ABSTRACT

This EArly-concept Grants for Exploratory Research (EAGER) award seeks to develop a scalable and cost-effective fabrication paradigm for rationally designed nanostructured substrates, which in concert with optical measurements and machine learning, offer highly sensitive and selective detection and identification of the coronavirus related to the Coronavirus Disease 2019 (COVID-19) pandemic. The research approach paves the way for large area rigid and flexible sensors that can be used to optically identify virus strains with minimal sample preparation in point-of-care settings thereby greatly improving preparedness for future waves of coronavirus outbreak and other pandemics. Crucially, the detection methodology eliminates the need for virus-specific biomolecular capture or detection elements and holds promise for detection of mutated viruses without any alteration to the platform. By combining expertise in the disparate fields of scalable nanomanufacturing, optical spectroscopy, biosensing, analytical chemistry and machine learning, this endeavor not only delivers a fundamentally different approach to population-wide testing for viruses but also creates a new tool to explore diverse biological systems. The project seeks to enhance the education curriculum for undergraduates while the research findings related to the fabrication of the large-area sensor fabric and its use in detection of infectious agents are incorporated into graduate teaching activities and disseminated into the scientific community.

This award supports the development of a new platform for ultrasensitive and rapid detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by exploiting Surface Enhanced Raman Spectroscopy (SERS) signatures recorded on highly reproducible plasmonically active substrates in a label-free manner. Large area nanogap (hot-spot) patterns are nanoimprinted on flexible fabric. The gap dimensions (5-10 nm) are regulated and reduced to sub-lithographic sizes by transfer onto pre-stretched substrates followed by strain release. SERS spectra are collected from low pathogenic viruses as well as from clinical samples with suspected SARS-CoV-2 and other human respiratory infections. Given the complexity of the samples and the presence of other spectral interferents, pattern recognition methods and supervised classification approaches are harnessed to relate the spectral information to the identification of pathogens. By capturing latent biological differences that are encoded in the vibrational fingerprints, this method creates a new landscape for pathogen analysis eschewing the need for complex sample preparation using specific capture and detection molecules. Through this multidisciplinary collaborative effort that integrates nanomanufacturing, biophotonics, and machine learning, this project lays the foundation for a broadly applicable sensing platform with applications extending beyond virus detection. In addition, the enhanced sensitivity of this novel sensing tool is expected to revolutionize the understanding of other nanoscale molecular processes such as energy transduction and protein conformational dynamics and function.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.