Research

My research focuses on surface-enhanced Raman spectroscopy (SERS), plasmonic nanomaterials, silver nanorod substrates, Raman spectroscopy, and machine learning-assisted chemical and biological sensing.

I develop label-free spectroscopic sensing approaches by combining nanofabricated SERS substrates, Raman spectral analysis, and machine learning models for classification, quantification, and interpretation of complex biological and chemical samples.

Research Projects

Label-Free SERS and Machine Learning for Virus Detection

This project develops a label-free SERS platform for rapid classification and quantification of bovine respiratory disease complex viruses. Silver nanorod substrates are used to enhance Raman signals, and machine learning models are applied for virus discrimination and concentration prediction.

Methods: Ag nanorod substrates, Raman spectroscopy, SVM classification, SVR regression, blind validation.

Keywords: SERS, virus detection, Raman spectroscopy, machine learning, silver nanorods.

Bacterial Supernatant Analysis Using SERS

This project investigates the use of Ag nanorod-based SERS substrates for detecting bacterial signatures from culture supernatants. Raman spectral fingerprints are analyzed using multivariate analysis and machine learning to distinguish bacterial samples.

Methods: SERS, bacterial supernatants, PCA, SVM, spectral preprocessing.

Keywords: bacterial sensing, SERS, Raman spectroscopy, PCA, SVM, biosensing.

Understanding SERS Spectral Shape Variability

This project focuses on understanding why SERS spectral shapes change under different experimental conditions. The study examines the effects of substrate optics, Ag nanorod length, molecular orientation, analyte concentration, and time-dependent adsorption.

Methods: Ag nanorod length study, BPE SERS, peak-ratio analysis, PCA, hierarchical clustering.

Keywords: SERS variability, molecular orientation, plasmonics, clustering, silver nanorods.

SpectraGuru and Raman/SERS Data Analysis Tools

This project contributes to Raman and SERS spectral data analysis workflows, including baseline correction, preprocessing, visualization, and scalable spectral analysis. The goal is to make spectroscopy data analysis more accessible, reproducible, and community-guided.

Methods: Raman/SERS preprocessing, baseline correction, spectral visualization, open-source analysis tools.

Keywords: SpectraGuru, Raman analysis, SERS analysis, baseline correction, open-source spectroscopy.

Raman Spectroscopy of PFAS Compounds

This project studies Raman spectral features of selected PFAS compounds and compares experimental Raman spectra with density functional theory predictions. The goal is to improve molecular-level spectral interpretation of PFAS-related Raman signatures.

Methods: Raman spectroscopy, PFAS compounds, DFT comparison, vibrational assignment.

Keywords: PFAS, Raman spectroscopy, DFT, vibrational analysis, environmental sensing.