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<p>This study presents a graphene metasurface-based surface plasmon resonance biosensor for rapid and sensitive detection of SARS-CoV-2. The sensor design incorporates four identical circular resonators coated with graphene monolayer on a SiO₂ substrate, operating in the terahertz frequency range of 0.6–1.4 THz. The sensor demonstrates a quality factor of 7.028 and sensitivity of 400 GHzRIU<sup>−1</sup>, with optimal performance observed at 1.002–1.004 THz. Electromagnetic simulations using COMSOL Multiphysics exemplifies the sensor’s response to variations in graphene chemical potential, angle of incidence, and resonator dimensions. The detection capability is enhanced through machine learning optimization utilizing 1D Convolutional Neural Networks, achieving perfect R<sup>2</sup> scores of 100% under specific operational parameters. The proposed sensor offers a promising platform for rapid, sensitive, and reliable COVID-19 detection, particularly suitable for point-of-care applications in resource-limited settings.</p>

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Graphene Metasurface-Based Surface Plasmon Resonance Biosensor for Rapid COVID-19 Detection with Machine Learning Optimization

  • 期刊论文

  • 发表日期：

    2025年3月12日

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  • Jacob Wekalao

摘要

This study presents a graphene metasurface-based surface plasmon resonance biosensor for rapid and sensitive detection of SARS-CoV-2. The sensor design incorporates four identical circular resonators coated with graphene monolayer on a SiO₂ substrate, operating in the terahertz frequency range of 0.6–1.4 THz. The sensor demonstrates a quality factor of 7.028 and sensitivity of 400 GHzRIU−1, with optimal performance observed at 1.002–1.004 THz. Electromagnetic simulations using COMSOL Multiphysics exemplifies the sensor’s response to variations in graphene chemical potential, angle of incidence, and resonator dimensions. The detection capability is enhanced through machine learning optimization utilizing 1D Convolutional Neural Networks, achieving perfect R2 scores of 100% under specific operational parameters. The proposed sensor offers a promising platform for rapid, sensitive, and reliable COVID-19 detection, particularly suitable for point-of-care applications in resource-limited settings.