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<p>Fast and reliable virus detection like SARS-CoV-2, continues to pose significant challenges in the worldwide health administration. This study introduces a refractive index sensor designed for COVID-19 detection. The sensor leverages a SiO<sub>2</sub>-based substrate supporting two quadrant-shaped resonators and other metasurfaces designs. The material choice incorporates a synergistic combination of black phosphorus (BP), MXene (MX), and graphene. Simulation results exemplify 800 GHzRIU<sup>−1</sup>, 11.429 RIU<sup>−1</sup>, and 0.119 THz as optimal sensitivity, figure of merit and detection limit. Additionally, machine learning algorithms, essentially the weighted k-nearest neighbour regression, were employed to predict sensor performance, yielding a near-perfect correlation between predicted and actual transmittance values. The suggested sensor’s capability to quickly and accurately detect viral particles without requiring labelling makes it an essential tool for point-of-care diagnostics during pandemics.</p>

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High-Sensitivity Terahertz Refractive Index Sensor Using Black Phosphorus-MXene-Graphene Hybrid Metasurfaces for Label-Free COVID-19 Detection

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Jacob Wekalao,
  • Refka Ghodhbani,
  • Dhivya R,
  • Arun Kumar U,
  • Ammar Armghan,
  • Shobhit K. Patel

摘要

Fast and reliable virus detection like SARS-CoV-2, continues to pose significant challenges in the worldwide health administration. This study introduces a refractive index sensor designed for COVID-19 detection. The sensor leverages a SiO2-based substrate supporting two quadrant-shaped resonators and other metasurfaces designs. The material choice incorporates a synergistic combination of black phosphorus (BP), MXene (MX), and graphene. Simulation results exemplify 800 GHzRIU−1, 11.429 RIU−1, and 0.119 THz as optimal sensitivity, figure of merit and detection limit. Additionally, machine learning algorithms, essentially the weighted k-nearest neighbour regression, were employed to predict sensor performance, yielding a near-perfect correlation between predicted and actual transmittance values. The suggested sensor’s capability to quickly and accurately detect viral particles without requiring labelling makes it an essential tool for point-of-care diagnostics during pandemics.