Malondialdehyde (MDA) is the commonly accepted biomarker of lipid peroxidation. We reported the surface-enhanced Raman (SERS) detection of MDA using Thiobarbituric acid (TBA) as a molecular probe. The lowest concentration of HPLC purified TBA-MDA adduct that can be determined with reasonable signal to noise ratio is 0.45 nM. The specificity of the SERS technique has been demonstrated by comparing the SERS spectrum of TBA-MDA adduct with other TBA-aldehyde adducts. As a small organosulfur compound, TBA exhibits tremendous structural complexity. Discussed in this thesis is the drastic pH and concentration dependence of TBA SERS spectral features. To understand the origins of the TBA SERS spectral variations, UV-Vis spectra of TBA were also acquired under same experimental conditions of SERS. Density function calculations (DFT) were performed for different TBA tautomers with different charge states to facilitate the SERS spectral interpretation, allowing us to speculate the type of tautomers dominating the nanoparticle surfaces.

Benzoyl peroxide (BPO) is a common bleaching agent used in wheat flour. Due to its ability to damage existing nutrients in food and potential adverse effect to health, BPO have been strictly banned as a food additive in several countries and regions, such as China and Europe. However, the United States specifies that BPO is generally recognized as safe (GRAS). So, the WHO/FAO created a Codex Alimentarius Commission (CAC) to regulate the international BPO usage standard. According to the CAC, it is restricted at 75 mg/kg or parts per million (ppm). BPO is very unstable and easily converts to benzoic acid (BA), which places the analytical challenge for accurate BPO quantification. The objective of this study is to develop a reliable method for BPO quantification in flour. Raman spectroscopy was first explored to detect BPO and BA on an aluminum foil slide. The result showed BPO and BA produced distinct Raman peaks that can be discriminated against. However, the sensitivity was not satisfactory to reach the regulation limit. To improve sensitivity, surface-enhanced Raman spectroscopy (SERS) was applied using silver nanoparticles as the substrate. Although the signals did enhance significantly using SERS, the characteristic peaks of BPO disappeared as BPO converted to BA during the sample preparation. We then went back to Raman spectroscopy but focused on optimizing the sample preparation to enhance the signal intensity. Using a hydrophobic surface (i.e., parafilm) which can hold the droplet and minimize the spread, the Raman signal was enhanced significantly after repeating multiple droplets on the same surface. A standard curve was created for BPO from 25 ppm to 250 ppm and for BA from 250 ppm to 1000 ppm, respectively. To detect BPO in wheat flour, we applied a more advanced Raman imaging instrument and focused on the analysis of Raman maps instead of spectra for the analysis of effect flour matrix to BPO extraction and detection. We firstly tried an in situ method, which scanned the pellet of flour spiked with different amounts of BPO without extraction. However, we could not detect BPO at 0.1% or lower in flour samples. We then tried an extraction method using acetonitrile as the solvent, which showed a lower detection limit compared to the in situ method. However, this extraction method yielded inconsistent results for BPO that is under 0.05% in flour. The extraction method developed was further improved with an evaporating step and a C18 solid phase extraction (SPE) spin column. This improved the extraction efficacy and provided a roughly 60% recovery percentage for detecting BPO in wheat flour without decomposing into BA. In conclusion, we developed a simple sample preparation protocol coupled with Raman spectroscopy to quantify BPO in flour without converting to BA, which would meet the regulation requirement. This method also shortened the experiment time including both sample preparation and detection time compared to current methods.

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The development of a waterborne pathogen detection method that is rapid, multiplex, sensitive, and specific, would be of great assistance for water treatment facilities and would help protect water consumers from harmful pathogens. Here we have utilized surface enhanced Raman spectroscopy (SERS) in a sensitive multiplex pathogen detection method. Two strategies are proposed herein, one that utilizes SERS antibody labels and one that measures the intrinsic SERS signal of organisms. For the SERS label strategy, gold nanoparticles are conjugated with antibodies specific to Cryptosporidium parvum and Giardia lamblia and with organic dye molecules. The dye molecules, rhodamine B isothiocyanate (RBITC) and malachite green isothiocyanate (MGITC) were surface enhanced by the gold nanoparticles resulting in unique fingerprint SERS spectra. The SERS label method was successful in detecting G. lamblia and C. parvum simultaneously. The method was subsequently coupled with a filtration step to both concentrate and capture cysts on a flat surface for detection. Raman mapping across the filter membrane detected ~95% of the spiked cysts in the optimized system. In the second type of strategy, intrinsic virus SERS signals were detected with silver nanoparticles for enhancement. Principal component analysis performed on the spectra data set resulted in the successful differentiation of MS2 and PhiX174 species and also for the differentiation of viable virus samples and inactivated virus samples. / Ph. D.

Surface-enhanced Raman spectroscopy (SERS) is a powerful analytical technique that combines molecular specificity of vibrational fingerprints offered by Raman spectroscopy with single-molecule detection sensitivity from plasmonic hotspots of noble metal nanostructures. Label-free SERS has attracted tremendous interest in bioanalysis over the last two decades due to minimal sample preparation, non-invasive measurement without water background interference, and multiplexing capability from rich chemical information of narrow Raman bands. Nevertheless, significant challenges should be addressed to become a widely accepted technique in bio-related communities. In this dissertation, limitations from different aspects (performance, reliability, and analysis) are articulated with state-of-the-art, followed by how introduced works resolve them. For high SERS performance, SERS substrates consisting of vertically-stacked multiple metal-insulator-metal layers, named nanolaminate, were designed to simultaneously achieve high sensitivity and excellent uniformity, two previously deemed mutually exclusive properties. Two unique factors of nanolaminate SERS substrates were exploited for the improved reliability of label-free in situ classification using living cancer cells, including background refractive index (RI) insensitivity from 1.30 to 1.60, covering extracellular components, and 3D protruding nanostructures that can generate a tight nano-bio interface (e.g., hotspot-cell coupling). Discrete nanolamination by new nanofabrication additionally provides optical transparency, offering backside-excitation, thereby label-free glucose sensing on a skin-phantom model. Towards reliable quantitative SERS analysis, an electronic Raman scattering (ERS) calibration method was developed. ERS from metal is omnipresent in plasmonic constructs and experiences identical hotspot enhancements. Rigorous experimental results support that ERS can serve as internal standards for spatial and temporal calibration of SERS signals with significant potential for complex samples by overcoming intrinsic limitations of state-of-art Raman tags. ERS calibration was successfully applied to label-free living cell SERS datasets for classifying cancer subtypes and cellular drug responses. Furthermore, dual-recognition label-SERS with digital assay revealed improved accuracy in quantitative dopamine analysis. Artificial neural network-based advanced machine learning method was exploited to improve the interpretability of bioanalytical SERS for multiple living cell responses. Finally, this dissertation provides future perspectives with different aspects to design bio-interfaced SERS devices for clinical translation, followed by guidance for SERS to become a standard analytical method that can compete with or complement existing technologies. / Doctor of Philosophy / In photonics, metals were thought to be not very useful, except mirrors. However, at a length scale smaller than wavelength, it has been realized that metallic structures can provide unique ways of light manipulation. Maxwell's equations show that an interface between dielectric and metal can support surface plasmons, resulting in collective oscillations of electrons and light confinement. Surface-enhanced Raman spectroscopy (SERS) is a sensing technique that combines enhanced local fields arising from plasmon excitation with molecular fingerprint specificity of vibrational Raman spectroscopy. The million-fold enhancement of Raman signals at hotspots has driven an explosion of research, providing tons of publications over the last two decades with a broad spectrum of physical, chemical, and biological applications. Nevertheless, significant challenges should be addressed for SERS to become a widely accepted technique, especially in bio-related communities. In this dissertation, limitations from different aspects (performance, reliability, and analysis) are articulated with state-of-the-art, followed by how innovative strategies addressed them. Each chapter's unique approach consists of a combination of five aspects, including nanoplasmonics, nanofabrication, nano-bio interface, cancer biology, statistical machine learning. First, high-performance SERS substrates were designed to simultaneously achieve high sensitivity and excellent uniformity, two previously deemed mutually exclusive properties, by vertically stacking multiple metal-insulator-metal layers (i.e., nanolaminate). Their 3D protruding nanotopography and refractive-index-insensitive SERS response enabled label-free in situ classification of living cancer cells. Tweaked nanofabrication produced discrete nanolamination with optical transparency, enabling label-free glucose sensing on a skin phantom. Towards reliable quantitative SERS analysis, an electronic Raman scattering (ERS) calibration method was developed that can overcome the intrinsic limitations of Raman tags, and it was successfully applied to label-free living cell SERS datasets for classifying cancer subtypes and cellular drug responses. Furthermore, dual-recognition label-SERS with digital assay revealed improved accuracy in quantitative dopamine analysis. Advanced machine learning (artificial neural network) was exploited to improve the interpretability of SERS bioanalysis for multiple cellular drug responses. Finally, this dissertation provides future perspectives with different aspects, including SERS, biology, and statistics, for SERS to potentially become a standard analytical method that can compete with or complement existing technologies.