Understanding Virus Evolution Through Deep Raman Spectroscopy

Sharon Huang

Sponsoring Agency
National Science Foundation


This Growing Convergence Research project aims at revolutionizing how circulating epidemic and pandemic influenza virus strains are surveilled and characterized. The project brings together virologists who perform controlled mutations of the virus, nanoscience engineers who create platforms for virus enrichment, materials scientists and optical spectroscopists who develop effective 2D material Raman signal enhancement platforms, and data scientists who analyze data with machine learning methods to achieve virus identification and prediction of potential emergence of new antigenic variants. The goal of the research team is to develop a convergent device platform that can rapidly capture, sense, and identify viruses and predict new antigenic strains against which the human population has limited or no immunity.

The project pursues a solution to a grand challenge in the surveillance and characterization of circulating epidemic and pandemic influenza virus strains by addressing deep scientific questions in enhanced Raman spectroscopy for virus detection and evolution prediction. The proposed platform is based on controlled virology experiments that propagate by culture and mutate viruses for specific research tasks, a novel virus enrichment platform for effectively capturing viruses without labels, biosensing of virus surface proteins with enhanced signal through a novel 2D/metal enhanced Raman spectroscopy technique, and rapid and sensitive virus identification and evolution prediction through machine learning analysis of enhanced Raman data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Research Area
Artificial Intelligence and Big Data
Health and Bioinformatics