (below) SERS Raman spectra. .......................................... 30

1

Chapter 1: Background and Motivation

1.1 Experimental Motivation

In today’s modern society, detection of trace amounts of neurotransmitters has become

significant in diagnostic and medical applications. The ability to precisely measure the presence

of diminutive concentrations of neurotransmitters and other biological analytes is of great

importance in the advancement of medical diagnosis procedures, pharmaceutical discovery,

homeland security applications, and environmental and industrial monitoring [1, 2]. Given the

wide range of physiological and pathophysiological effects of dopamine, precise and accurate

measurement of dopamine and its metabolites in biological systems is of great clinical importance

[3]. By taking advantage of the interaction electromagnetic radiation has with matter, the following

investigation explores the feasibility of using silver nanoparticle (Ag NPs) substrates in

combination with a Raman system to detect and monitor concentrations of dopamine (DA) below

physiological levels. Specifically, the technique of surface-enhanced Raman spectroscopy (SERS)

will be applied in this work along with computational density functional theory (DFT) calculations

for comparison and an in-depth comprehension of the physiochemical process of adsorption onto

metallic surfaces. Experimental findings also have biomedical engineering applications in the

invention and development of biosensor technology as SERS provides rapid, accurate, reliable,

and real-time information for analyte detection [1, 4].

Although the benefits in detecting biological components are many, there are some

drawbacks that must be overcome. Analytes including drugs, single molecules, and viruses are

especially difficult to detect due to their small molecular weights and concentrations in the human

body [2]. On the other hand, larger molecules such as cells, bacteria, and spores are easier to

analyze because they can be stained with colored or florescent dyes. Such investigations, however,

usually lead to the destruction of the specimen and does not permit the continuous analysis of the

2

sample [2]. Due to the difficulty in detecting samples through their intrinsic physical properties,

biological research has typically attached a “label” to an analyte; it is designed to indirectly

indicate the presence of the substance it is attached to through a recognizable color or its ability to

produce photons of a particular wavelength [2]. The advantage of using nanoparticles is the

amplification gained in the signal from their interaction with electromagnetic radiation [5].

1.2 Biomedical Optics

All molecules can interact with electromagnetic fields that permeate through them because

they contain atomic nuclei and electrons in various orbital states [2]. Fundamental electromagnetic

theory proposes that any electrically charged material will feel a Coulombic force when exposed

to an electric field, such as the one a light beam generates [2, 6, 7, 8]. Depending on the size, shape,

and orientation of the molecule in the electric field, molecules may become polarized and induce

an electric dipole moment [2, 6]. The electric field generated by light is oscillatory, therefore the

Coulombic force experienced by charged matter will be time-varying and produce a polarization

current [2, 7, 8, 9].

The foundation of an optical biosensor’s capability to detect biological analytes stems from

the fact that all biological materials have a dielectric permittivity greater than that of air and water

[2, 9]. Light travels through free space at the speed of light [8]. A vaccuum has a permittivity

defined as e0 = 8.85 x 10-12 F/m [7, 8, 9]. When a dielectric material is introduced, its permittivity

𝜀 is given by

𝜀 = 𝜀#𝜀$,

where 𝜀$ is the relative permittivity of the dielectric constant of the material [2, 9]. The relative

permittivity of the dielectric is mathematically related to the polarizability of the material through

the expression

𝜀$ = 1 + 𝛼

3

where 𝛼 is the polarizability of the molceule [2, 6, 8]. In terms of the dielectric’s refractive index

n, the relative permittivity is 𝜀$ = 𝛼 [2].

1.3 Dopamine

Dopamine (DA) is a potent neuromodulator in the nervous system that influences a variety

of behaviors and is involved in several neurologic diseases [10]. The molecule is widely present

in the central nervous system, where it controls several aspects the brain circuitry [3]. It affects

the sleep-wake cycle, is critical for motivated behaviors, reward-learning, movement, and

cognitive processing [11]. In addition, it plays a role in attention span, neuronal plasticity, and

memory [3]. The structure of dopamine reveals two important ionizable functional groups that

may participate in acid-base equilibrium [12]. A catechol structure consisting of a benzene ring

with two hydroxyl substituents is covalently bonded to an ethylamine as depicted in Figure 1.1.

The nitrogenous portion of the molecule acts as an organic base due to its high affinity to be

protonated [5, 12, 13]. Conversely, the two hydroxyl functional groups can be readily deprotonated

and act as proton donors [5, 12, 14]. Depending on the pH of its local environment, DA may exist

as a complex mixture of zwitterionic, charged, and neutral species; theoretical results indicate that

at physiological pH, dopamine exists in its cationic (DA+) form [12]. Furthermore, physiological

and pathophysiological levels in vivo are of the order of about 10-7 M [12].

Figure 1.1 The molecular structure of dopamine (C5H11NO2) shows a catechol structure attached

to an ethylamine.

4

Tyrosine, an amino acid copious in dietary proteins, is usually considered the starting point

in the biosynthesis of dopamine [15]. The amino acid is also naturally manufactured in the body

through the conversion of phenylalanine, an essential amino acid, in both the dopamine neurons

and liver through an enzymatic reaction [15, 16]. When tyrosine has reached the circulatory

system, it gets transported to dopaminergic neurons where it is converted to

dihydroxyphenylalanine (L-DOPA) by tyrosine hydroxylase [11, 15, 17]. The enzyme’s purpose

is to augment a hydroxyl group to the tyrosine molecule [17]. Next, aromatic amino acid

decarboxylase (AADC) converts L-DOPA to DA by eliminating a carboxyl group [15, 17]. A

general schematic of the primary biosynthetic pathway of dopamine can be referenced in Figure

1.2.

Figure 1.2 The primary synthesis pathway of dopamine begins with the conversion from tyrosine

to L-DOPA with tyrosine hydroxylase.

5

Dopamine has long been hypothesized to have a key role in addiction [18]. Its role mediates

motivated responding and is a psychostimulant of many narcotics [19]. Drug addiction is defined

by the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) as a chronically relapsing

disorder characterized by compulsion to seek or take a drug, loss of control in limiting intake, and

the emergence of a negative emotional state that includes dysphoria, anxiety, and irritability [20].

This reflects a motivational withdrawal syndrome when access to the drug is prevented. Drugs

such as cocaine, methamphetamine, nicotine, heroin and alcohol are not the only sources of

addiction. Other behaviors, such as sexual intercourse and the consumption of food release

dopamine into the body [21]. The rewarding properties of drugs and other behaviors do not

necessarily consist of sheer sensations of pleasure like the “high” or “rush” of typical drugs. It can

take milder forms of happiness, like tension relief, reduction of fatigue, increased arousal,

improvement of performance, etc. These positive actions can explain why drugs and other

behaviors are abused.

Dopamine has been the focus of research on the neural mechanisms of addiction. Interest

in the neurotransmitter and addiction occurred when scientists discovered that rats would

electrically self-stimulate their brains [22]. It was here that a link between addictive drug action,

DA systems, and notions of reward were established. Initially it was suggested that DA release

mediated the pleasurable, or hedonic, aspects of reward, whether natural, such as food or sex, or

drug rewards.

Parkinson’s disease, first described by James Parkinson in 1817, is a neurological disorder

in which degeneration of nigrostriatal dopaminergic neurons leads to a dramatic reduction of

dopamine levels in the striatum [23]. Behaviorally, the disease is characterized primarily by a

progressive loss of voluntary motor functions along with tremors. Parkinson’s disease is one of

the most common neurological disorders and is more prevalent in those over 65 years of age. Its

cause is believed to be a malfunction of Parkin and other neuroprotective genes in addition to being

6

exposed to environmental toxins. Although dopamine reduction is cardinal in aging human brains,

the loss of dopamine is much more sever in Parkinson’s patients [24].

Although it affects a small portion of the population, with the disease affecting males

almost twice as much as females, schizophrenia is the best studied of all neuropsychological

disorders involving dopamine, partly because of the strange thought patterns and delusions

associated with it and the fact that many great minds have at least temporarily succumbed to it,

including physicists Isaac Newton and Michael Faraday [17]. The National Institute of Mental

Health defines schizophrenia as a chronic and severe mental disorder that affects how a person

thinks, feels, and behaves [25]. Positive symptoms, or psychotic behaviors generally not present

in healthy people, include hallucinations, delusions, and thought disorders [25, 26]. Negative

symptoms, behaviors that are disruptions to normal emotions, include reduced feelings, reduced

speaking, lack of motivation, and social withdrawl [25, 26].

1.4 Currently Developed Methods of Dopamine Detection

Essential for monitoring diseases and human health, the accurate analysis of

neurotransmitter concentrations in biological systems has become an increasingly important

ability. Scientific disciplines such as biology, chemistry, medicine, and the like have developed

and used methods to identify and quantify trace amounts of neurotransmitter analytes.

Amperometry is among one of the handful of established analytical techniques. The

procedure involves detection of ions in a solution based on electric current or changes in electric

current. In one study, scientists fabricated an implantable enzyme-based biosensor and applied it

to in vivo measurements of dopamine evoked from stimulation rat brains [3]. The biosensor was

engineered using the natural polymer polysaccharide chitosan and mixed it with ceria/titania-based

metal oxide nanoparticles for the enhanced selectivity for dopamine. It was found that the sensor

can continuously measure levels of dopamine in the nanomolar range at a low applied potential of

-150 mV. The detection limit of the sensor was reported to be 1.0 nM. In another amperometric

7

study, detection of dopamine was achieved by gold nanoparticles molecularly imprinted on a

polymer. Like the previous investigation, the procedure was only able to detect trace amounts of

dopamine in the nanomolar range [27].

Another developed method utilizes chromatography. Chromatography is a procedure that

separates components of a mixture in a solution, suspension, or vapor through a medium in which

components move at different rates. An example of such a study involved the highly selective and

sensitive column liquid chromatographic method for fluorescence determination of various

neurotransmitters [28]. In the experiment, chromatography allowed for the simultaneous

determination of multiple substances at once.

Fluorescence and magnetic resonance spectroscopy (MRS) are also among the handful of

developed analytical techniques for bioanalysis. The first is a biophysical method that exploits the

phenomenon of fluorescence to examine and analyze ligand-receptor interactions, while the latter

is a non-invasive ionizing-radiation-free analytical technique that uses signals from hydrogen

protons to determine relative concentrations of relevant neurochemistry [29]. The limits of the

analytical techniques described above include detection times that are extensive in comparison

with characteristic physiological processes. Additionally, some can only test one analyte at a time

in the sea of commonly found neurotransmitters in vitro. Some fluorophores used in fluorescence

imaging can also be toxic to their host [30].

The most common method of dopamine detection relies on voltammetry. With

voltammetry, a voltage is continuously raised and lowered in a solution. When the voltage is at a

particular range, the analyte under investigation will continuously undergo a series of redox

reactions, thereby producing a current. This small induced current can be plotted against the

potential to produce a unique voltage vs. current graph for each particular molecule. The

advantages of this technique is the applicability to use it in vivo and provides real-time data. It’s

limitation, however, only allows the detection of neurotransmitters to the nanomolar range [31].

8

1.5 Noble Metal Nanoparticles and SERS Substrates

Since the advent of the SERS technique in 1974, many advances have been achieved in

developing new strategies for designing high-performance, sensitive, and reproducible SERS

substrates. As a result, reliable materials have been fabricated ranging from noble metals,

semiconductors, and hybrid composite materials. For medical purposes, however, most of the

surface-enhanced Raman spectroscopy substrates still rely on noble metal nanostructures and their

derivatives. Nanostructures can be considered as nanoantenae for transmitting and enhancing

Raman scattered light [32]. The ability to enhance optical electromagnetic fields depends on the

morphology and the material used in the metal nanostructures, in addition to the excitation

wavelength [32].

Colloidal solutions of noble metals, especially those of gold and silver, have extensively

been studied because of their intense absorption band in the visible region, commonly known as

surface plasmon absorption. This band is attributed to the collective oscillation of the electron gas

in the particles with a change in the electron density at the surface [12].

9

Chapter 2: Methodology

2.1 The Theory of Raman Scattering and Modern Raman Spectroscopy

Light, the physical entity that has the dual nature of an electromagnetic wave and a quantum

mechanical particle, interacts with matter in three ways [33, 34]. A light beam observes a certain

probability of either getting absorbed, scattered, or transmitted through a material [9]. When light

of frequency u0 is scattered off a molecule in an inelastic fashion, an experimenter can detect what

is known as the Raman effect; the phenomenon manifests itself as a shift in the frequency (color)

of the incident light beam [33, 34, 35, 36]. The change in the wavelength occurs as light interacts

with the vibrations of the molecular bonds and the polarizable electron cloud present in the

material’s atomic structure [33]. Although first experimentally observed by the Indian scientist

Chandrasekhara Venkata Raman in 1928, the inelastic scattering of light was theorized by Adolf

Smekal in 1923 [35, 36]. The Raman effect is modernly used as an analytical spectroscopy

technique that can identify materials based upon a Raman spectrum that is unique for every

chemical that is present [33]. In Raman’s experiment, a telescope was used to focus sunlight on a

purified liquid to which then a second lens collected the scattered radiation. Through the utilization

of a network of optical fibers, it was shown that there existed radiation of an altered frequency that

was scattered [33, 35].

In Raman spectroscopy experiments, such as the one presented in this investigation,

scattered photons can be differentiated in accordance to how their energy changes as it interacts

with a sample. A monochromatic light beam is therefore utilized so that all incident photons are

of the same frequency and can be compared to the incident radiation [33]. In the event the scattered

light remains at the initial frequency u0, the photons have undergone Rayleigh scattering; it is the

dominating physical process [34]. Although it occurs most often, Rayleigh scattering gives no

feasible information about the molecular composition of a sample. There are, however, two other

Raman processes that can give viable information about the substance the light particles interact

10

with. Stokes scattering occurs when an incident photon transfers some of its energy into a

constituent molecule of the piece of matter under investigation. As a result, the scattered light is

left with a depreciable frequency that can be detected [34]. On the other hand, anti-Stokes

scattering occurs when the incident photon robs a molecule of some of its vibrational energy [34].

The gain in the photon’s energy can consequentially be detected in scattered light with a higher

frequency. Unfortunately, only about one in a million photons interact in either the Stokes or anti-

Stokes fashion, resulting in weak Raman signals from the two information-bearing interactions

[33].

Scattered Raman light is analyzed by a computer software and then graphically displayed

on a characteristic Raman spectrum; it gives both qualitative and quantitative information about

the composition of a sample [37]. Wavenumbers, a value reciprocal to a photon’s wavelength, are

usually used as the unit to measure shifts in spectral bands. Blue shifted bands, or bands where

light gains energy, are called anti-Stokes bands. These spectral markers move toward a higher

wavenumber [34]. In the Stokes regime, bands are red shifted by moving toward a lower

wavenumber [34]. The peak intensities of the spectral bands give information about the amount of

a specific compound present in the area surveyed. Moreover, the translation of peaks provides

information about the stresses and strains in a molecule [37].

2.2 Classical Electromagnetic Treatment of the Raman Effect

Without taking into consideration the quantization of vibrational energy states, a target

sample is classically described as a collection of molecules executing simple harmonic vibrations

about their chemical bonds. When these molecules are placed within a small electric field e, the

electron cloud in the material gets displaced relative to the nuclei [15, 33, 34]. When this occurs,

the sample is momentarily polarized, and consequently an electric dipole moment µ is induced.

The polarizability a of the electron cloud describes the ease at which an electron cloud can be

distorted [8]. Mathematically, this process is expressed

11

𝜇 = 𝛼𝜀.

Due to the electromagnetic radiation nature of light, it generates an oscillating electric field in

space as a function of time t represented by

𝜀 = 𝜀#cos(2𝜋𝜐#𝑡).

Substituting the above expression into that of the induced dipole moment, one arrives at the

following equation:

𝜇 = 𝛼𝜀#cos(2𝜋𝜐#𝑡).

From this result, it is easy to see that the induced dipole moment will oscillate at the same

frequency as the light that produced it. The dipole moment can therefore also emit or scatter

radiation of the same frequency producing the effect of Rayleigh scattering.

If we now consider a diatomic molecule executing simple harmonic vibrations of frequency

uu, we can denote a generalized coordinate q along the axis of vibration to be of the form

𝑞 = 𝑞#cos(2𝜋𝜐4𝑡).

If the polarizability of the electron cloud changes during the vibration, then for a small amplitude

of vibration, the polarizability can be written as the Taylor expansion

It is paramount to mention that the polarizability of the molecule must change during a vibration

if the vibration is to be Raman active. In other words, ( 56578)# ≠ 0. Combining these mathematical

models into a single expression for the dipole moment, we finally get 𝜇 = 𝛼#𝐹# cos 2𝜋𝑣#𝑡 + ( 56

578)#

=>7>?[cos 2𝜋 𝑣# + 𝑣A 𝑡 + 𝑐𝑜𝑠2𝜋 𝑣# − 𝑣A 𝑡],

where the first term in the expression describes the Rayleigh scattering process as before. In

addition, two new terms introduce themselves into the oscillating dipole expression. The second

term describes an increase in frequency of the incident electromagnetic radiation. This term

therefore mathematically models the Stokes scattering process. In a similar fashion, the last term

shows a decrease in frequency of the incident light, therefore characterizing the anti-Stokes

scattering activity [33, 34].

12

2.3 Quantum Mechanical Model for Raman Scattering

The photon is the quantum mechanical manifestation of light as a particle [9]. Unlike the

classical electromagnetic model, the quantum treatment of the Raman scattering process

recognizes the quantized vibrational energy states of a molecule. Linear molecules exhibit 3N-5

normal vibrational modes, where N is the number of atoms in the molecule. A non-linear molecule

will have 3N-6 modes. The quantized vibrational energy for a molecule depends on the frequency

of the vibration f and the quantum vibrational number v as in the following relation:

𝐸A = ℎ𝑓 𝑣 + J?

.

The Rayleigh process occurs when a molecule absorbs a photon’s energy, gets promoted

to a virtual quantum state, then returns to its initial vibrational state by reemitting a photon. The

re-emitted photon will remain at the same frequency. When a molecule is in a quantum vibrational

state, it also can absorb a photon, robbing it of some of its energy. The molecule will consequently

get promoted to a higher vibrational energy state upon re-emitting a photon of smaller frequency.

This is the quantum mechanical mechanism of Stokes scattering. On the other hand, when a

molecule is already in a higher vibrational state, it has the ability to transfer some of its vibrational

energy to the photon. After a photon is re-emitted, the molecule is consequently left at a lower

vibrational state than it had begun with. Typically, a molecule will be in its ground state and will

therefore not have any energy to transfer to the photon. For this reason, Anti-Stokes scattering

bands will give out a weak signal. [34] The quantum mechanical transitions are summarized in

Illustration 2.1 below.

13

Figure 2.1 The Rayleigh, Stokes, and anti-Stokes processes show the energy interaction of a

quantum of light with the vibrational energy levels of a molecule. [34]

2.4 Confocal Raman Spectroscopy

There are various techniques that enhance the output from a Raman spectroscopy

experiment. Confocal Raman imaging utilizes a Raman microscope to scan a sample point by point

to produce an image with a complete spectrum for every pixel. This process, often referred to as

hyperspectral imaging, collects thousands of spectra for a single sample. A software can then

analyze the spectra to produce an image that models the spatial distribution of the chemical

constituents of a material.

Normal microscopes gather information about a material by illuminating the whole sample

with light. Using a confocal microscope is beneficial to Raman imaging because it measures the

14

light from a small volume of the sample at a given time. This is accomplished by focusing a light

source on the specimen using an objective lens. The scattered light is then gathered by the lens and

then focused onto a pinhole aperture which is then fed to a detector for analysis. The benefit in

doing so greatly reduces the amount of unfocused light that reaches the detector and increases the

resolution of the system.

2.5 Surface-Enhanced Raman Spectroscopy

In 1974, a useful variation to the Raman technique was first observed in the United

Kingdom with pyridine adsorbed onto a silver surface [38]. The experiment considered the Raman

spectra in solution as Fleischmann et al. probed the solid-liquid interface of the pyridine molecule

adsorbed at various voltages applied to the soft silver electrode surface. The data obtained had

high resolution and was responsive to the detailed nature of the surface molecular environment in

addition to the orientation of Raman active molecules with respect to the electrode surface [38,

39]. Surface-enhanced Raman spectroscopy (SERS) therefore involves the amplification of the

Raman signal when molecules, under certain conditions, are adsorbed onto noble metal

nanoparticles [40]. The magnitude of the SERS enhancement depends on the chemical nature of

the adsorbed molecules, the roughness of the surface, and the optical properties of the adsorbate

[12]. The ultrahigh sensitivity of SERS allows experimenters to record spectra for concentrations

down to the single molecular level but also to estimate the forms and orientations of the on the

metal surface [12].

Subsequent experiments performed on the enhancement of the pyridine Raman signal were

performed at Northwestern University. After some debate on the issue, it is now generally agreed

that the core contributor to the SERS process is the electromagnetic enhancement mechanism. The

mechanism describes an amplification of the light by the excitation of localized surface plasmon

resonances. The light concentration occurs preferentially in the gaps, crevices, or sharp features of

the noble metals. In most cases the enhancement factor of the localized electromagnetic field is

15

proportional to the fourth power. The other mechanism involved is chemical enhancement. This

process primarily involves the transfer of charge and where the excitation wavelength is resonant

with the metal-molecule charge transfer electronic states. The factor of enhancement of this

mechanism is 103, however scientists have discovered that this mechanism is highly molecule

specific. The total SERS enhancement can be a combination of the electromagnetic and chemical

enhancement mechanisms.

2.6 Competing Physical Processes

When light irradiates matter, the Raman and Rayleigh effects are not the only processes

that occur. Fluorescence is a competing physical process. If the incoming photons have an energy

corresponding to the energy gap between the ground and excited state of the molecular constituents

of the material, then the photons are absorbed. The molecule gets promoted to its excited state

during this activity. After a certain lifetime, the system de-excites to a lower energy state by

emitting a photon corresponding to the energy difference between the excited and lower energy

state. Although both the Raman effect and florescence produce a scattered ray of light, florescence

is fundamentally a resonance effect. In other words, Raman scattering can in principle be

conducted with any frequency of light. In contrast, fluorescence needs a specific frequency to get

absorbed and then re-emitted by the system. Furthermore, the scattering cross section for Raman

scattering is usually 14 orders of magnitude smaller than that of florescence [41]. As mentioned

before, Rayleigh scattering is the dominating effect. On a spectrum, this corresponds to a sharp

peak at zero wavenumber. In SERS phenomena, however, florescence associated with Raman

bands is subdued. The range of detection of molecules, crude mixtures, and extracts is therefore

extended [12].

16

2.7 Density Functional Theory

Density Functional Theory (DFT) is a quantum mechanical method used to model the

electronic structure, especially the ground state, of a many-body system [42]. The application of

DFT calculations is rapidly becoming a standard tool for diverse materials modeling problems in

physics, chemistry, materials science, and multiple branches of engineering. In addition, the theory

is currently one of the most popular and successful quantum mechanical approaches to describe

matter and its physical properties [42]. It is a known fact that the solution to the time-independent

Schrodinger equation cannot be solved exactly, unless the system is Hydrogenic by nature, thus

several approximations must be made. Among the various approximations, one of the most

common is the Born-Oppenheimer approximation that allows the wavefunction to be broken into

a nuclear and electronic component due to their gargantuan difference in mass [43]. Furthermore,

a many-electron system and a given nuclear potential allows one to use the variational principle as

a procedure to determine the approximate ground-state energy and wavefunction, along with other

properties of interest. DFT methods utilizes functionals of the electron density, which is a function

of only 3 spatial variables and thus independent of the system size [44]. On the other hand, ab

initio methods such as Hartree-Fock, Moller-Plesset perturbation theory, configuration interaction,

and coupled cluster methods use a wavefunction for a system of N elections that depends on 3N

spatial variables. Consequently, the complexity of the calculations increases with system size [42].

The basic principle of density functional theory emerged in the1920s with the work of the

uniform electron gas by Fermi and Thomas. Their idea stated that the energy of the system is given

completely in terms of the electron density. In 1964, Hohenberg and Kohn published theorems to

which a year later, Kohn and Shams derived a set of monoelectronic equations which can obtain

the ground-state density. The first theorem shows that the electron density uniquely determines the

Hamiltonian operator and thus, all the physical properties of the system. The latter demonstrates

that the functional pertaining to the ground-state energy of the system delivers the lowest energy

17

if and only if the input density is the true ground state energy. This is nothing more that the

variational principle at work. The accuracy and efficiency of DFT-based methods depend on the

basis set for the expansion of the Kohn-Sham orbitals, but particularly upon the quality of the

applied exchange-correlation functional.

2.8 Sample Preparation and Instrumentation

For biomedical applications, water-soluble silver nanoparticles are of great importance and

are highly desirable. Unfortunately, producing silver nanoparticles in an aqueous solution is

challenging in that the nucleation process is difficult to control from the high reactivity of the silver

precursors in water [45, 46]

Table 2.1 The reagents used to synthesize silver nanoparticles are listed in addition to their initial

concentrations.

Reagent Concentration (w/v)

Silver nitrate (AgNO3) >99%

Sodium borohydride (NaBH4) >99%

Citric acid trisodium salt dehydrate (C6H5Na3O7˚H20) >99%

The synthesis of the silver nanoparticles begins by heating a mixture of 20 mL of 1% citrate

solution with 75 mL of deionized water until a steady temperature of 80oC is achieved. A solution

of 1.75 mL 1% AgNO3 and 2 mL of newly made 0.1% NaBH4 were added to the mixture. The

mixture was maintained at the same temperature for half an hour (30 min) while continuously

being stirred. The solution was then allowed to cool to ambient temperature before the purification

process.

18

To remove chemical impurities, the product went through a series of washes with pure

water coupled with centrifugation. Dopamine at 10-11 M concentration was mixed with 100𝜇L of

the newly synthesized silver nanoparticles After subjecting the mixture to ultrasonic vibrations to

fragment the product for 20 seconds, the solution was poured on clean cover slips to form dense

homogenous silver nanoparticle thin films. Soon after, the film was vacuum dried. This was done

to avoid the unwanted oxidation of the neurotransmitter and silver nanoparticles. In addition, this

process aided the formation of dense and uniform silver nanoparticle thin films [5, 45].

An alpha 300R WiTec system was used to take Raman measurements in combination with

the WiTec control 1.60 software for fast data acquisition. A 532-nm excitation of a frequency-

doubled neodymium-doped yttrium aluminum garnet (Nd:YAG) laser was restricted to a power

output of about 100𝜇W to avoid sample damage and a 20 X objective were used at room

temperature. Time series Raman spectra of 200 milliseconds a piece was recorded. The WiTec

Control 1.60 software was used for fast data acquisition. An illustration of the Raman apparatus

can be referenced in Figure 2.2.

19

Figure 2.2 The Raman apparatus used in the experiment is displayed alongside the software used

for data acquisition.

2.9 Density Functional Quantum Calculations

Using the Gaussian-09 analytical suite software, quantum density functional theory

calculations were treated. To begin such calculations, dopamine was first energetically optimized

for all its ionic and geometric forms including the neutral (DA0), anionic (DA-), catatonic(DA+),

and dopaminequinone species. The vibrational frequencies of each class were then computed using

the Becke three hybrid exchange (B3LYP) and the Lee-Yang-Parr correlation functional [47, 48].

A Pople split valence diffused and polarized 6-311++G(d,p) basis set was used for the calculations.

It is well known that the 6-311++G(d,p) basis in combination with the B3LYP functional are

adequate for the estimation of geometric parameters and harmonic frequencies of molecules like

dopamine that have substituted phenyl ring structures [12].

20

The Raman activities, Si, obtained from the Gaussian-09 software were further converted

into relative Raman intensities (Ii) using:

𝐼L =𝑓𝑆L(𝑣# − 𝑣L)N

𝑣L 1 − exp −ℎ𝑐𝑣L𝑘𝑇

where v0 is the excitation frequency [49]. In the case of the 532 nm laser, the excitation frequency

is 18796.99 cm-1. Other constants appearing in the above equation include Boltzmann’s constant

k, the speed of light c, Planck’s constant h, and the ambient temperature of the room T which was

given the value of 293.15 Kelvin.

Lorentzian band shapes were given to the output of the Raman intensities with a

normalization factor of f=1e-12. Furthermore, the full width at half maximum of the bands were

given a value of 7 cm-1. The results were then converted into a Raman spectra through MATLAB

version 2016a.

21

Chapter 3: Results

3.1 Experimental and Computational Raman Data

Using the Gaussian-09 software, density functional calculations were carried out for the

optimized molecular structure of neutral dopamine (DA0) in addition to its Raman spectra. Such

theoretical vibrations are depicted in Figure 3.1. Furthermore, as a comparative tool, the

experimentally measured Raman spectrum of solid dopamine powder is also illustrated in the

figure. At first glance, there is relatively good agreement between the simulated and measured

spectral lines.

The computational Raman spectra has been scaled by a factor of 0.98 to account for and

overcome systematic practical errors originating from the force field constants employed in the

algorithms in the quantum mechanical DFT calculations [12, 49]. A scaling factor was used to

minimize errors gained from the simulation process. These factors, although helpful, cannot

completely account for the discrepancies from theoretical calculations. Moreover, even more

detailed calculations cannot accurately describe the specifics and limitations of the experimental

environment. Nonetheless, scaling factors are still used to obtain a more precise comprehension of

SERS Raman spectra in both the theoretical and experimental point of view [5].

22

Figure 3.1 The Raman spectra for DA0 from computational analysis (above) is compared with

experimentally derived bands (below) for solid dopamine powder. [5]

By looking at the comparative Raman spectra in Figure 3.1, there are average discrepancies

of 10±2 cm-1 between the experimentally observed and simulated values. The error becomes much

larger at higher frequencies for unscaled data suggesting the overestimation of the force field

constants employed in Gaussian DFT calculations. The measured and computationally estimated

values of the main vibrational modes along with their assignments for pure neutral dopamine are

summarized in Table 3.1.

400 600 800 1000 1200 1400 1600 18000

1500

3000

4500

6000

1631

1466

961

1148

1209 16

17

1455

1615

1355

132112

94

1029

111796

293

193

4577

381

744

782

63559

3

393

1285

750

795

Ram

an In

tens

ity (c

ount

s)

Wavenumbers (cm-1)

B

1387

1156

11231060

B

23

Table 3.1 Comparison of theoretically simulated and experimentally measured Raman

vibrational assignments of DA.

Theoretical

(cm-1)

Experimental

(cm-1) Vibrational Assignment

381 393 C-H wagging, ring deformation

577 593 C-H in-plane ring deformation

635 C-H wagging; C-C aliphatic chain vibrations

744 750 C-H out-of-plane ring deformation (two band response)

782 795 C-H out-of-plane ring deformation (two band response)

934 931 N-H twisting

961 962 N-H twisting; C-H wagging ring deformation

1029 C-H wagging

1060 C-C-N stretching; C-H wagging

1123 1117 C-H twisting; N-H twisting; C-N stretching

1156 1148 O-H rocking; C-H aromatic rocking; weak ring breathing; C-H

wagging

1209 C-O stretching

1294 1285 Ring breathing, C-H aromatic rocking; C-H twisting

1321 Ring breathing; C-H aromatic in-plane rocking; C-H twisting

1355 Ring deformation; O-H scissoring; C-H twisting

1387 C-H wagging; N-H twisting

1466 1455 C-H scissoring

1615 1617 Ring deformation, O-H scissoring

1631 NH2 scissoring

24

Based on biochemical studies, deprotonation of dopamine is expected when it is dissolved

in water, which can result in molecular transformation of dopamine in to dopaminequinone.

Furthermore, the same aqueous environment can manufacture the cationic and anionic DA states

through acid-base equilibria reactions [50]. During sample preparation, DA0 was mixed with

deionized water to prepare the 10-11 M dopamine solution for analysis and then once again when

preparing the Ag NP solution. Due to the aqueous environment that the neutral dopamine powder

was subjected to, chemical reactions could have occurred that could have caused the conversion

of the molecule into its various molecular conformers. Figure 3.2 shows the conversion of neutral

dopamine into its dopaminequinone state which is a result of a one-step redox reaction involving

two electrons and two protons. The quinonic form is discussed first due to the symmetry of the

dopamine molecule with respect to the O-H and N-H bonds.

Figure 3.2 The redox conversion of dopamine to dopaminequinone. [5]

The effect of the SERS environment on the Raman vibrational frequencies of dopaminequinone is

further analyzed from theoretical and experimental perspectives in Figure 3.3.

25

Figure 3.3 The optimized structural representation of dopaminquinone is shown near a silver

dimer (above) along with the simulated Raman spectrum of the molecule in a SERS

environment (middle) and experimentally measured data (below). [5]

26

The structural representation of the analyte in the proximity of silver in Figure 3.3, after

energy optimization, uses a LanL2DZ basic set that takes into consideration the pseudopotentials

for metal atoms [5]. Comparison between the Raman spectra of experimentally obtained and

simulated bands of dopaminequinone shown in Figure 3.3 show relatively good agreement. When

the experimental spectrum is compared with the SERS Raman spectrum of DA0 as in Figure 3.4,

it is important to note the disappearance of the dominant dopamine vibrations that are located at

750 cm-1 and 795 cm-1. This suggests adsorption of dopaminequinone on the silver nanoparticle

thin film.

The stronger interaction of the dopaminequinonic form with silver could be attributed to a

somewhat greater electronegativity of oxygen atoms in this configuration than in the neutral

dopamine form. Dominant Raman vibrations around 1360 cm-1 and 1510 cm-1 are observed for

dopaminequinone interaction with silver nanoparticles. The results of this finding suggests that not

only can dopamine be detected at subphysiological concentrations, but also implies that SERS is

a powerful technique that can even identify the specific configuration of dopaminequinone.

27

Figure 3.4 Dopamine in the presence of a silver dimer is presented. [5]

28

Since other vibrational lines have been experimentally observed and reported in the

literature for dopamine, together with the fact that the dissociation of the dopamine into its ionic

forms is expected from its interaction with water molecules, DA+ and DA- are further investigated

in this study. Due to our ability to detect dopaminequinone, analysis of other positions on the silver

nanoparticle thin film were conducted to see if the presence of these other expected ionic forms

could be established. In Figure 3.5, one of the various spectra that were measured is illustrated.

Again, when this spectrum is compared to what should be expected for DA0, there is a discrepancy.

The SERS spectrum depicted in Figure 3.5 is therefore not that of neutral dopamine. For

comparison with experimental observations, the optimized molecular structure of DA- and DA+

were simulated with the Gaussian-09 software to produce SERS spectrum for each ion. When the

experimental spectrum of Figure 3.5 is compared to the estimated vibrational frequencies of

anionic dopamine, there is relatively good concurrence between the two. Furthermore, the Raman

band at around 1150 cm-1 in both the experimentally and theoretically observed frequencies has

been reported in literature as the in-plane bending of anionic dopamine [12]. Both comparison

from simulation and the appearance of the band further support the presence of DA- in the

experiment.

In a similar fashion, an experimental SERS Raman spectrum was measured at a different

location on the synthesized silver nanoparticle surface that was different from that of DA0,

dopaminequinone, and DA-. From previously simulated DFT calculations, this new spectrum was

compared to the final dopamine configuration that is expected in Figure 3.6. Comparison of the

two spectra show relatively good agreement and confirm of the manifestation of DA+ in the

experimental set-up. The appearance of the Raman band at around 1190 cm-1 in Figure 3.6, which

is characteristic of the in-plane bending of cationic dopamine, further supports the existence of

DA+ [51].

29

Figure 3.5 The anionic molecular form of DA- is shown (above) along with the experimentally

measured (middle) and theoretically calculated (below) SERS Raman spectra. [5]

30

Figure 3.6 The cationic structural representation of DA+ is shown (above) along with the

experimental (middle) and simulated (below) SERS Raman spectra. [5]

31

3.2 Conclusions

In conclusion, the study presented in this paper set out to detect and monitor dopamine at

the subphysiological concentration of 10-11 M, which is at least two orders of magnitude smaller

than those reported with current techniques. Such a feat was achieved through the optical technique

of surface-enhanced Raman spectroscopy with the aid of synthesized silver nanoparticle

substrates. In addition, the analysis of trace quantities of the dopamine neurotransmitter was

supplemented by a comparative computational simulation of Raman spectral bands which were in

relatively good agreement with experimental observations.

Although the measurements presented in this investigation were not performed in an

aqueous environment, such measurements are preferred since dopamine is found in solution in

real-world clinical applications. Still, the experimental observations facilitate a reliable

comprehension of the Raman vibrational assignments and eliminate the complications that liquids

and other organic matter introduce into the investigative process. Examples of such difficulties

include the unusual broadening of Raman spectral markers that is characteristic of measurements

in solutions and a significant complication to theoretical computational analysis. Furthermore,

when an aqueous environment is introduced, the ability to generate an agreement on the

reproducibility of Raman spectra would be even more difficult to achieve. Especially at low

concentrations, this issue involves the probability that molecules will become localized in regions

of intense optical fields trapped between adjacent plasmonic metallic nanoparticles and thereby be

subjected to SERS enhancement of their inherently weak Raman signals. Yet, the results of this

experiment still give important insights into the complex process of DA detection at very low

concentrations by eradicating the aforementioned limitations.

The relatively good agreement between the simulated and experimentally determined

results suggests that all forms of the dopamine molecule, such as its neutral, anionic, cationic, and

that of dopaminequinone are present. The dopaminequinone molecular configuration in a SERS

32

environment loses the dominant DA Raman lines at 650 cm-1 and 795 cm-1, suggesting its

adsorption onto the silver nanoparticle surface. The high resolution and sensitivity of the current

experimental data, in combination with the theoretical analysis presented in this work, provides

valuable information for advancing and developing an alternative method the detection and

monitoring of dopamine using surface-enhance Raman spectroscopy as an analytical technique. In

addition, the data has applicability in the development of more sensitive bio detectors for

diagnostic medical purposes.

33

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39

Vita

Matthew Alonzo was born and raised in El Paso, Texas and attended Mission Early College

High School (MECHS). While at MECHS Matthew was able to ascertain his Associate of Arts

degree in Pre-Medicine with special honors from the Honor College a year before he earned his

high school diploma. In his senior year, Matthew was awarded the Gates Millennium Scholarship

by the Bill and Melinda Gates Foundation. After graduating high school, he matriculated to Texas

Tech University in Lubbock, Texas where in December 2015 he graduated with a Bachelor of

Science degree in physics. The following semester, Matthew enrolled at the University of Texas

at El Paso where he worked as a teaching assistant and did research in the Optical Spectroscopy

and Microscopy Laboratory under the direction of his advisor Dr. Felicia S. Manciu. In May 2017,

he graduated with his Master of Science degree in physics. In addition, he has been accepted into

UTEP’s biomedical engineering program where he will work toward the ascertainment of his

Ph.D.

Contact Information: alonzo_matthew@yahoo.com

This thesis was typed by Matthew Alonzo.