Influenza A Virus Detection via Ag@c-Si SERS and Machine Learning

Thisstudy demonstrates the efficacy of surface-enhanced Raman spectroscopy (SERS) combined with machine learning for detecting and distinguishing influenza A virus in an STE buffer medium. Dendritic Ag structures on single-crystal silicon were proposed as SERS-active substrates, synthesized via a costeffective method yielding varied morphologies. Spectroscopic ellipsometry aided in substrate selection based on optical properties, with the most promising substrate exhibiting a localized surface plasmon resonance (LSPR) near the excitation wavelength. SERS analysis before and after viral exposure revealed subtle spectral differences, necessitating machine learning algorithms for accurate classification, achieving a detection accuracy of $76.6 \pm 4.2 \%$.