Bio-interfaced Nanolaminate Surface-enhanced Raman Spectroscopy Substrates

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.