100
Graphene Oxide-Coupled Surface Plasmon Resonance Detection of Immunoassays Yeonsoo Ryu The Graduate School Yonsei University School of Electrical & Electronic Engineering
Graphene Oxide-Coupled Surface Plasmon
Resonance Detection of Immunoassays
Yeonsoo Ryu
The Graduate School
Yonsei University
School of Electrical & Electronic Engineering
Graphene Oxide-Coupled Surface Plasmon
Resonance Detection of Immunoassays
A Dissertation
Submitted to the School of
Electrical & Electronic Engineering
and the Graduate School of Yonsei University
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Yeonsoo Ryu
August 2014
This certifies that the dissertation
of Yeonsoo Ryu is approved.
The Graduate School
Yonsei University
August 2014
Acknowledgements
89 년 재료공학 석사학위 취득 후 항상 박사학위에 대한 꿈을 꾸었지만
감히 도전하지 못하였습니다.‘늦었다고 생각할 때가 가장 빠른
때다’,‘남이 가지 않은 길을 가라’는 가르침에 따라 40 대에 직장에서
가장 가까운 대학의 박사학위 과정에 도전하였습니다. 직장생활과 공학박사
학위 과정을 병행하면서 생각하지 못하였던 많은 어려움과 지도교수님 정년
퇴직으로 인한 연구실 변경 등 많은 우여곡절이 있었습니다. 처음 연세대
박사과정으로 인도해주신 이명호 교수님과 좋은 결실을 거둘 수 있도록
지도해주신 지도교수님이자 학문적 멘토 이신 김동현 교수님에게 감사
드립니다. 아울러 좋은 논문을 쓸 수 있도록 상세한 지도와 심사를
진행해주신 황도식 교수님, 이태윤 교수님, 정효일 교수님, 이강택 교수님
에게도 감사 드립니다.
7 년반 동안 늦깎이 학생의 공부를 뒷바라지 하느라 많은 희생을 감내한
아내 이희정과 사랑하는 두 딸 지원, 재원에게 박사학위의 영광을 돌리고
싶습니다. 항상 격려해주시고 용기를 북돋아 주신 장인, 장모님, 처남, 처제
이정은, 어머님과 연숙, 화신, 화수, 오순, 오철에게도 감사 드립니다. 바쁜
연구실 생활 중에도 언제나 친절하게 도와준 광생체공학 연구실의 문세영박사,
김규정교수, 최종율박사, 오영진박사, 이원주, 류호정, 김용휘, 이태웅,
손태황, 이홍기, 양희진 연구원에게 감사의 인사를 드립니다. 연구실
식구들의 도움이 없었다면 졸업의 영광은 결코 없었을 것입니다. 대구에서
실험을 도와준 후배 DGIST 장재은 교수에게도 감사를 표합니다. 25 년만에
공학박사의 꿈이 이루어질 수 있도록 도와주시고 격려를 아끼지 않으신 주위
모든 분 들에게도 감사의 말씀을 드립니다.
柳 沇秀 拜上
Contents
List of Figures ..........................................................................................................i
List of Tables ......................................................................................................... iii
List of Abbreviations .............................................................................................iv
Abstract ..................................................................................................................vi
Chapter 1. Introduction ......................................................................................... 1
1.1. Introduction ···················································································· 2
1.2. Biosensors ······················································································ 5
1.3. Label-Free Detection·········································································· 8
1.4. Surface Plasmon Phenomenon ······························································ 10
1.5. Surface Plasmon Resonance Biosensors··················································· 14
1.6. Sensor Application of Graphene Oxide ···················································· 18
1.7. Surface Functionalization ··································································· 21
Chapter 2. Performance Evaluation and Numerical Analysis ......................... 25
2.1. Sensor Performance Evaluation ···························································· 26
2.2. Theoretical Approaches to Sensitivity ····················································· 27
2.3. Numerical Analysis ·········································································· 29
Chapter 3. Study on the Correlation between Field-Target Overlap and
Sensitivity of Surface Plasmon Resonance Biosensors ................. 32
3.1. Introduction ···················································································· 33
3.2. Numerical Simulation ········································································ 35
3.3. Experimental Methods ······································································· 37
3.4. Optical Set-up ················································································· 40
3.5. Results and Discussion ······································································· 41
3.6. Summary ······················································································· 50
Chapter 4. Study on the Detection Sensitivity of Graphene Oxide-Coupled
Surface Plasmon Resonance for Immunoassays ........................... 51
4.1. Introduction ···················································································· 52
4.2. Numerical Simulation ········································································ 54
4.3. Experimental Methods ······································································· 56
4.4. Results and Discussion ······································································· 60
4.5. Summary ······················································································· 71
Chapter 5. Conclusion ......................................................................................... 72
5.1. Conclusion ................................................................................................................. 73
5.2. Future Work ............................................................................................................... 75
References ............................................................................................................. 76
Publication & Presentation Lists ........................................................................ 83
Abstract (Korean) ................................................................................................ 84
i
List of Figures
Figure 1.1 Basic components of biosensors························································ 5
Figure 1.2 Schematic of SP phenomenon ·························································· 11
Figure 1.3 Dispersion relations: (a) light in air, (b) photon in metal, and (c)
SPP ····································································································
12
Figure 1.4 Typical EELS spectrum ···································································· 13
Figure 1.5 Prism configurations: (a) Otto configuration and (b) Kretschmann
configuration ·····················································································
14
Figure 1.6 SPR measurements with protein interaction: (a) before protein
interaction, (b) SPR measurement result before protein interaction,
(c) after protein interaction, and (d) SPR measurement result after
protein interaction ·············································································
17
Figure 1.7 Comparison of graphite and graphene related materials ·················· 18
Figure 1.8 Structure of GO ················································································ 19
Figure 1.9 Typical immobilization methods: (a) covalent (amine, thiol,
aldehyde), (b) capture (streptavidin-biotin), (c) capture (antibody-
base), and (d) monolayer attachment ················································
23
Figure 1.10 Typical assay formats: (a) sandwich immunoassay, (b) indirect
inhibition immunoassay, (c) protein-labeled inhibition
immunoassay, and (d) direct small molecule immunoassay·············
24
Figure 2.1 N-layer stacked structure ·································································· 30
Figure 3.1 Schematic model for numerical calculation: (a) sandwich
immunoassay, (b) reverse sandwich immunoassay, and (c)
parameter values ···············································································
36
Figure 3.2 Experimental procedure for sandwiched immunoassays··················· 39
Figure 3.3 Schematic of a SPR detection system ·············································· 41 Figure 3.4 Measured results for sandwich immunoassays: (a) measured SPR
curves, (b) resonance angle and angle shifts ····································
42
Figure 3.5 Measured results for reverse sandwiched immunoassays: (a)
measured SPR curves and (b) Resonance angle and angle shifts ·····
43
Figure 3.6 Normalized differences in optical signatures (left axis) and
resonance angle shifts that were calculated theoretically and
experimentally measured (right axis) ): (a) reverse sandwich and
(b) sandwiched assay ········································································
45
Figure 3.7 Correlation coefficients: with (a) theoretical results and (b)
experimental data. Filled squares (■) and open circles (○)
represents reverse sandwich and sandwich assays, respectively ······
47
Figure 3.8 Calculated near-field distribution in sandwich assay. Inset
magnifies xE in the binding layers and ambient buffer near the
interface. The fields were calculated assuming unit incident
electric field amplitude. yH is normalized by vacuum
ii
characteristic impedance 2/1
00 )/( εµη = ········································ 49
Figure 4.1 Schematic model for numerical calculation: (a) schematic structure
and (b) parameter values ···································································
55
Figure 4.2 Experiment procedure for sandwich immunoassays ························ 57
Figure 4.3 Deposition material and machine: (a) GO solution (Graphene
Labs., USA) and (b) LB machine (KSV NIMA, Finland) ···············
58
Figure 4.4 Schematics of sandwich antibody-antigen interaction: (a)
carboxylate-modified, (b) antibody binding of a-h-IgG, (c) reaction
blocking of BSA treatment, (d) antigen binding of h-IgG, and (e)
subsequent antibody binding of a-h-IgG ··········································
59
Figure 4.5 SEM and AFM images of deposited GO: (a) SEM image (the inset
shows sample shape), (b) AFM image, and (c) AFM height profiles
of GO surface ····················································································
61
Figure 4.6 Measuring results: (a) measured resonance angle shift and (b)
FWHM versus thickness of SiO2 spacer ···········································
63
Figure 4.7 Measured resonance angle shift for metal-enhanced SPR detection
(without GO) and GO-coupled SPR detection. Here SiO2 spacer was not used ·····················································································
64
Figure 4.8 Relative resonance shift (RRS) calculated for GO-coupled and
metal-enhanced SPR detection versus thickness of SiO2 spacer.
Arrows show the increase of GO and gold layer thickness ··············
66
Figure 4.9 Normalized RRS from simulation and experiment: simulation
result for GO-coupled SPR detection (black line), and metal-
enhanced SPR detection (red line). Also shown are experiments
result for GO-coupled detection (blue squares) ································
68
Figure 4.10
Tangential near-field amplitude profiles of xE calculated for
GO-coupled SPR detection: 2SiOt = (a) 0, (b) 40, and (c) 80 nm.
The field amplitude was normalized by that of an incident light
field. Metal-enhanced SPR detection of sandwich immunoassays:
2SiOt = (d) 0, (e) 40, and (f) 80 nm. Thickness of GO and gold: 0-
10 nm. SI denotes sandwich interaction ···········································
70
Copyright to reproduce full contents are permitted by The Optical Society and Elsevier.
iii
List of Tables
Table 1.1 Types of transducers with the measured properties ······················· 7
Table 1.2 Types of detection techniques for biosensors ································ 8
Table 1.3 Comparison of labeled and label-free biosensors ·························· 9
Table 1.4 Comparison of gold and graphene-related materials ····················· 16
Table 1.5 Recent studies of GO biosensors ··················································· 20
Table 1.6 Comparison of assembly methods ················································· 21
Table 2.1 Comparison of simulation methods used in photonics ·················· 29
Table 3.1 Recent theoretical approaches to clarifying the sensitivity of SPR
sensor ·····························································································
34
Table 4.1 Recent studies in SPR sensor based on graphene-related
materials ························································································
53
iv
List of Abbreviations
AFM atomic force microscopy
AgNPs silver nanoparticles
HPAI highly pathogenic avian influenza
AIDS acquired immunodeficiency syndrome
ATR attenuated total reflection
AuNPs gold nanoparticles
BRE biomolecular recognition elements
BSA bovine serum albumin
CNT carbon nanotube
DNA deoxyribonucleic acid
EDC 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide
hydrochloride
EELS electron energy loss spectroscopy
ELISA enzyme-linked immunosorbent assay
EM electromagnetic
FDTD finite difference time-domain method
FEM finite element method
FET field effect transistor
FOM figure of merit
FMD foot-and-mouth disease
FWHM full width at half maximum
GO graphene oxide
HIV human immunodeficiency virus
h-IgG human immunoglobulin
IoT internet of things
IUPAC International Union of Pure and Applied Chemistry
LB Langmuir-Blodgett
LBL layer-by-layer
LOD limit of detection
LSPR localized surface plasmon resonance
MEA mercaptoethylamine acid
MPA 3-mercaptopropionic acid
MS mass-spectrometry
NHS N-hydroxysuccinimide
NPs nanoparticles
OI overall integral
PBS phosphate buffered saline
PMMA polymethyhnethacrylate
PMT photomultiplier tube
PSPR propagating surface plasmon resonance
QCM quartz crystal microbalance
RCWA rigorous coupled-wave analysis
RIU refractive index unit
v
SAM self-assembled monolayer
SARS severe acute respiratory syndrome
SEM scanning electron microscopy
SELDI surface-enhanced laser desorption/ ionization
SERS surface-enhanced Raman scattering
SPP surface plasmon polariton
SPW surface plasmon wave
SP surface plasmon
SPR surface plasmon resonance
TIR total internal reflection
TE transversal electric
TM transversal magnetic
TMM transfer matrix method
WBN wireless biosensor network
vi
Abstract
Graphene Oxide-Coupled Surface Plasmon Resonance
Detection of Immunoassays
Ryu Yeon Soo
School of Electrical and Electronic
Engineering
The Graduate School
Yonsei University
As our society ages, the number of patients suffering from chronic diseases, including
dementia, cardiovascular disease, hypertension, and cancer, is rapidly increasing. New
epidemics such as severe acute respiratory syndrome, foot-and-mouth disease, and avian
influenza, which are caused by various types of molecular-sized viruses, have been
frequent recently. To diagnosis and cure these diseases in the early stage, detection and
monitoring of low-molecular analytes have become more important than ever before. To
satisfy these demands, it is necessary to develop high-performance biosensors. Although
surface plasmon resonance (SPR) biosensors have received much attention for their label-
free detection and real-time sensing, it is still difficult to detect small molecules (< 2 kDa)
and monitor inter-molecular reactions using them.
This thesis briefly reviews the important issues with regard to the development of SPR
biosensors, such as the types of biosensors and detection, surface plasmon phenomenon,
graphene oxide application, surface functionalization, and sensitivity. This thesis also
presents two studies: (1) a study of the correlation between the field-target overlap and
vii
sensitivity of SPR biosensors and (2) a comparison of graphene oxide-coupled SPR
detection and metal-based SPR detection.
Many experimental results have demonstrated improved sensitivity, but the mechanism
behind the enhancement is not yet clear. Therefore, I investigated the correlation of the
detection sensitivity of SPR biosensors with the optical signatures related to the near-field
overlap of biomolecules. The sandwich antibody-antigen interaction and reverse
sandwich interaction were used to measure the resonant angle shift. Human and
antihuman immunoglobulin G molecules were used as the antigen and antibody,
respectively. The detection sensitivity of the biosensor was examined using the
correlation with the field-target overlap in the near-field. The results show that the
overlap and detection sensitivity are strongly correlated, both theoretically and
experimentally, with correlation coefficients exceeding 95% in all cases.
A gold surface has been used as a platform for biosensors, but it is limited by relatively
poor adsorption with biomolecules. Since Prof. Geim received the Nobel Prize in 2010,
there have been many studies of graphene and graphene oxide because of its superior
electrical and optical properties. Graphene oxide is an intermediatry obtained during
fabrication of graphite into graphene. It has great potential for application in SPR
biosensors because of its superior characteristics such as good water dispersibility, high
mechanical strength, facile surface modification and sp2/sp3 existing structure. Therefore,
I tried to measure the detection sensitivity of graphene oxide-coupled SPR after
depositing a SiO2 spacer and graphene oxide on the gold surface. The resonance angle
shift of the biosensors was studied by experimentally comparing graphene oxide-coupled
SPR detection and conventional SPR detection. Graphene oxide-coupled SPR detection
enhances the resonant shift to at least 113% of that of conventional SPR detection. This is
viii
the first attempt to calculate and measure the detection sensitivity of an SPR biosensor
based on graphene oxide deposited by Langmuir-Blodgett assembly. This thesis confirms
the feasibility of applying graphene oxide to SPR biosensors.
The results of this research will contribute to the development of a high-performance
SPR biosensor that can detect small molecules, in particular, at low concentrations.
Keywords: surface plasmon resonance, biosensors, graphene oxide, Langmuir-Blodgett,
sensitivity
1
Chapter 1
Introduction
2
1.1 Introduction
As our society ages, the number of patients suffering from chronic diseases, including
dementia, cardiovascular disease, hypertension, and cancer, is rapidly increasing. New
viral diseases such as SARS, FMD, AI, which are caused by various types of molecular-
scale viruses, occur frequently. To respond quickly to these diseases, detection and
monitoring of low-molecular analytes are more important than ever before.
After Biacore launched the first biosensor system based on surface plasmon resonance
(SPR) in 1990, SPR biosensors have been widely used in clinical diagnostics and
chemical reaction monitoring because of their advantages in real-time detection without
labeling. On the other hand, it is still difficult to detect small molecules (< 2 kDa) and
monitor inter-molecular reactions using SPR biosensors. For this reason, there have been
various attempts to enhance their sensitivity. An SPR immunosensor, which uses an
antibody and antigen as a bioreceptor, has received much attention because of its good
performance in terms of sensitivity, specificity and speed [1]. For the assay formats,
sandwich immunoassays are preferred because of their advantages for molecular-level
detection and diagnosis.
To date, numerous experiments have been conducted to improve the sensitivity of SPR
biosensors. For example, gold nanoparticles (AuNPs) were used to immobilize
biomolecules [2, 3]. Nanostructures fabricated on the metal surface were used to localize
SPs [4, 5]. The sensitivity of SPR biosensors was reportedly improved by more than 300
times recently compared to that of conventional thin film detection by combining NPs
and nanostructure [6]. In contrast to experimental enhancement of the sensitivity of SPR
biosensors, theoretical studies on the mechanism of the sensitivity enhancement have not
3
progressed greatly. A previous study reported a strong correlation between the field-target
overlap and sensitivity of SPR biosensors [7, 8], which was used to study the mechanism
behind the sensitivity using the results of SPR measurement and simulation.
The sensor platform plays an important role in determining the sensitivity of the
biosensor because molecules are immobilized on the surface of the platform, and the
sensory output signals depend on the optical and electrical properties of the platform
material. A gold surface has often been used as a platform for biosensors, but it is limited
by relatively poor adsorption with biomolecules [9]. For this reason, there have been
numerous attempts to improve the adsorption of the gold surface. It was reported recently
that the deposition of graphene oxide (GO) on the gold surface contributed to enhanced
sensitivity of SPR biosensors [10, 11]. GO has great potential for application to SPR
biosensors because of its superior characteristics such as good water dispersibility, high
mechanical strength, facile surface modification and sp2/sp3 existing structure [12].
According to Zhang et al. [10], carboxyl groups on the GO surface are helpful for
immobilizing antibodies and detecting antigens. On the basis of SPR measurements and
simulations, the effect of GO on the detection sensitivity of SPR biosensors has been
investigated.
Ultimately, this dissertation will contribute to the development of an SPR biosensor
that can detect small molecules (< 2 kDa) and monitor molecular interactions. This
dissertation contains five chapters divided into major topics as indicated below.
First, Chapter 1 provides an overview of biosensors and the SP phenomenon. The
basic theories and technology of SPR biosensors are described. In Chapter 2, the theory
of the sensitivity and the method of numerical analysis of SPR biosensors are presented.
Chapter 3 contains the result of a numerical simulation and an experiment with sandwich
4
and reverse sandwich immunoassays. In Chapter 4, the results of a numerical simulation
and an experiment with GO-coupled SPR for sandwich immunoassay interaction are
presented. Finally, Chapter 5 summarizes the experimental results and proposes future
work.
5
1.2 Biosensors
Since the concept of glucose enzyme electrodes was proposed by Clark and Lyon in
1962 [13], biosensor technologies have achieved considerable success in biomedicine,
food technology, and the environmental sciences and engineering. IUPAC recently
defined a biosensor as a “device that uses specific biochemical reactions mediated by
isolated enzymes, immunosystems, tissues, organelles or whole cells to detect chemical
compounds usually by electrical, thermal or optical signals” [14]. In other words, a
biosensor can be defined as “an analytical device that can transform the information
measured or detected into a recognizable signal.”
Biosensors typically consist of three components, a bioreceptor, transducer, and signal
output system, as shown in Figure 1.1 [15].
Figure 1.1 Basic components of biosensors.
6
1) The bioreceptor is a biological recognition element (BRE) that selectively binds to
the molecules that are to be sensed, often called the ‘target or analyte’. The BRE
contributes to enhancing the sensitivity of the sensor by selectively interacting or
binding with specific materials. Examples of BREs are antibodies/antigens, enzymes,
DNA, cells and viruses and biomimetic materials.
2) The transducer converts the recognized information from a chemical reaction into a
measurable physical signal. Typical signal transduction methods include
electrochemistry, colorimetry, and the use of fluorescence, SPR, FET, and QCM.
3) The signal output system is responsible for signal processing and displaying the
output.
Biosensors can be classified by the signal transduction method, for example, optical
methods [16], electrochemical methods [17], mass-sensitive methods [18], and thermal
methods [19]. Table 1.1 lists the types of transducers with their measured properties. For
optical methods, various optical properties such as the luminescence, fluorescence,
absorption, phosphorescence, SERS, dispersion, and refraction spectroscopy are used to
measure the physical signal. Optical biosensors can also be classified as fiber optical or
non-fiber optical methods depending on whether an optical fiber is used.
Electrochemical methods use a current change that occurs during the oxidation and
reduction reactions. In other words, electrochemical biosensors use the correlation
between the change in the current and the change in the concentration of the electroactive
species present or in the rate of production/consumption. Electrochemical biosensors are
widely used because of their advantages for detecting biomolecules. They can be
classified by the methods of impedance measurement, which includes impedimetric,
7
amperometric, and voltametric methods.
The mass-sensitive method measures the change in mass that occurs during chemical
binding between the analytes and a small piezoelectric crystal. This method exhibits
highly sensitive detection compared to the other methods.
The thermal method measures the changes in temperature during interactions between
molecules and analytes which is correlated to the amounts of reactants consumed or
products formed.
Table 1.1 Types of transducers with their measured properties [20].
Type of Transducer Measured Property
Optical
• Luminescence
• Fluorescence
• Adsorption
• Phosphorescence
• Surface-enhanced Raman scattering
• Dispersion
• Refraction spectroscopy
Electrochemical • Impedance
• Current
• Voltage
Mass-sensitive • Resonance frequency of piezocrystals
Thermal • Heat of reaction
• Heat of adsorption
8
1.3 Label-Free Detection
Biosensors can be classified by the method of signal detection. The detection methods
used in biosensors can be divided into labeling and label-free methods depending on the
label materials, as shown in Table 1.2. In labeled detection techniques, label materials
such as fluorescent materials, radioactive materials, and enzymes, are used. Label-free
detection techniques use SPR, a QCM, an interferometer, an optical waveguide, an
optical ring resonator, and an optical fiber.
Table 1.2 Types of detection techniques for biosensors [21].
Types of Techniques Detection Method
Label
Fluorescence • Fluorescence microscopy
• Total internal reflection fluorescence
Radioactivity • Scintillation proximity assay
Enzyme • Enzyme-linked immunosorbent assay
Label-free
• SPR
• QCM
• Interferometer
• Optical waveguide
• Optical ring resonator
• Optical fiber
9
Labeled and label-free biosensors are compared in Table 1.3. Label-free detection
techniques have the advantages of real-time monitoring and direct detection but have
limitations in terms of the sensitivity, LOD, and concentration. Label-based detection
methods have good performance in terms of the sensitivity and LOD. It is known that a
labeled technique generally has a sensitivity four orders higher than that of a label-free
technique [22].
Table 1.3 Comparison of labeled and label-free biosensors [22].
Type of
Technique Advantages Disadvantages
Labeled
� High sensitivity
� High LOD
� Small molecules
� Indirect detection
� Reaction between molecules
and label
� No real-time detection
Label-free
� Real-time monitoring
� Direct detection
� Small volume sample
� Low sensitivity
� Low LOD
� Low concentration
� No small molecule detection
� Immobilization effect
10
1.4 Surface Plasmon Phenomenon
In 1902, Wood first reported that he had discovered abnormal dark and light bands
when polarized light was incident on a metal-backed diffraction grating [23]. This was
theoretically explained in 1942 by Fano [24], as a result of the oscillation of
electromagnetic waves at the metallic diffraction grating. The SP phenomenon was
completely explained in 1968 by Otto [25] and Kretschmann [26], who used the concept
of total internal reflection (TIR). With respect to the commercialization of the SPR
biosensor, Liedberg et al. first proposed the SPR immunosensor in 1983 [27]. In 1990,
Biacore (based in Sweden) launched the first SPR-based biosensor system [28]. In 2006,
GE Healthcare acquired Biacore for 390 million dollars [29].
A plasmon is a quantum of the collective charge density (or plasma) oscillation in
metal [30, 31]. An SPP refers to the surface electromagnetic waves (or plasmon) confined
to a metal-dielectric interface, as shown in Figure 1.2. Here, an SPP is a combined
oscillation of an SP and a photon. When an SP is formed on a metallic NP, it is called a
localized SPR (or LSPR) [32].
11
Figure 1.2 Schematic of SP phenomenon [28].
An SP can propagate along a metal-dielectric interface and have a plasmon frequency
(ω ) related to the wave vector (κ ) by a dispersion relation. Because an SP is a type of
electromagnetic wave, its dispersion relation can be calculated using Maxwell’s equations
and appropriate boundary conditions. In this calculation, only p-polarized surface
oscillations (whose magnetic field H is parallel to the interface, 0== zx HH and
0=yE ) are considered and may propagate along the surface. The boundary conditions
used here are that the component of the electric and magnetic fields parallel to the surface
must be continuous. Solving Maxwell’s equations under the boundary conditions yields
the SP dispersion relation, for example, equation (1.1), that is, the frequency-dependent
SP wave-vector ( spκ ),
md
mdsp
εε
εε
c
ωκ
+= (1.1)
where dε and mε denote the dielectric constant of the interfacing dielectric (analyte)
12
and the metal, respectively. For dielectrics, the dispersion relation is a parabolic line as
shown in Figure 1.3. For light travelling through dielectrics at frequency ω, the wave
vector ( phκ ) is described by equation (1.2):
dph εc
ωκ = (1.2)
Here c is the speed of light in air, and for air the dispersion relation is a straight line.
Figure 1.3 shows the dispersion curves of an SP, where (a) and (b) are the dispersion
curves of light in air and in metal, respectively. For the dispersion of light in air, the SP
dispersion curve (c) does not intersect with curve (a). Therefore, an SP cannot be excited
directly by incident light in air. For the dispersion of photons in metal [curve (b)], the SP
dispersion curve (c) intersects with curve (b) at resx ,κ . In this case, an SP is generated by
momentum matching.
Figure 1.3 Dispersion relations: (a) light in air, (b) photon in metal, and (c) SPP [31].
13
Bulk plasmons and SPs have been measured in carbon-based materials using EELS.
The EELS spectrum, which was developed by Hillier and Baker in the 1940s [33], is a
measure of the dielectric response of a film to external electromagnetic excitation.
Typical EELS spectrum is shown in Figure 1.4. Here the larger peaks at multiples of 15.3
eV are from bulk plasmons and the smaller peaks at multiples of 10.3 eV are from SPs.
Mkhoyan et al. [34] reported that the dominant mechanism for energy loss in GO is SP
excitation for specimens less than 5 nm thickness.
Figure 1.4 Typical EELS spectrum [34].
14
1.5 Surface Plasmon Resonance Biosensors
The prism configurations proposed by Otto and Kretschmann are shown in Figure 1.5.
In the Otto configuration, there is a gap between the metal and the TIR surface containing
a lower reflective index medium. The Otto configuration is generally used for SPR
studies of solid phase media. In the Kretschmann configuration, the metal is located
directly on top of the TIR surface without any gap between them. The Kretschmann
configuration is frequently used for SPR measurement because it creates plasmons more
efficiently than the Otto configuration.
In the evanescent field formed at the TIR surface, the electromagnetic field amplitude
decreases exponentially with the distance from the TIR surface.
(a) (b)
Figure 1.5 Prism configurations: (a) Otto configuration and (b) Kretschmann
configuration [25, 26].
15
SPR occurs at the critical angle (or resonance angle) when there is momentum
matching between the evanescent wave and incident light. The resonance angle,
sometimes called the Kretschmann angle ( spθ ), at which SP occurs can be obtained using
equation (1.3).
adm
adm
spssp εε
εε
c
ωθnκκ
+
+
+=sin= 0 (1.3)
where 0κ , sn and spθ represent the wave number in free space, reflective index of the
prism, and angle of incidence at resonance, respectively. ad +ε represents the target
permittivity.
The electrical and chemical properties of the metal used in SPR are very important
because SPR uses the evanescent wave in the metal, and the SP must propagate along the
surface of the metal. Noble metals such as Au, Ag, and Pt are frequently used because of
their good electrical and optical properties. Because of its biocompatibility and stability, a
gold thin film is the most commonly used for biological applications.
However, gold does not easily functionalize and immobilize molecules, which makes is
unsuitable for application in a biosensor [35]. Graphene and GO have recently received
much attention as platform materials for biosensors [36]. Gold and graphene (or GO) are
compared in Table 1.4. Graphene has many advantages in its electrical, thermal, and
optical properties but limited by difficulties in functionalizing, patterning, and mass
production. On the other hand, GO has the advantages of water dispersibility and feasible
mass production despite its poor conductivity.
16
Table 1.4 Comparison of gold and graphene-related materials.
Material Advantages Disadvantages
Gold � Chemical stability
� Good optical properties � Difficult functionalization
Graphene
� Superior electrical,
thermal and optical
properties
� Difficult functionalization
� Difficult patterning
� Problems with mass production
Graphene
oxide
� High affinity to water
� Mass production � Poor conductor
An SPR biosensor is a device that combines biosensing and SPR phenomenon. The
measurement principle for using an antibody–antigen interaction is shown in Figure 1.6.
If the concentration of a protein changes at a fixed location, it will appear as a change in
the refractive index, which corresponds to mass loading of the protein. A 1 2/mmng
surface coverage of biomolecules is known to generate a refractive index change of
3101 −× RIUs, which corresponds to an SPR resonance angle shift of °10.0 [37, 38].
Since the late 1980s, many attempts have been made to improve the sensitivity of
SPR biosensors. The hybridization of nanotechnology and sensor technology has been
accelerating the development of a biosensor with high sensitivity. These techniques
include the use of nanostructures (e.g., nanoislands [39], nanowires [40], nanogaps [6], a
nanograting [41], and nanoholes [42]), NPs (e.g., nanorods [43], AuNPs [44]), carbon-
based materials (CNT, graphene, and GO) [45-47], optical fiber [48], and cantilever
structures [49].
17
(a) (b)
(c) (d)
Figure 1.6 SPR measurements using protein interaction: (a) before protein interaction,
(b) SPR measurement result before protein interaction, (c) after protein interaction, and
(d) SPR measurement result after protein interaction [50].
18
1.6 Sensor Application of Graphene Oxide
GO is an intermediate material obtained during the fabrication of graphene from
graphite by a chemical oxidation and reduction method, as shown in Figure 1.7 [51, 52].
GO has great potential for application in SPR biosensors because of its superior
characteristics such as good water dispersibility, high mechanical strength, facile surface
modification and sp2/sp3 existing structure. In terms of structure, GO has many
negatively charged functional groups such as hydroxy groups, carboxylic acids, and
epoxides, as shown in Figure 1.8.
Figure 1.7 Comparison of graphite and graphene-related materials [53].
19
Figure 1.8 Structure of GO [53].
20
Therefore, the application of GO to biosensors is expected to enhance the performance
of SPR biosensors.
Table 1.5 lists recent studies on the application of GO to biosensors. Liu et al. [54]
and Loh et al. [36] used GO as a sensing platform for optical applications. Zhang et al.
[10], Huang et al. [11], and Mao et al. [55] reported results related to the hybridization
of a GO sheet and AuNPs.
Table 1.5 Recent studies of GO biosensors.
Year Author Title Ref.
2013 H. Zhang
et al. A novel graphene oxide-based surface plasmon
resonance biosensor for immunoassay [10]
2013 C. F.
Huang et
al.
Graphene oxide capped gold nanoparticles based SPR
biosensors [11]
2012 F. Liuet al. A sensitive graphene oxide-DNA based sensing
platform for fluorescence “turn-on” detection of
bleomycin [54]
2010 K. P. Loh
et al. Graphene oxide as a chemically tunable platform for
optical applications [36]
2010 S. Mao et
al.
Specific protein detection using thermally reduced
gaphene oxide sheet decorated with gold
nanoparticle-antibody conjugates [55]
2010 F. Liu et
al.
Graphene oxide arrays for detecting specific DNA
hybridization by fluorescence resonance energy
transfer [56]
2010 J. H. Jung
et al. A graphene oxide based immuno-biosensor for
pathogen detection [57]
21
To apply graphene or GO in a biosensor, deposition and patterning are important
challenges to be met. Self-assembly and direct assembly methods are generally used to
deposit molecules on the sensor surface, as shown in Table 1.6. In this study, a GO film is
deposited on the gold surface using the Langmuir-Blodgett (LB) methods, one of the
direct assembly methods.
Table 1.6 Comparison of assembly methods.
Assembly Method Definition Ref.
Self-
assembly self-assembled
monolayer Single layers of organic molecules are
chemically adsorbed onto gold substrates [50,
58]
Direct-
assembly
LB
One or more mono-layers of material are
deposited from a liquid surface onto a solid
substrate by dipping the substrate through a
floating monolayer at a constant molecular
density
[59,
60]
layer-by-layer Alternating layers of oppositely charged
materials are deposited with wash steps in
between. (e.g., dip-coating, spin-coating,
spray-coating)
[61,
62]
1.7 Surface Functionalization
To enhance the performance of a biosensor, it is important to improve the analyte
22
binding and selectivity characteristics on the sensor surface. This requires a detailed
strategy in the design stage of an experiment. This strategy must include a solution for the
activation and immobilization methods, and assay format. The immobilization method
depends on the type of BRE and the specifics of the application. Typical immobilization
methods are shown in Figure 1.9 [63]. The covalent methods use amine (e.g. lysine), thiol
(cysteine), or aldehyde (carbohydrate) functional groups on proteins [64].
Amine coupling, a typical method, consists of the following three steps. In the first step,
activation, a highly active surface that reacts with amine and other nucleophilic groups on
proteins is created by adding activation agents such as 1-ethyl-3-[(3-
(dimethylamino)propyl]carbo-diimide hydrochloride (EDC) and N-hydroxysuccinimide
(NHS). In the second step, coupling, the protein is injected into a pre-concentration buffer,
thereby realizing high protein concentrations and driving the coupling interaction. In the
third step, blocking, the remaining activated carboxymethyl groups are blocked by
injecting very high concentrations of ethanolamine.
There are four types of assay formats, direct [65], sandwich [66], displacement [67],
and indirect competitive assay [68], as shown in Figure 1.10. For molecular-level analysis
and diagnosis, sandwich immunoassay formats are preferred.
For the application of graphene or GO to biosensors, the development of an optimal
functionalization method is important. To date, two approaches to the development of
functionalization, the covalent and non-covalent methods, are under study [69-71]. In
covalent methods, the oxygen functional group on the GO surface, carboxylic acid groups
at the edge and epoxy/hydroxyl groups on the basal plane are used to immobilize
molecules. In non-covalent methods, weak interactions such as the p-p interaction, van
der Waals interactions, and electrostatic interactions are used to immobilize molecules.
23
(a)
(b)
(c)
(d)
Figure 1.9 Typical immobilization methods: (a) covalent (amine, thiol, aldehyde), (b)
capture (streptavidin-biotin), (c) capture (antibody-base), and (d) monolayer attachment
[63].
24
(a)
(b)
(c) (d)
Figure 1.10 Typical assay formats: (a) sandwich immunoassay, (b) indirect inhibition
immunoassay, (c) protein-labeled inhibition immunoassay, and (d) direct small
molecule immunoassay [72].
25
Chapter 2
Performance Evaluation and
Numerical Analysis
26
2.1 Sensor Performance Evaluation
The important characteristics of commercial SPR biosensors are the sensitivity ( S ),
resolution (σ ), and LOD. Their correlation can be expressed by equation (2.1) [73, 74].
S
σLOD = (2.1)
Here the resolution (σ ) is the smallest variation in the sensor input that produces a
detectable change in the sensor output. The LOD is the smallest quantity of variation in
the reflective index of an analyte (converted into a concentration) that can be detected by
a sensor. Improving the sensitivity is helpful for lowering the LOD.
It is not easy to evaluate the performance of a sensor from an SPR curve directly. The
figure of merit (FOM) and sensitivity are generally used to determine the sensor
performance [75]. The FOM is often used because direct calculation of the sensitivity is
difficult. The FOM is defined as in equation (2.2)
FWHM(eV)
SFOM = (2.2)
The sensitivity of an SPR biosensor is defined as the ratio of the change in the sensor
output (e.g., the resonant angle, wavelength, phase, intensity, or polarization) to the
change in the sensor input (e.g., refractive index, concentration) [76, 77]. The sensitivity
of an SPR biosensor can generally be decomposed into two components as shown in
equation (2.3)
surfRIRI
RI
SSi
(n)n
n
P
i
PS =
∂∂
∂∂
=∂∂
= (2.3)
Here P is the measured change in the sensor output (resonant angle) and i is the change
in the sensor input (concentration). From equation (2.3), RIS denotes the intrinsic
27
optical sensitivity (the ratio of the resonance angle shift to the change in the refractive
index of the sensor platform) and surfS is the effectiveness of the surface
functionalization (the ratio of the change in the refractive index of the sensor platform to
the amount of specific binding of the molecules on the sensor surface).
2.2 Theoretical Approach to Sensitivity
The sensitivity is proportional to the change in an overlap integral (OI) in the
perturbation theory as follows [7, 8]:
∫−−
)dVr)f(rε( (2.4)
where ε(r) and f(r) are the spatial distribution of the target permittivity and normalized
evanescent fields, respectively, in three-dimensional space.
To date, there have been many theoretical attempts to explain the origin of sensitivity
enhancement in SPR biosensors. Abdulhalim et al. [7] proposed that the change in the
wave vector ( kδ ) is proportional to the ratio of the OI of 2
Eδε ⋅ in the interaction
volume after molecular interaction to the OI of 2
Eε ⋅ in the overall volume before
molecular interaction. It is given by
drEEε
drEEδε
2
k≈δkiv i
rV ii r
∫∫
(2.5)
Here kδ is the shift in the wave vector due to molecular interaction and δε is the
change in the analyte dielectric constant due to binding of the analyte on the sensor
28
surface. Further, ik and iE are the wave vector and electrical field before the
molecular interaction occurs, and rE and r are the field after the molecular interaction
and the thickness, respectively.
The optical signature is defined as the intensity of the signal measured at the sensor as
a result of an interaction of biomolecules. For SPR biosensors, the optical signature is
described as the OI between the near-field surface wave and biomolecules. In this study,
the OI is defined as follows [78]:
dr(r)Eε(r)OI(1)2
x∫= (2.6)
dr(r)Hε(r)OI(2)2
y∫= (2.7)
dr(r)Eε(r)OI(3)2
z∫= (2.8)
drE(r)ε(r)OI(4)2
∫= (2.9)
dr(r)Sε(r)OI(5) x∫= (2.10)
dr(r)Sε(r)OI(6) z∫= (2.11)
drS(r)ε(r)OI(7)2
∫= (2.12)
Here )(rε is the spatial change in the dielectric constants, which depends on the
distribution of biomolecules.
Considering appropriate integral limits, the OI for thin-film-based detection is given by
dz(z)E(z)εdz(z)E(z)εOI(1)2
4x
∞d 4
2
3x
d
0 33
3
∫∫ += (2.13)
Here, z = 0 refers to the metal surface [79].
29
2.3 Numerical Analysis
To date, various methods have been developed to calculate the electromagnetic fields
for photonic application. This includes FDTD method and RCWA. The simulation
methods are compared in Table 2.1. In this study, RCWA and the transfer matrix method
(TMM) were used.
Table 2.1 Comparison of simulation methods used in photonics
Simulation
Method Description Ref.r.
FDTD
� Major photonics analysis tool
� Space is divided into small meshes. Electric and
magnetic fields in each mesh are solved by Maxwell’s
equations.
[80]
RCWA � Space is transformed using Fourier transform method
� Only periodic structure can be analyzed [81]
TMM
� Used to obtain the propagation characteristics, which
include losses for various modes of an arbitrarily
graded planar waveguide and multilayered structure
[82]
For multilayered structures, TMM has often been used to obtain the resonance angle
and SPR curve. In TMM, it is assumed that each layer is stacked along the z-axis and the
tangential electrical field is continuous at the interface as shown in Figure 2.1.
30
Figure 2.1 N-layer stacked structure
To generate an SPR curve, a generalized N-layer model was used, as shown in Figure
2.1. The SPR curve can be obtained by calculating the following TMM formalism [83,
84].
=
−
−
1N
1N
1
1
H
EM
H
E (2.14)
where 1E and 1H are the tangential components of the electric and magnetic fields,
respectively, at the boundary of the first layer. 1−NE and 1−NH are the corresponding
fields at the boundary of the thN layer. Here, M is the characteristic matrix of the
combined structure and given by
M =Χ1
2
−
=
n
k
kM =
22
12
21
11
M
M
M
M for k = 2, 3, 4……N-1 (2.15)
with
31
−
−=
k
kk
kk
k
k
q
iqM
β
β
β
β
cos
/sin
sin
cos (2.16)
k
k
k
k
kk
nq
εθε
θµε )sin(
cos 1
22
1−=
= (2.17)
( )k
k
kk
nd
εθε
λπ
β 1
22
1 sin2 −
= (2.18)
where kd , kε , and kn are the medium thickness, dielectric constant and refractive
index, respectively, of the kth layer.
2
222111211
2221112112
)()(
)()(
NN
NNpp
qMMqqMM
qMMqqMMrR
++++−+
== (2.19)
If the reflectance ( pR ) is drawn as a function of the incident angle ( )θ , the angle
corresponding to the minimum reflectance ( minR ) becomes the resonance angle.
Therefore, the sensitivity can be obtained by using the OI after 2)(zEx is calculated
from the result of the TMM.
32
Chapter 3
Study on the Correlation
Between Field-Target Overlap and
Sensitivity of SPR Biosensors
33
3.1 Introduction
Fluorescence-based-labeling detection methods perform fairly well in terms of
sensitivity and accuracy. However, they have the possibility of label interference and
limitations on real-time monitoring and sample preparation. Therefore label-free
detection methods such as SPR have received much attention. The main limitation of SPR
biosensors is the low sensitivity because they do not use label materials such as
fluorescent dyes.
Many experimental results have demonstrated sensitivity improvement, but the
mechanism behind the enhancement has not been explained clearly. Many studies have
been performed to clarify the relation between the field enhancement and sensitivity.
Table 3.1 lists recent papers that explained the sensitivity enhancement theoretically. In
2010, Shalabney et al. first proposed that the correlation between the field enhancement
and sensitivity enhancement agrees with the OI [7, 8]. In 2011, Kim et al. [86] verified
the correlation for two-dimensional nanogratings. Therefore, it is clear that the
effectiveness of the field-target overlap as a measure of the detection sensitivity is more
useful than originally suggested.
34
Table 3.1 Recent theoretical approaches to clarifying the sensitivity of SPR sensor.
Year Author Title Ref.
2012 Y. S. Ryu
et al.
Correlation study between field-target overlap and
sensitivity of SPR biosensors [79]
2012 W. J. Lee
and D. Kim
Field-matter integral overlap to estimate the
sensitivity of SPR biosensors [85]
2011 N. H. Kim
et al.
Correlation analysis between plasmon field
distribution and sensitivity enhancement in
reflection- and transmission-type localized SPR
biosensors
[86]
2010
I.
Abdulhalim
et Al.
Biosensing configurations using guided wave
resonant structures” among Optical Waveguide
Sensing and Imaging, NATO Science for Peace and
Security Series B, Physics and Biophysics, edited by
W. J. Bock, I. Gannot, and S. Tanev (Springer-
Verlag)
[7]
2010
A.
Shalabney
et al.
Electromagnetic fields distribution in thin film
structure and the origin of sensitivity enhancement
in SPR sensors [8]
In this study, I investigated and confirm the correlation between the field-target overlap
and SPR detection experimentally. To measure the resonance angle shift, simple thin
film-based SP structures and sandwich interactions of h-IgG and a-h-IgG were used. Hoa
et al. [37] and Geddes et al. [87] also used sandwich assays to investigate the sensitivity
enhancement of SPR and fiber-optic biosensors, respectively. The sensitivity was
theoretically obtained by numerical calculation using the OI and the Abeles TMM. The
sensitivity was experimentally obtained from the measured SPR curves. The correlation
between the field-target overlap and sensitivity of SPR biosensors was studied by
comparing the theoretical and experimental sensitivity.
35
3.2 Numerical Simulation
Figure 3.1 shows the schematic model used in this study. Sandwich and reverse
sandwich immunoassays are shown in Figure 3.1(a) and (b), respectively. Here IgG and
a-h-IgG are used as the antigen and antibody, respectively. Human immunoglobulin plays
an important role in allergic reactions and related diseases. Immunoassays can be useful,
for example, in the diagnosis of HIV, which causes AIDS [88]. Immunosensors that use
immunoglobulin as a bioreceptor show high selectivity and sensitivity, so their
importance in biomedicine is increasing.
For the substrate, SF10 prism glass was used. For application in the biosensing
platforms, a 50-nm-thick gold film was evaporated on the SF10 glass. The optical
constants of glass and gold were referenced from [46, 89]. All the layers in the model
were assumed to be homogeneous and optically isotropic effective media. The refractive
indices of h-IgG and a-h-IgG are reportedly in the range of 1.41-1.49 [47, 48, 90, 91]. In
this study, they are assumed to be 1.41. The thicknesses of the h-IgG and a-h-IgG layers
were assumed to be 7 and 9 nm, respectively [88]. In addition, the effective refractive
index of the buffer was the same as that of water, 1.33. The light source was modeled to
have a wavelength of λ = 632.8 nm and p-polarization.
36
(a) (b)
Layer
Thickness (nm)
Refractive Index
Real (n) Imag. (k)
Gla substra e 1.72 0
Gold 50 0.198 3
h-IgG 7 1.41 0
a-h-IgG 9 1.41 0
Buffer - 1.33 0
(c)
Figure 3.1 Schematic model for numerical calculation: (a) sandwich immunoassay. (b)
reverse sandwich immunoassay, and (c) parameter values.
37
3.3 Experimental Methods
As a target molecular interaction, immunoassay of h-IgG (antigen) and a-h-IgG
(antibody) was used in this study. In the sandwich assay, the experiment was performed
using the procedure shown in Figure 3.2. In the reverse sandwich assay, all the
procedures were the same except for the sequence of h-IgG and a-h-IgG. H-IgG purified
from serum and a-h-IgG (Fc-specific) from antiserum were purchased from Sigma-
Aldrich (St. Louis, MO, USA). A gold film (thickness: 50 nm) was deposited on an
SF10 prism substrate by thermal evaporation.
The immobilization strategy used in this experiment was first developed by Lyon et al.
[92] and consists of four steps: surface activation, protein coupling, blocking, and
protein coupling. The first step is to activate the surface. The gold films were modified
by 3-mercaptopropanoic acid (MPA) with 10 mM ethanolic solutions for 30 min. The
carboxylate-modified (MPA-coated) surfaces were prepared to covalently attach
proteins to the gold film via traditional carbodiimide coupling to protein free amine
moieties. An active ester at the surface was formed by reacting 100 µL of a 100 mM, pH
5.5, 1-ethyl-3-[3·(dimethylamino)propyl] carbodiimide hydrochloride (EDC) solution
with the carboxylated surface for 15 min. A 50 µL aliquot of Sulfo-N,
hydroxysuccinimide (S-NHS: 40 mM, pH 7) was then injected into the flow cell to
stabilize the reactive surface by displacing the imide moiety. After 15 min of incubation
with S-NHS, the cell was rinsed with 1 mL of buffer (85 mM phosphate, pH 7.4). The
first SPR curve was measured. The second step is to couple the protein. A-h-IgG
solution (1.0 mg/mL) was injected for 30 min and the second SPR curve was measured.
The third step is to block the unreacted sites. The surface was then treated with a 10
38
mg/mL solution of bovine serum albumin (BSA) to block the unreacted sites on the
surface to eliminate possible non-specific adsorption, followed by measurement of the
third SPR curve. The final step is to couple the protein. Immunochemical interaction
with h-IgG was performed for 30 min, followed by the fourth SPR measurement. After
another immunochemical interaction to form the sandwich configuration, the fifth SPR
curve was measured. All the SPR curves were measured in the buffer condition.
39
Figure 3.2 Experimental procedure for sandwich immunoassays.
40
3.4 Optical Set-up
For SPR measurement of the sandwich immunoassay interactions, an angle scanning
SPR detection instrument was used. The arrangement is shown schematically in Figure
3.3. The angle-scanning SPR detection system consists of two concentric motorized
rotation stages (URS75PP, Newport, Irvine, CA) for a sample mount and a detector arm
to implement θ-2θ detection, where a light source is fixed as the sample mount and the
detector arm rotate by θ and 2θ, respectively. Light from a He-Ne laser (20 mW, λ =
632.8 nm, Melles-Griot, Carlsbad, CA, USA) was first p-polarized to illuminate a sample
index-matched to an SF10 prism substrate. The photocurrent was measured by a p-i-n
photodiode (818-UV, Newport, Irvine, CA, USA) and fed to a low-noise lock-in
amplifier (SR830DSP, Stanford Research Technology Inc., Sunnyvale, CA, USA). The
lock-in amplifier was synchronized with a chopper for intensity modulation. The
measurement procedure was fully controlled by computer.
41
Figure 3.3 Schematic of SPR detection system.
3.5 Results and Discussion
Figures 3.4 and 3.5 show the results of SPR measurement for the sandwich and reverse
sandwich interaction, respectively. For the sandwich interaction, the resonance angle shift
is 1.9°. For the reverse sandwich interaction, the resonance angle shift is 1.23°. Both
assays show positive resonance angle shifts.
42
(a)
Measurement Step θsp (deg) ∆ θsp (deg)
SPR (1) Bare-gold 59.82 ± 0.013 -
SPR (2) a-h-IgG 60.39 ± 0.010 0.57 ± 0.02
SPR (3) BSA blocking 60.43 ± 0.027 0.045 ± 0.01
SPR (4) IgG 60.81 ± 0.0089 0.38 ± 0.01
SPR (5) a-h-IgG 61.72 ± 0.010 0.91 ± 0.02
(b)
Figure 3.4 Measured results for sandwich immunoassays: (a) measured SPR curves, (b)
resonance angle and angle shifts.
43
(a)
Measurement Step θsp (deg) ∆ θsp (deg)
SPR (1) Bare-gold 58.93 ± 0.025 -
SPR (2) IgG 59.43 ± 0.007 0.50 ± 0. 3
SPR (3) BSA blocking 59.47 ± 0.0013 0.04 ± 0.02
SPR (4) a-h-IgG 60.08 ± 0.027 0.61 ± 0.04
SPR (5) IgG 60.16 ± 0.026 0.08 ± 0.05
(b)
Figure 3.5 Measured results for reverse sandwich immunoassays: (a) measured SPR
curves, (b) resonance angle and angle shifts.
44
Figure 3.6 shows the resonance angle shifts that were measured (yellow bar, ∆θsp
experiment) and calculated (green bar, ∆θsp theory) (right axis) and the normalized
differences in the optical signatures (left axis). The resonance angle and optical signature
increase as the protein interaction progresses: bare gold, after adsorption of h-IgG, and
following the interaction of h-IgG and a-h-IgG. This is because of the increase in the
intensity of the surface electric field with increasing refractive index and interaction layer
thickness. Equation (2.13) shows that the optical signature increases with increasing
electric field and thickness. Figure 3.6 reveals that ∆θsp (theory) and the OIs are almost
perfectly correlated. Further, the result for ∆θsp (experiment) is smaller than that of ∆θsp
(theory). This difference may result from suboptimal interaction of h-IgG and a-h-IgG
caused by the limited interaction efficiency.
45
(a)
(b)
Figure 3.6 Normalized differences in optical signatures (left axis) and resonance angle
shifts that were calculated theoretically and measured experimentally (right axis): (a)
reverse sandwich and (b) sandwich assay.
46
Figure 3.7 shows the correlation between the optical signature defined in equations
(2.6)-(2.12) and the resonance angle shift and detection sensitivity. The correlation
coefficient R was calculated from Pearson’s correlation coefficient. From Figure 3.7, all
optical signatures, OI(1)-OI(7), show correlation coefficients R greater than 0.998 and
0.97 for the theoretical and experimental results, respectively. In the correlation with the
theoretical results presented in Figure 3.7(a), the reverse sandwich assays show a higher
correlation than the sandwich assays. This is likely because the sandwich assays are
thicker than the reverse sandwich assays because the thicknesses of a-h-IgG and h-IgG
are assumed to be 9 and 7 nm, respectively.
47
(a)
(b)
Figure 3.7 Correlation coefficients: with (a) theoretical results and (b) experimental data.
Filled squares (■) and open circles (○) represent reverse sandwich and sandwich assays,
respectively.
48
Figure 3.8 shows the xE , yH and zE profiles for the sandwich assays. The
figure shows that a standing wave is formed on the glass and enhanced at the gold layer
before it decreases at the interaction and buffer layer. Further, xE , yH and zE did
not vary on the substrate and metal, and most of the variations occurred at the interaction
and buffer layer. Figure 3.8 shows that the near-field distribution affects the sensitivity of
SPR biosensors.
49
Figure 3.8 Calculated near-field distribution in sandwich assay. Inset magnifies xE in
the binding layers and ambient buffer near the interface. The fields were calculated
assuming unit incident electric field amplitude. yH is normalized by the vacuum
characteristic impedance 2/1
00 )/( εµη = .
50
3.6 Summary
The correlation between the near-field overlap and detection sensitivity was
investigated using thin-film-based SPR measurement of immunoassay interactions. In a
theoretical study, Abeles TMM analysis was used to acquire the resonance angle shift,
and the OI was used to calculate the optical signature. The resonance angle shifts were
also measured experimentally. Both sandwich and reverse sandwich assays showed a
positive resonance angle shift. The correlation between the calculated and measured
results was investigated by introducing the correlation coefficients, which were calculated
from Pearson’s correlation coefficient. All the optical signatures, OI(1)-OI(7), showed
correlations that exceed 0.998 and 0.97 for the theoretical and experimental results,
respectively. The reverse sandwich assays showed a higher correlation with the
theoretical results than the sandwich assays.
In conclusion, a strong correlation was found between field-target overlap and
detection sensitivity. The correlation coefficients exceeded 95% in all the cases that were
considered. From the near-field distribution, it is clear that the optical signature can be an
important tool for estimating the sensitivity of SPR biosensors.
51
Chapter 4
Study on the Detection
Sensitivity of Graphene Oxide-
Coupled SPR for Immunoassays
52
4.1 Introduction
A gold surface has often been used as a platform for biosensors, but it has relatively
poor adsorption with biomolecules. For this reason, there have been numerous attempts to
improve the adsorption properties of gold surfaces. Graphene and GO have been
considered recently as an alternative to a gold surface. Graphene is a single layer of sp2
carbon atoms in a two-dimensional honeycomb lattice and has attracted much attention
because of its high conductivity and fast electron transfer rate. GO is an intermediary
obtained during the conversion of graphite to graphene.
Many studies have been performed to apply graphene and GO to SPR biosensors.
Table 4.1 lists recent studies on SPR biosensors based on graphene-related materials.
From 2010 to 2012, according to reports from Kumar, Verma, and Choi et al., gold or
silver on graphene contributed to enhanced sensitivity in SPR biosensors [84, 95, 96]. In
2013, Zhang et al. [10] and Huang et al. [11] reported that GO or GO-capped AuNPs
contributed to an increase in the sensitivity of SPR biosensors. In 2012, Salihoglu et al.
measured the protein adsorption and desorption on a graphene surface in real-time [93].
Wu et al. explained that GO plays an important role in the enhancement of sensitivity
as follows [9]: (1) GO has a large surface area and contains carboxyl groups, which are
helpful for immobilizing antibodies on the sample surface and detect more antigens. (2)
GO modifies the propagation constant of the SPP and increases the refractive index.
53
Table 4.1 Recent studies in SPR biosensors based on graphene-related materials.
Year Author Title Ref.
2013 H. Zhang et
al. A novel graphene oxide-based surface plasmon
resonance biosensor for immuoassays [10]
2013 C. F. Huang
et al. Graphene oxide and dextran capped gold
nanoparticles based SPR biosensors [11]
2012 E. Wijaya et
al. Graphene-based high performance SPR biosensors [35]
2012 O. Salihoglu
et al. Plasmon-polaritons on graphene-metal surface and
their use in biosensors [93]
2012 P. Kumar Graphene on gold, sensitivity calculation [94]
2011 R. Verna Graphene on gold, sensitivity calculation [84]
2010 S. H. Choi
et al. Graphene on silver, sensitivity calculation [95]
2010 L. Wu et al. Graphene on gold, sensitivity calculation [9]
In this study, I investigated the effect of GO on the sensitivity of SPR detection by
modulating the coupling of plasmonic fields to GO. A SiO2 spacer was also employed to
modulate the degree of plasmonic coupling between metal and GO, as shown in Figure
4.1(a). It is expected that the resonance shift may decrease because the plasmonic
coupling field weakens with distance from the SPs as a result of an interaction that occurs
on the SiO2 spacer. The change in the plasmonic coupling with the thickness of the
dielectric spacer may improve the characteristics of GO in SPR detection.
The optical properties of GO-coupled SPR detection were evaluated by measuring the
sandwich immunoassay of h-IgG and a-h-IgG. The shifted resonance angle was obtained
54
from the numerical calculation using the Abeles TMM, which confirmed a correlation of
more than 97% between the optical signature calculated with the Abeles TMM and the
measured resonance angle in Chapter 3. To investigate the near-field distribution on the
surface, an RCWA calculation was used.
4.2 Numerical Simulation
The schematic model used in this experiment is presented in Figure 4.1. For the
substrate, SF10 prism glass was used. For the biosensing platforms, a 50-nm-thick gold
film was evaporated on the SF10 glass. To simplicity, a chrome layer was ignored in the
numerical calculation. The optical constants of glass and gold are the same as in
Chapter.3. All the layers in the model were assumed to be homogeneous and of optically
isotropic effective media. The refractive index of GO was taken to be 1.7 + i0.17 [96].
The other refractive indices used in this simulation and the calculation method used for
the axial near-field distribution are the same as in Chapter 3.
55
(a)
Layer
Thickness (nm)
Refractive Index
Real (n) Imag. (k)
Glass substrate 1.72 0
Gold 50 0.198 3
Graphene Oxide variable 1.7 0.17
h-IgG 7 1.41 0
a-h-IgG 9 1.41 0
Buffer - 1.33 0
(b)
Figure 4.1 Schematic model for numerical calculation: (a) schematic structure and (b)
parameter values.
56
4.3 Experimental Methods
Figure 4.2 shows the experimental procedure used in this study. First, chrome
(thickness: 2 nm) and gold (thickness: 50 nm) layers were evaporated on the SF10 prism
substrate sequentially. A SiO2 layer was deposited on the gold surface by sputtering. The
deposition thickness of the SiO2 layer was in the range of 0-100 nm. A GO layer was
deposited on the SiO2/Au film with LB assembly. The surface of the deposited GO layer
was observed in SEM and AFM images. The LB machine and GO solution used in this
experiment are presented in Figure 4.3: they were purchased from KSV NIMA in Finland
[97] and Graphene Labs in the USA [98], respectively. The flake size of GO was
determined to be in the range of 0.5-5 µm.
To measure the shift of the resonance angle with the protein interaction, the sandwich
assay interaction of IgG/ a-h-IgG/ IgG was performed, as shown in Figure 4.4. After the
GO was rinsed with 1 mL of phosphate-buffered saline (PBS, 85 mM phosphate, pH 7.4),
the SPR measurement was performed. MPA was not coated on the GO because GO has
its own carboxyl group on the surface. An active surface was formed by reacting 100 µL
of a 100 mM, pH 5.5, 1-ethyl-3-[3·(dimethylamino)propyl]carbodiimide hydrochloride
(EDC) solution with the GO surface and a 50 µL aliquot of Sulfo-N, hydroxysuccinimide
(S-NHS: 40 mM, pH 7) for 10 min. The cell was rinsed with 1 mL of PBS (85 mM
phosphate, pH 7.4). Rinsing was performed before each protein interaction. On millimeter
of a-h-IgG solution (120 µg/mL) was injected at 0.1 mL/ min to form an immunoassay
interaction on the GO surface. One millimeter of a bovine serum albumin solution (BSA,
10 mg/mL) was injected to block the unreacted chemical moieties, eliminating noise from
nonspecific bindings. One milliliter of h-IgG (1 mg/mL) was injected at 0.1 mL/min to
57
form protein coupling. Another 1 mL of a-h-IgG solution was then injected to form the
sandwich immunoassay configuration. After the GO was rinsed with 1 mL of PBS (85
mM phosphate, pH 7.4), the SPR measurement was performed.
Figure 4.2 Experimental procedure for sandwich immunoassay
58
(a)
(b)
Figure 4.3 Deposition material and machine: (a) GO solution (Graphene Labs., USA)
[97] and (b) LB machine (KSV NIMA, Finland) [98].
59
(a)
(b)
(c)
(d)
(e)
Figure 4.4 Schematic of sandwich antibody-antigen interaction: (a) carboxylate-
modification: (b) antibody binding of a-h-IgG, (c) reaction blocking of BSA treatment, (d)
antigen binding of h-IgG, and (e) subsequent antibody binding of a-h-IgG.
60
4.4 Results and Discussion
Figure 4.5(a) and (b) show SEM and AFM images of a sample after GO deposition,
respectively. The inset in Figure 4.5(a) shows a picture of the sample. Gray GO flakes
were observed in the SEM image. The AFM height profile is shown in Figure 4.5(c).
According to Cote et al. [59], and Li [60], a monolayer of graphite was coated by using
an LB assembly. Contrary to expectation, the deposited GO was much thicker than a
monolayer GO and is likely to be one to three layers thick. GO films are known to
typically be deposited as a monolayer in a single coating using LB deposition. The greater
thickness may reflect non-uniform GO deposition (i.e., overlapping of flakes) using the
LB method and large size deviations of the flakes within the GO solution.
61
(a)
(b)
(c)
Figure 4.5 SEM and AFM images of deposited GO: (a) SEM image (the inset shows
sample shape), (b) AFM image, and (c) AFM height profiles of GO surface.
62
Figure 4.6(a) shows the measured resonance angle shift in GO-coupled SPR detection
with samples of dielectric thickness 2SiOt = 0, 10, 20, 30, and 100 nm. The measured
resonance angle shift is largest without the SiO2 spacer (2SiOt = 0) and decreases as the
SiO2 thickness increases. Figure 4.6(b) shows the FWHM in GO-coupled SPR detection
with samples of dielectric thickness 2SiOt = 0, 10, 20, 30, and 100 nm. The FWHM was
calculated from the measured SPR curve. It is smallest without the SiO2 spacer (2SiOt =
0) and increases with increasing thickness of the dielectric spacer. It can be thought that
the plasmonic fields are more weakly overlapped and the optical signature is reduced with
a thicker dielectric spacer [99]. On the other hand, the performance (i.e., sensitivity) of a
sensor is inversely proportional to the FWHM, as implied in equation (2.2). Therefore, it
can be estimated that the sensitivity of GO-coupled SPR detection may decrease with
increasing SiO2 thickness.
63
(a)
(b)
Figure 4.6 Measurement results: (a) measured resonance angle shift and (b) FWHM
versus thickness of SiO2 spacer.
64
Figure 4.7 shows the measured resonance angle shift for metal-enhanced SPR detection
(i.e., without GO) and GO-coupled SPR detection. Here a SiO2 spacer was not used. The
resonance angle shift for GO-coupled SPR detection is 0.08° larger than that for metal-
enhanced SPR detection. Therefore, it is expected that the deposition of GO on the gold
surface may contribute to enhancing the sensitivity of an SPR biosensor.
Figure 4.7 Measured resonance angle shift for metal-enhanced SPR detection (without
GO) and GO-coupled SPR detection. Here a SiO2 spacer was not used.
65
Figure 4.8 shows the relative resonance shift (RRS) according to the SiO2 layer
thickness. Here, the RRS is defined as the ratio of the resonance angle shift with GO to
the resonance angle shift without GO at the same SiO2 layer thickness, i.e., RRS
= (w/o)GOGO θ/∆θ ∆ . The resonance angle shift for GO and Au was obtained from the results
of the calculation. The black and red lines represent the RRS values of GO and gold,
respectively. The RRS curve of gold is presented for comparison with the GO curve. The
RRS in this case is defined as (w/o)AuAu θ/∆θ ∆ . When there is no SiO2 spacer (2SiOt = 0
nm), the RRS is 1.24 for GOt = 10 nm. In other words, the resonance shift is enhanced
by 24%, compared to the case without a GO layer. In Figure 4.8, the RRS for
gold/dielectric (SiO2) spacer/dielectric (GO) is much smaller than that for gold/dielectric
(SiO2) spacer/metal (Au). This is the result of amplification of the plasmonic fields
between the two gold layers. For Figure 4.7, the RRS at 2SiOt = 0 nm was measured to
be RRS = 0.69/0.61 = 1.13, in good agreement with the theoretical results.
66
Figure 4.8 Relative resonance shift (RRS) calculated for GO-coupled and metal-
enhanced SPR detection versus thickness of SiO2 spacer. Arrows show the increase in GO
and gold layer thickness.
67
Figure 4.9 shows the RRS normalized by that of a spacer-free (2SiOt = 0 nm) structure.
The black and red lines represent the results of calculations for GO and gold, respectively.
The blue squares show the experimental results, and the blue line is the result of fitting
the experimental data to a quadratic polynomial (R = 0.95118). The RRS decreased more
abruptly for GO-coupled SPR than for metal-enhanced SPR detection. For GO-coupled
SPR, a thinner GO increased the RRS. In contrast, for the metal-enhanced structure, a
thinner metal layer reduced the RRS. This can be explained by localization of the
plasmonic fields, as shown in Figure 4.9. For GO-coupled SPR detection, the plasmonic
fields are localized between the SiO2/gold interface and the GO. For the metal-enhanced
structure, the plasmonic field is enhanced in the interface between the coupling and
interaction layers. The experimental results show decreasing RRS as the thickness of the
SiO2 spacer increases, which is in fairly good agreement with the theoretical results for a
SiO2 spacer thickness of 0-10 nm. The difference between the calculated (black) and
measured (blue) data increases as the thickness of the SiO2 spacer increases. In addition,
GO-coupled SPR detection shows a larger standard deviation than that observed in
conventional SPR detection. This may reflect the nonuniform deposition of GO flakes
during LB deposition and large variation in GO flakes size (i.e., 0.5-5 µm), as shown in
Figure 4.3 [99]. The deviation decreases for a thicker spacer because the SiO2 spacer has
a great effect than the GO flakes.
68
Figure 4.9 Normalized RRS from simulation and experiment: simulation result for GO-
coupled SPR detection (black line), and metal-enhanced SPR detection (red line). Also
shown are experimental results for GO-coupled detection (blue squares).
69
The near-field profiles were simulated to analyze the far-field results, as shown in
Figure 4.10, which shows the near-field profiles of the tangential field amplitude xE . For
GO-coupled SPR detection, the near-field intensity decrease as the thickness of the GO
increases. The reason is the non-zero attenuation in the refractive index of GO. For the
metal-enhanced SPR structure, the near-field intensity also decreases as the thickness of
the GO increases, which is related to the field enhancement associated with the coupling
of the SP formed in the metal-dielectric interfaces. The RRS for the metal-enhanced
structure is larger than that of GO-coupled SPR detection. This is related to the significant
increase in the fields in the coupling layer. The ratio between the slopes,
z(GO)/δE/z(gold)/δE xx δδ , in the coupling layer is approximately 17 and 2 at 2SiOt
= 40 and 80 nm, respectively. Therefore, for metal-enhanced SPR detection, stronger
field enhancement and larger resonance shifts can be obtained.
70
Figure 4.10 Tangential near-field amplitude profiles of xE calculated for GO-coupled
SPR detection: 2SiOt = (a) 0, (b) 40, and (c) 80 nm. The field amplitude was normalized
by that of an incident light field. Metal-enhanced SPR detection of sandwich
immunoassays: 2SiOt = (d) 0, (e) 40, and (f) 80 nm. Thickness of GO and gold: 0-10 nm.
SI denotes sandwich interaction.
71
4.5 Summary
The detection characteristics of a GO-coupled SPR structure were investigated
numerically and experimentally for a sandwich immunoassay. A SiO2 spacer was
deposited on gold surfaces by sputtering, and GO was deposited on the gold and SiO2
spacer surfaces by the LB assembly method. It was found that in GO-coupled SPR
detection, the resonance shifts decrease and the FWHM increases as the thickness of the
SiO2 spacer increases. The resonance angle shift for GO-coupled SPR detection was
0.08° larger than that for metal-enhanced SPR detection. The RRS for gold/dielectric
(SiO2) spacer/dielectric (GO) was much smaller than that for gold/dielectric (SiO2)
spacer/metal (Au). The difference in RRS is due to the different amplification of the
plasmonic fields between two metals and between a metal and a dielectric, as shown in
Figure 4.10.
Experimentally, the resonant angle shift for GO-coupled SPR detection was enhanced
by at least 13% compared to that of metal-enhanced SPR detection. The experimental
results showed a decrease in the RRS as the thickness of the SiO2 increases, which was in
good agreement with theoretical results for a SiO2 spacer thickness of 0-10 nm. GO-
coupled SPR detection showed a larger standard deviation than that observed in
conventional SPR detection. This may reflect the nonuniform deposition of GO during
LB deposition and large variation in the GO flake size. The resonant angle shift for GO-
coupled SPR detection is expected to be enhanced if the deposition conditions in the LB
method are optimized and more uniform GO flakes are used.
72
Chapter 5
Conclusion
73
5.1. Conclusion
This thesis investigated methods of enhancing the detection sensitivity of biosensors
based on SPR. As our society ages, the number of patients suffering from chronic
diseases is rapidly increasing. As food and environmental pollution become severe, new
viral diseases such as SARS, FMD, and AI, have occurred frequently. To treat these
diseases properly, early detection and diagnosis is of utmost importance. For this purpose,
the development of high-sensitivity biosensors is urgently required.
The important points made in this thesis are as follows:
1) The SPR immunosensor, which uses an antibody and antigen as a bioreceptor, has
received much attention because of its good performance in terms of sensitivity,
specificity and speed. For the assay formats, sandwich immunoassays are preferred
because of their advantage in molecular-level detection and diagnosis.
2) The plasmon phenomenon can be analyzed with EELS. A strong peak appears in
association with bulk plasmons and a smaller peak beside the larger peak is caused by
an SP. By comparing the intensities of bulk plasmons and surface plasmons, the
dominant mechanism for energy loss within carbon-based materials can be analyzed.
3) To enhance the sensitivity of SPR biosensors, it is important to understand the
mechanism behind the sensitivity. A correlation study between the field-target overlap
and detection sensitivity of SPR biosensors demonstrated a strong correlation
between the overlap and the detection sensitivity. Both theoretically and
experimentally, the correlation coefficients exceeded 95% in all the cases that were
considered.
74
4) Reverse sandwich assays showed a stronger correlation than sandwich assays. The
overlap effect is more prominent for reverse sandwich assays than for sandwiches
assays because of the larger thickness of the former.
5) The calculated near-field distribution revealed a correlation between the near-field
distribution and the sensitivity of SPR biosensors. The enhancement in the sensitivity
may be produced by localization of the near-field distribution.
6) In an experimental study of GO-coupled SPR detection, the resonance shift decreases
and FWHM increases as the thickness of the SiO2 spacer increases owing to the
decrease in the field overlap with GO.
7) GO-coupled SPR detection enhances the resonant shift by at least 13%
experimentally compared to that of conventional SPR detection; i.e., a shift of 113%
would be obtained by thin-film-based SPR detection.
8) The experimental results show a decrease in the RRS as the thickness of the SiO2
increases, which is in good agreement with the theoretical results. GO-coupled SPR
detection shows a larger standard deviation than conventional SPR detection. This
may reflect non-uniform deposition of GO and large variations in the GO flake size.
This thesis is the first attempt to calculate and measure the detection sensitivity of an
SPR biosensor containing GO deposited using an LB assembly. This thesis confirmed the
possibility of applying GO to the SPR biosensor. The dissertation will contribute to the
development of a high-performance SPR biosensor that can detect small molecules, in
particular, at low concentrations.
75
5.2. Future Work
The future challenges for SPR biosensors that need to be addressed can be summarized
as follows:
1) The deposition of GO on the sensor surface is expected to contribute to the enhanced
sensitivity. For reproducible deposition of a GO film on the gold surface,
optimization of the LB deposition and preparation of a good GO solution with small
uniform GO flakes are important factors.
2) An optimal functionalization strategy for the gold surface has been determined, yet an
optimal functionalization strategy for GO needs to be developed. For biosensor
applications of GO, the development of a functionalization strategy is another
important issue to be resolved.
3) Hybridization will be helpful for enhancing the performance of SPR biosensors. A
combination of GO flakes and AuNPs will yield enhanced detection sensitivity.
4) With the advent of the era of the “Internet of Things,” the need for smart biosensors
is rapidly increasing. There is a demand for the development of high-performance
biosensors that satisfy requirement, such as low power consumption, small size,
portability, sensing, and wireless networking.
76
References
[1] D. R. Shankaran, K. V. Gobi, and N. Miura, “Recent advancements in surface
plasmon resonance immunosensors for detection of small molecules of
biomedical, food and environmental interest,” Sensor and Actuators B:
Chemical, 121(1), pp. 158-177, 2007.
[2] L. He, M. D. Musick, S. R. Nicewarner, F. G. Salinas, S. J. Benkovic, M. J.
Natan, and C. D. Keating, “Colloidal Au-enhanced surface plasmon resonance
for ultrasensitive detection of DNA hybridization,” Journal of the American
Chemistry Society, 122(38), pp. 9071-9077, 2000.
[3] Y. Sun and Y. Xia, “Increased sensitivity of surface plasmon resonance of gold
nanoshells compared to that of gold solid colloids in response to environmental
changes,” Analytical Chemistry, 74(20), pp. 5297-5305, 2002.
[4] K. M. Byun, S. J. Kim, and D. Kim, “Design study of highly sensitive nanowire
enhanced surface plasmon resonance biosensors using rigorous coupled wave
analysis,” Optics Express, 13(10), pp. 3737-3742, 2005.
[5] L. Malic, B. Cui, T. Veres, and M. Tabrizian, “Enhanced surface plasmon
resonance imaging detection of DNA hybridization on periodic gold nanoposts,”
Optics Letters, 32(21), pp.3092-3094, 2007.
[6] Y. Oh, W. Lee, Y. Kim, and D. Kim, "Self-aligned colocalization of 3D
plasmonic nanogap arrays for ultra-sensitive surface plasmon resonance
detection," Biosensors and Bioelectronics, 51C, pp. 401-407, 2014.
[7] I. Abdulhalim, “Biosensing configurations using guided wave resonant
structures” among Optical Waveguide Sensing and Imaging, NATO Science for
Peace and Security Series B, Physics and Biophysics, edited by W. J. Bock, I.
Gannot, and S. Tanev (Springer-Verlag).
[8] A. Shalabney and I. Abdulhalim, “Electromagnetic fields distribution in
multilayer thin film structures and the origin of sensitivity enhancement in
surface plasmon resonance sensors,” Sensors and Actuators A: Physical, 159(1),
pp. 24-32, 2010.
[9] L. Wu, H. S. Chu, W. S. Koh, and E. P. Li, “Highly sensitive graphene
biosensors based on surface plasmon resonance,” Optics Express, 18(14), pp.
14395-14400, 2010.
[10] H. Zhang, Y. Sun, S. Gao, J. Zhang, H. Zhang, and D. Song, “A novel graphene
oxide-based surface plasmon resonance biosensor for immunoassay,” Small,
9(15), pp. 2537-2540, 2013.
[11] C. F. Huang, G. H. Yao, R. P. Liang, and J. D. Qiu, “Graphene oxide and dextran
capped gold nanoparticles based surface plasmon resonance sensor for sensitive
detection of concanavalin A,” Biosensors and Bioelectronics, 50(15), pp. 305-
77
310, 2013.
[12] H. Ma, D. Wu, Z. Cui, Y. Li, Y. Zhang, B. Du, and Q. Wei, “ Graphene-based
optical and electrochemical biosensors: a review,” Analytical Letters, 46(1), pp.
1-17, 2013.
[13] L. Clark Jr. and C. Lyons, “Electrode systems for continuous monitoring in
cardiovascular surgery,” Annals of the New York Academy Sciences, 102(1), pp.
29-45, 1962.
[14] D. R. Thevenot, K. Toth, R. A. Durst, and G. S. Wilson, “Electrochemical
biosensors: recommended definitions and classification,” International Union of
Pure and Applied Chemistry, 71(12), pp. 2333-2348, 1999.
[15] T. Vo-Dinh and B. Cullum, “Biosensors and biochips: advances in biological and
medical diagnostics”, Fresenius’ Journal of Analytical Chemistry, 366(6-7), pp.
540-551, 2000.
[16] M. N. Velasco-Garcia, “Optical biosensors for probing at the cellular level: a
review of recent progress and future prospects,” Seminars in Cell &
Developmental Biology,” 20(1), pp. 27-33, 2009.
[17] D. W. Kimmel, G. LeBlanc, M. E. Meschievitz, and D. E. Cliffel,
“Electrochemical sensors and biosensors,” Analytical Chemistry, 84(2), pp. 685-
707, 2012.
[18] M. A. Cooper and V. T. Singleton, “A survey of the 2001 to 2005 quartz crystal
microbalance biosensor literature: applications of acoustic physics to the analysis
of biomolecular interactions,” Journal of Molecular Recognition, 20, pp.154-184,
2007.
[19] K. Ramanathan and B. Danielsson, “Principles and applications of thermal
biosensors,” Biosensors and Bioelectronics, 16(6), pp.417-423, 2001.
[20] K. E. Gonsalves ,C. L. Laurencin, C. R. Halberstdt, and L. S. Nair, “Biomedical
Nanostructure,” John Wiley & Sons, Hoboken, New Jersey, 2008.
[21] X. Fan, I. M. White, S. I. Shopova, H. Zhu, J. D. Suter, and Y. Sun, “Sensitive
optical biosensors for unlabeled targets: a review,” Analytical Chimica Acta,
620(1-2), pp. 8-26, 2008.
[22] S. Ray, G. Mehta, and S. Srivastava, “Label-free detection techniques for protein
microarrays: prospects, merits and challenges,” Proteomics, 10(4), pp. 731–748,
2010.
[23 ] R. W. Wood, “On a Remarkable Case of uneven distribution of light in a
diffraction grating spectrum,” Philosophical Magazine Series 6, 4(21), pp. 396-
402, 1902.
[24] U. Fano, “The theory of anomalous diffraction gratings and of quasi-stationary
waves on metallic surfaces (Sommerfeld's Waves),” Journal of the Optical
Society of America, 31(3), pp. 213-222, 1941.
[25] A. Otto, “Excitation of surface plasma waves in silver by the method of
frustrated total reflection,” Z. Physik.(Leipzig), 216, pp. 398-410, 1968.
[26] E. Kretschmann and H. Raether, “Radiative decay of non-radiative surface
78
plasmons excited by light,” Z. Naturforsch., 23A, pp. 2135-2136, 1968.
[27] B. Liedberg, C. Nylander, and I. Lundström, “Surface plasmon resonance for gas
detection and biosensing,” Sensors and Actuators, 4, pp. 299-304, 1983.
[28] M. Malmqvist, “Biospecific interaction analysis using biosensor technology,”
Nature, 361(6408), pp. 186-187, 1993.
[29] Reference website for Biacore SPR systems (GE Healthcare Life Sciences,
http://www.biacore.com/lifesciences/)
[30] S. Maier, Plasmonics: Fundamentals and Applications, Springer, U.K., 2007.
[31] A. V. Zayats, I. I. Smolyaninov, A. A. Maradudin, “Nano-optics of surface
plasmon polaritons,” Physics Reports, 408(3-4), pp. 131-314, 2005.
[32] W. P. Hall, S. N. Ngatia, and R. P. Van Duyne, “LSPR biosensor signal
enhancement using nanoparticle-antibody conjugates,” Journal of Physical
Chemistry, 115(5), pp. 1410-1414, 2011.
[33] J. Hillier and R. F. baker, “Microanalysis by means of electrons,” Journal of
Applied Physics, 15(9), pp. 663-675, 1944.
[34] K. A. Mkhoyan, A. W. Contryman, J. Silcox, D. A. Stewart, G. Eda, C. Mattevi,
S. Miller, and M. Chhowalla, “Atomic and electronic structure of graphene-
oxide,” Nano Letters, 9(3), pp. 1058-1063, 2009.
[35] E. Wijaya, N. Maalouli, R. Boukherrroub, S. Szunerits, and J. P. Vilcot,
“Graphene-based high-performance surface plasmon resonance biosensors,”
Proceeding of SPIE, 8424, pp. 84250R/ 1-7, 2012.
[36] K. P. Loh, Q. L. Bao, G. Eda, and M. Chhowalla, “Graphene oxide as a
chemically tunable platform for optical applications,” Nature Chemistry, 2, pp.
1015-1024, 2010.
[37] X, D, Hoa, A. G. Kirk, and M. Tabrizian, “Towards integrated and sensitive
surface plasmon resonance biosensors: A review of recent progress,” Biosensors
and Bioelectronics, 23(2), pp. 151-160, 2007.
[38] J. Homola, S. S. Yee, and G. Gaulitz, “Surface plasmon resonance sensors:
review,” Sensors and Actuators B, 54(1-2), pp. 3-15, 1999.
[39] H. Yu, K. Kim, K. Ma, W. Lee, J. W. Choi, C. O. Yun, and D. Kim, “Enhanced
Detection of virus particles by nanoisland-based localized surface plasmon
resonance,” Biosensors and Bioelectronics, 41, pp. 249-255, 2013.
[40] K. Kim, S. J. Yoon, and D. Kim, “Nanowire-based enhancement of localized
surface plasmon resonance for highly sensitive detection: a theoretical study,”
Optics Express, 14(25), pp. 12419-12431, 2006.
[41] K. Ma, D. J. Kim, K. Kim, S. Moon, and D. Kim, “Target-localized nanograting-
based surface plasmon resonance detection toward label-free molecular
biosensing,” IEEE Journal of Selected Topics in Quantum Electronics, 16(4), pp.
1004-1014, 2010.
[42] H. F. Ghaemi, T. Thio, T. D. E. Grupp, T. W. Ebbesen, and H. J. Lezec, “Surface
plasmons enhance optical transmission through subwavelength holes,” Physical
Review B, 58(11), pp. 6779-6782, 1998.
79
[43] X. Wang, Y. Li., H. Wang., Q. Fu., J. Peng, Y. Wang, J. Du, Y. Zhou, and L.
Zhan, “Gold nanorod-based localized surface plasmon resonance biosensor for
sensitive detection of hepatitis B virus in buffer, blood serum and plasma,”
Biosensors and Bioelectronics, 26(2), pp. 404-410, 2010.
[44] X. Cao, Y. Ye, and S. Liu, “Gold nanoparticle-based signal amplification for
biosensing,” Analytical Biochemistry, 417(1), pp. 1-16, 2011.
[45] R. J. Chen, H. C. Choi, S. Bangsaruntip, E. Yenilmez, X. Tang, Q. Wang, Y. L.
Chang, and H. Dai, “An investigation of the mechanisms of electronic sensing of
protein adsorption on carbon nanotube devices,” Journal of the American
Chemical Society, 126(5), pp. 1563-1568, 2004.
[46] K. Maehashi and K. Matsumoto, “Label-free electrical detection using carbon
nanotube-based biosensors,” Sensors, 9, pp. 5368-5378, 2009.
[47] N. F. Chiu, T. Y. Huang, C. C. Kuo, W. C, Lee, M. H. Hsieh, and H. C. Lai,
“Single-layer graphene based SPR biochips for tuberculosis bacillus detection,”
Proceedings of SPIE, 84273M, pp. 84273M/ 1-7, 2012.
[48] T. Vo-Dinh and Y. Zhang, “Single-cell monitoring using fiberoptic
nanosensors,” Wiley Interdisciplinary Reviews: Nanomedicine and
Nanobiotechnology, 3(1), pp. 79-85, 2011.
[49] J. Fritz, M. K. Baller, H. P. Lang, H. Rothuizen, P. Vettiger, E. Meyer, H. J.
gerber, and J. K. Gimzewski, “Translating biomolecular recognition into
nanomechanics,” Science, 288(5464), pp, 316-318, 2000.
[50 ] Y. Zhou, C.W. Chiu, and H. Liang, “Interfacial structures and properties of
organic materials for biosensors: an overview,” Sensors, 12, pp. 15936-15062,
2012.
[51] S. Park and R. S. Ruoff, “Chemical methods for the production of graphenes,”
Nature Nanotechnology, 4, pp. 1-8, 2009.
[52] Y. Zhu, S. Murali, W. Cai, X. Li, J. W. Suk, J. R. Potts, and R. S. Ruoff,
“Graphene and grapheme oxide: synthesis, properties, and applications,”
Advanced Materials, 22(35), pp. 3906-3924, 2010.
[53] D. R. Dreyer, S. Park, C. W. Bielawski, and R. S. Ruoff, “The chemistry of
graphene oxide," Chemical Society Reviews, 39, pp. 228-240, 2010.
[54] Y. Liu, X. Dong, and P. Chen, “Biological and chemical sensors based on
graphene materials,” Chemical Society Reviews, 41(6), pp. 2283-2307, 2012.
[55] S. Mao, G. Lu, K. Yu, Z. Bo, and J. Chen, “Specific protein detection Using
thermally reduced graphene oxide sheet decorated with gold nanoparticle-
antibody conjugates,” Advanced Materials, 22(32), pp. 3521-3526, 2010.
[56] F. Liu, J. Y. Choi, and T. S. Seo, “Graphene oxide array for detecting specific
DNA hybridization by fluorescence resonance energy transfer,” Biosensors and
Bioelectronics, 25 (10), pp. 2361-2365, 2010.
[57] J. H. Jung, D. S. Cheon, F. Liu, K. B. Lee, and T. S. Seo, “A Graphene oxide
based immuno-biosensor for pathogen detection”, Angewandte Chemie
International Edition, 49(33), pp. 5708-5711, 2010.
80
[58] D. Samanta and A. Sarkar, “Immobilization of bio-macromolecules on self-
assembled monolayers: methods and sensor applications,” Chemical Society
Reviews, 40(5), pp. 2567-2592, 2011.
[59] L. J. Cote, F. Kim, and J. Huang, “Langmuir-Blodgett assembly of graphite
oxide single layers,” Journal of the American Chemical Society, 131(3), pp.
1043-1049, 2009.
[60] X. Li, G. Zhang, X. Bai, X. Sun, X. Wang, E. Wang, and H. Dai, “Highly
conducting graphene sheets and Langmuir–Blodgett films,” Nature
Nanotechnology, 3, pp. 538-542, 2008.
[61] J. Shen, Y. Hu, C. Li, C. Q, M. Shi, and M. Ye, “Layer-by-layer self-assembly of
graphene nanoplatelets,” Langmuir, 25(11), pp. 6122-6128, 2009.
[62] J. S. Park, S. M. Cho, W.J. Kim, J. H. Park, and P. J. Yoo, “Fabrication of
graphene thin films based on layer-by-layer self-assembly of functionalized
graphene nanosheets,” ACS Applied Materials & Interfaces, 3(2), pp. 360-368,
2011.
[63] GE Healthcare, Biacore sensor surface handbook, BR-1005-71 Ed. AB.
[64] S. Lofas, B. Johnsson, A. Edström, A. Hansson, G. Lindquist, R. M. M. Hillgren,
and L. Stigh, “Methods for site controlled coupling to carboxymethyldextran
surfaces in surface plasmon resonance sensors,” Biosensors and Bioelectronics,
10(9-10), pp.813-822, 1995.
[65] S. Sonezaki, S. Yagi, E. Ogawa, and A. Kondo, “Analysis of the interaction
between monoclonal antibodies and human hemoglobin (native and crosslinked)
using a surface plasmon resonance (SPR) biosensor,” Journal of Immunological
Methods, 238(1-2), pp. 99-106, 2000.
[66] J. H. Gu, H. Lu, Y. W. Chen, L. Y .Liu, P. Wang, J. M. Ma, and Z. H. Lu,
“Enhancement of the sensitivity of surface plasmon resonance biosensor with
colloidal gold labeling technique,” Supramolecular Science, 5(5-6), pp. 695-698,
1998.
[67] V. I. Chegel, Y. M. Shirshov, E. V. Piletskaya, and S. A. Piletsky, “Surface
plasmon resonance sensor for pesticide detection,” Sensors and Actuators B:
Chemical, 48(1-3), pp. 456-460, 1998.
[68] D. R. Shankaran, K. Matsumoto, K. Toko, and N. Miura, “Development and
comparison of two immunoassays for the detection of 2, 4, 6-trinitrotoluene
(TNT) based on surface plasmon resonance,” Sensors and Actuators B:
Chemical, 114(1), pp. 71-79, 2006.
[69] T. Kuila, S. Bose, A. K. Mishra, P. Khanra, N. H. Kim, and J. H. Lee, “Chemical
functionalization of graphene and its applications,” Progress in Materials
Science, 57(7), pp. 1061-1105, 2012.
[70] Y. Wang, Z. Li, J. Wang, J. Li, and Y. Lin, “Graphene and graphene oxide:
biofunctionalization and applications in biotechnology,” Trends in
Biotechnology, 29(5), pp. 205-212, 2011.
[71] D. Chen, H. Feng, and J. Li, “Graphene oxide: preparation, functionalization, and
81
electrochemical applications,” Chemical Reviews, 112(11), pp. 6027-6053, 2012.
[72] J. Mitchell, “Small molecule immunosensing using surface plasmon resonance,”
Sensors, 10, pp. 7323-7346, 2010.
[73] J. Homola, Surface plasmon resonance (SPR) sensors, Springer Ser Chem Sens
Biosens, 4, pp. 45-67, Springer-Verlag Berlin Heidelberg, 2006.
[74] S. K. Roh, T. R, Chung, and B. H. Lee, “Overview of the characteristics of
micro- and nano-structured surface plasmon resonance sensors,” Sensors, 11(2),
pp.1565-1588, 2011.
[75] L. J. Sherry, S. H. Chang, G. C. Schatz, and R. P. Van Duyne, “Localized surface
plasmon resonance spectroscopy of single silver nanocubes,” Nano Letters,
5(10), pp. 2034-2038, 2005.
[76] J. Homola, “Surface plasmon resonance sensors for detection of chemical and
biological species,” Chemical Reviews, 108(2), pp.462-493, 2008.
[77] E. Wijaya, C. Lenaerts, S. Maricot, J. Hastanin, S. Habraken, J-P. Vilcot, R.
Boukherroub, and S. Szunerits, “Surface Plasmon resonance based biosensors:
from the development of different SPR structure to novel surface functionlization
strategies,” Current Opinions in Solid State and Materials Science, 15, pp.208-
224, 2011.
[78] Y. Ryu, T. Son, and D. Kim, "Near−field evaluation of surface plasmon
resonance biosensor sensitivity based on the overlap between field and target
distribution," Korean Journal of Optics and Photonics, 24(2), pp. 86-91, 2013.
[79] Y. Ryu, S. Moon, Y. Oh, Y. Kim, and D. Kim, “An experimental correlation
study between field–target overlap and sensitivity of surface plasmon resonance
biosensors based on sandwiched immunoassays,” Optics Communications,
285(21-22), pp. 4626-4631, 2012.
[80] A. Teflove, Advanced in Computational Electrodynamics: The Finite-Difference
Time-Domain Method, 3rd edition, Artech House Publishers: Norwood, MA,
U.S.A, 2005.
[81] M. G. Moharam and T. K. Gaylord, “Rigorous coupled-wave analysis of metallic
surface-relief gratings,” Journal of the Optical Society America A, 3(11), pp.
1780-1787, 1986.
[82] C. C. Katsidis and D. I. Siapkas, “General transfer-matrix method for optical
multilayer systems with coherent, partially coherent, and incoherent
interference,” Applied Optics, 41(19), pp. 3978-3987, 2002.
[83] A. K. Ghatak and K. Thyagarajan, Optical Electronics, Cambridge University
Press, Cambridge, U.K., 1989.
[84] R. Verma, B. D. Gupta, and R. Jha, “Sensitivity enhancement of a surface
plasmon resonance based biomolecules sensor using graphene and silicon
layers,” Sensors and Actuators B: Chemical, 160(1), pp. 623-631, 2011.
[85] W. Lee and D. Kim, “Field-matter integral overlap to estimate the sensitivity of
surface Plasmon resonance biosensors,” Journal of the Optical Society of
America A, 29(7), pp. 1367-1376, 2012.
82
[86] N. H. Kim, W. K. Jung, and K. M. Byun, “Correlation analysis between plasmon
field distribution and sensitivity enhancement in reflection- and transmission-
type localized surface plasmon resonance biosensors,” Applied Optics, 50(25),
pp. 4982-4988, 2011.
[87] N. J. Geddes, A. S. Martin, F. Caruso, R. S. Urquhart, D. N. Furlong, J. R.
Sambles, K. A. Than, and J. A. Edgar, “Immobilisation of IgG onto gold surfaces
and its interaction with anti-IgG studied by surface plasmon resonance,” Journal
of Immunological Methods, 175(2), pp.149-160, 1994.
[88] M. Vestergaard, K. Kerman, and E. Tamiya, “An overview of label-free
electrochemical protein sensors,” Sensors, 7(12), pp. 3442-3458, 2007.
[89] E. D. Palik, Handbook of Optical Constants of Solids, Academic Press, Orlando,
FL., U.S.A. 1985.
[90] J. Vörös, “The density and refractive index of adsorbing protein layers,”
Biophysical Journal, 87(1), pp. 553-561, 2004.
[91] C. Preininger, H. C. Schaumann, A. Ahluwalia, and D. de Rossi,
“Characterization of IgG Langmuir–Blodgett films immobilized on
functionalized polymers,” Talanta, 52(5), pp. 921-930, 2000.
[92] L. A. Lyon, M. D. Musick, and M. J. Natan, “Colloidal Au-enhanced surface
plasmon resonance immunosensing,” Analytical Chemistry, 70(24), pp. 5177-
5183, 1998.
[93] O. Salihoglu, S. Balci, and C. Kocabas, “Plasmon-polaritons on graphene-metal
surface and their use in biosensors,” Applied Physics Letter, 100(21), pp.
213110/1-5, 2012.
[94] P. K. Maharana and R. Jha, “Chalcogenide prism and graphene multilayer based
surface plasmon resonance affinity biosensor for high performance,” Sensors and
Actuators B: Chemical, 169(5), pp. 161-166, 2012.
[95] S. H. Choi, Y. L. Kim, and K. M. Byun, “Graphene-on-silver substrates for
sensitive surface plasmon resonance imaging biosensors,” Optics Express, 19(2),
pp. 458-466, 2011.
[96] Y. Wan, Y. Wang, J. Wu, and D. Zhang, “Graphene oxide sheet-mediated silver
enhancement for application to electrochemical biosensors,” Analytical
Chemistry, 83(3), pp. 648-653, 2011.
[97] Supplier information for GO solution (http://www.graphenelab.com).
[98] Supplier information for LB machine (http://www.biolinscientific.com/ksvnima).
[99] Y. Ryu, S. Moon, Y. Oh, Y. Kim, T. Lee, D. H. Kim, and D. Kim, "Effect of
coupled graphene oxide on the sensitivity of surface plasmon resonance
detection," Applied Optics, 53(7), pp. 1419–1426, 2014.
83
Publication & Presentation Lists
[1] Yeonsoo Ryu, Seyoung Moon, Youngjin Oh, Yonghwi Kim, Taewoong
Lee, Dong Ha Kim, and Donghyun Kim, "Effect of coupled graphene oxide
on the sensitivity of surface plasmon resonance detection," Applied Optics,
53(7), 1419–1426, 2014.
[2] Yeonsoo Ryu, Taehwang Son, Donghyun Kim, "Near−field evaluation of
surface plasmon resonance biosensor sensitivity based on the overlap
between field and target distribution," Korean Journal of Optics and
Photonics, 24(2), 86-91, 2013.
[3] Yeonsoo Ryu, Seyoung Moon, Youngjin Oh, Yonghwi Kim, and Donghyun
Kim, "An experimental correlation study between field-target overlap and
sensitivity of surface plasmon resonance biosensors based on sandwiched
immunoassays," Optics Communications, 285(21-22), 4626–4631, 2012.
[4] Donghag Choi, Yeonsoo Ryu, Youngbum Lee, SeDong Min and
Myoungho Lee, “Accuracy comparison of motor kmagery performance
evaluation factors using EEG based brain computer interface by
neurofeedback effectiveness,” Journal of Biomedical Engineering
Research, 32, 295-304, 2011.
[5] Donghag Choi, Yeonsoo Ryu, Youngbum Lee, and Myoungho Lee,
“Performance evaluation of a motor-imagery based EEF-Brain computer
interface using a combined cue with heterogeneous training data in BCI-
Naive subjects,” BioMedical Engineering Online, 10:91, 2011.
[6] Yeonsoo Ryu, Seyoung Moon, Youngjin Oh, Yonghwi Kim, Taewoong
Lee, and Donghyun Kim, "Graphene oxide coupled sandwiched
immunoassays based on surface plasmon resonance biosensing," The 21st
Korean Conference on Semiconductors, Hanyang University, February 26,
2014.
[7] Yeonsoo Ryu, Youngbum Lee, Chungeun Lee, Byungwoo Lee, Jinkwon
Kim and Myoungho Lee, “Comparative analysis of the optimal
performance evaluation for motor imagery based EEG-brain computer
interface,” 5th Kuala Lumpur International Conference on Biomedical
Engineering, June 23, 2011.
[8] Yeonsoo Ryu, Youngbum Lee, Wanjin Jeong, Sangjoon Lee, Daehoon
Kang, and Myoungho Lee, “Cross evaluation for characteristics of motor
imagery using neuro-feedback based EEG-brain computer interface,” 5th
Kuala Lumpur International Conference on Biomedical Engineering, June
23, 2011.
84
국문초록
면역에세이 적용 그래핀 산화물 기반
표면 플라즈몬 공명 측정
연세대학교 대학원
전기전자공학과
류 연수
우리 사회가 노령화 시대로 진입함에 따라 치매, 심혈관 질환, 고혈압,
암과 같은 만성질환으로 고통받는 환자의 수가 급격하게 증가하고 있다. 최근
중증 급성 호흡기 증후군, 구제역, 조류독감 등 여러 종류의 분자크기
바이러스에 의해서 발생하는 새로운 바이러스성 질병이 자주 발생되고 있다.
초기 단계에서 이러한 질병을 진단하고 치료하기 위해서는 낮은 분자크기의
타겟 물질을 감지하고 모니터링하는 것이 과거 어느 때보다 중요해졌다.
이러한 수요를 만족시켜주기 위해서 고성능 바이오센서의 개발이 요구되었다.
표면 플라즈몬 공명 바이오센서는 표지물질을 사용하지 않고 실시간으로
반응을 감지할 수 있기 때문에 많은 주목을 받고 있지만 아직까지 2킬로달론
미만의 작은 분자 감지와 분자간 반응 모니터링에는 어려움이 있다.
본 논문에서 바이오센서와 감지의 종류, 표면 플라즈몬 현상, 그래핀
산화물 응용, 표면 기능화와 감도 등 표면 플라즈몬 공명 바이오센서의
개발에 관계되는 중요한 이슈들을 간단히 조사하였다. 본 논문은 또한 두
가지 연구를 다루고 있다. 첫 번째는 장-타겟 중첩과 표면 플라즈몬 공명의
85
감도간의 상관관계 연구에 대한 것이다. 두 번째는 그래핀 산화물 기반 표면
플라즈몬 공명 감지와 금속 기반 표면 플라즈몬 공명 감지간의 비교 연구에
대한 것이다.
지금까지 수행된 많은 실험에서 감도향상 결과를 발표하였지만 감도 향상
메커니즘에 대하여는 명확하게 설명되지 못하였다. 따라서 표면 플라즈몬
공명의 감지감도와 분자의 근접 장 중첩과 관련된 광학적 자취간의
상관관계를 조사하였다. 샌드위치형태의 항체-항원 반응과 역 샌드위치의
상호작용이 공명 각 변화를 측정하기 위하여 사용되었다. 휴먼 면역글로불린
G분자와 앤티 휴먼 면역글로불린 G분자가 항원과 항체로서 사용되었다.
바이오센서의 감지감도는 근접장에서의 장-타겟 중첩간의 상관관계에 의하여
연구되었다. 연구결과 중첩과 감지감도간에는 이론적, 실험적으로 강한
상관관계가 존재함이 밝혀졌으며 상관계수는 모든 경우에 95% 이상으로
나타났다.
금 표면은 바이오센서의 플랫폼으로 자주 사용되었으나 생체분자와의
흡착성이 나쁜 단점이 있다. 가임 교수가 2010년 노벨 물리학상을 수상한
이래 우수한 전기적, 광학적 특성을 가진 그래핀에 대하여 많은 연구가
진행되고 있다. 그래핀 산화물은 흑연에서 그래핀을 제조할 때 얻어지는
중간물질이다. 그래핀 산화물은 물과 우수한 분산성, 높은 기계적 강도,
용이한 표면처리와 sp2/sp3가 존재하는 구조와 같은 우수한 특성을 보유하고
있기 때문에 표면 플라즈몬 공명 바이오센서 응용에 높은 가능성을 가지고
있다.
86
종래에는 표면 플라즈몬 공명 바이오센서의 광학적 플랫폼 재료로서 금
표면이 자주 사용되어 왔지만 분자물질과의 흡착특성이 나쁜 단점이 있었다.
본 연구의 목적은 금 표면에 그래핀 산화물 층을 코팅하여 표면 플라즈몬
공명 바이오센서의 감도를 향상키는 것이다. 최근 그래핀과 그래핀 산화물은
표면 플라즈몬 공명 바이오센서의 감지특성 향상을 위한 독특한 전기적,
광학적 특성을 보유하고 있기 때문에 많은 주목을 받고 있다. 본 연구에서 금
표면에 실리콘 산화물과 그래핀 산화물을 증착한 후 그래핀 산화물 기반 표면
플라즈몬 공명의 감지감도를 측정하였다. 바이오센서의 공명각 변이를
이용하여 그래핀 산화물 기반 표면 플라즈몬 공명 감지와 종래의 표면
플라즈몬 공명 감지를 실험적으로 비교하였다. 그래핀 산화물 기반 표면
플라즈몬 공명 감지가 종래의 표면 플라즈몬 공명 감지보다 113% 높게
나타났다. 이 결과는 랑뮤르-블로젯 조립 방법에 의하여 증착된 그래핀
산화물기반 표면공명감지 바이오센서의 감지감도를 계산하고 측정한 첫 번째
시도이다. 본 논문에서 표면 플라즈몬 공명 바이오센서에 그래핀 산화물의
적용 가능성을 확인하였다.
본 연구의 결과는 저농도에서, 작은 크기 분자를 감지할 수 있는 고성능
표면 플라즈몬 공명 바이오센서의 개발에 기여하게 될 것이다.
핵심어: 표면 플라즈몬 공명, 바이오센서, 그래핀 산화물, 랭뮤르-블로젯,
감도
