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Open Access Theses & Dissertations
2017-01-01
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.