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<p>This work presents the computational design, numerical optimization, and machine learning-assisted performance evaluation of a multi-resonator terahertz (THz) metasurface biosensor intended for potential non-invasive glucose monitoring applications. Numerical simulations using COMSOL Multiphysics reveal that the sensor achieves a high sensitivity of 1000&#xa0;GHz/RIU across a refractive index range of 1.335–1.347 RIU, with a quality factor exceeding 14 and figure of merit reaching 20 RIU<sup>−1</sup>. The design's versatility is demonstrated through parametric studies analysing the effects of graphene chemical potential, incident angle, and resonator dimensions on transmission spectra. For glucose detection applications, the sensor exhibits systematic frequency shifts from 0.719 THz to 0.714 THz corresponding to varying glucose concentrations, with a total tuning range of 30&#xa0;GHz. One-dimensional convolutional neural networks further optimize detection accuracy, achieving up to 93% predictive effectiveness.</p>

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MXene-graphene hybrid multi-resonator terahertz metasurface biosensor for potential non-invasive glucose monitoring: a computational design and proof-of-concept study

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Hussein A. Elsayed,
  • Emad Solouma,
  • Jacob Wekalao,
  • Ahmed Mehaney,
  • Mohammed Messaoudi,
  • Amuthakkannan Rajakannu,
  • Pelluce Kabarokole

摘要

This work presents the computational design, numerical optimization, and machine learning-assisted performance evaluation of a multi-resonator terahertz (THz) metasurface biosensor intended for potential non-invasive glucose monitoring applications. Numerical simulations using COMSOL Multiphysics reveal that the sensor achieves a high sensitivity of 1000 GHz/RIU across a refractive index range of 1.335–1.347 RIU, with a quality factor exceeding 14 and figure of merit reaching 20 RIU−1. The design's versatility is demonstrated through parametric studies analysing the effects of graphene chemical potential, incident angle, and resonator dimensions on transmission spectra. For glucose detection applications, the sensor exhibits systematic frequency shifts from 0.719 THz to 0.714 THz corresponding to varying glucose concentrations, with a total tuning range of 30 GHz. One-dimensional convolutional neural networks further optimize detection accuracy, achieving up to 93% predictive effectiveness.