Next-generation COVID-19 detection using a metasurface biosensor with machine learning-enhanced refractive index sensing.

Abstract

This work introduces a high-performance graphene-silver hybrid metasurface biosensor for the fast and precise detection of COVID-19. Through parametric optimization with COMSOL Multiphysics, the sensor achieves a sensitivity of 400 GHz/RIU, a figure of merit (FOM) of 5.000 RIU⁻¹, and a Q factor of 12.7 within the refractive index range of 1.334-1.355 RIU. A machine learning framework enhances predictive reliability across different refractive indices, as reflected by a coefficient of determination (R²) of 0.90. The fabrication strategy-combining CVD graphene growth, electron beam lithography, and silver deposition-ensures scalability and practical realization. The novelty of this study lies in the synergistic integration of a graphene-silver metasurface platform with machine learning-based predictive modeling, enabling rapid, label-free, and highly accurate COVID-19 detection. Unlike conventional RT-PCR and antigen-based tests, which suffer from delays, high costs, or reduced sensitivity in asymptomatic cases, the proposed sensor achieves superior balance between sensitivity, figure of merit, and predictive accuracy, thereby surpassing state-of-the-art optical and terahertz biosensors. This positions the device as a novel, portable, and cost-effective diagnostic tool for next-generation pandemic preparedness.