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<p>Diabetes mellitus is a rapidly growing global health concern that requires accurate, rapid, and non-invasive diagnostic technologies for effective screening, early detection, and disease management. This study presents a hybrid graphene-assisted multilayer terahertz (THz) metasurface biosensor comprising Ag, Cu, Al, Zn, MXene (Ti₃C₂Tₓ), WSe₂, and graphene on a SiO₂ substrate for high-sensitivity glucose detection. The electromagnetic response is investigated using the finite-element method in COMSOL Multiphysics, incorporating frequency-dependent Drude–Lorentz and Kubo conductivity models. Sensor performance is evaluated over a refractive-index range of 1.335–1.347 RIU, corresponding to clinically relevant glucose concentrations. The optimized biosensor achieves a maximum sensitivity of 0.976 THz/RIU (976&#xa0;GHz/RIU), while electric-field analysis reveals strong edge-localized surface plasmon polariton confinement that enhances light–matter interaction and sensing performance. To enable rapid sensor evaluation, a machine-learning regression model is developed as a computational surrogate for full-wave simulations, achieving an independent test-set coefficient of determination of R² &gt; 0.9997 with substantially reduced computational cost. The proposed hybrid metasurface integrated with machine-learning-assisted prediction offers a promising platform for high-sensitivity THz glucose biosensing, supporting rapid diabetes screening, early diagnosis, continuous disease management, and future point-of-care healthcare applications.</p>

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Design and numerical investigation of a multi-resonant Ag–Cu–Al–Graphene terahertz plasmonic metasurface biosensor with machine learning–assisted optimization for high-sensitivity glucose detection

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • Shanthi K,
  • Ajith.B. Singh,
  • Gopinath S,
  • Arun Kumar U

摘要

Diabetes mellitus is a rapidly growing global health concern that requires accurate, rapid, and non-invasive diagnostic technologies for effective screening, early detection, and disease management. This study presents a hybrid graphene-assisted multilayer terahertz (THz) metasurface biosensor comprising Ag, Cu, Al, Zn, MXene (Ti₃C₂Tₓ), WSe₂, and graphene on a SiO₂ substrate for high-sensitivity glucose detection. The electromagnetic response is investigated using the finite-element method in COMSOL Multiphysics, incorporating frequency-dependent Drude–Lorentz and Kubo conductivity models. Sensor performance is evaluated over a refractive-index range of 1.335–1.347 RIU, corresponding to clinically relevant glucose concentrations. The optimized biosensor achieves a maximum sensitivity of 0.976 THz/RIU (976 GHz/RIU), while electric-field analysis reveals strong edge-localized surface plasmon polariton confinement that enhances light–matter interaction and sensing performance. To enable rapid sensor evaluation, a machine-learning regression model is developed as a computational surrogate for full-wave simulations, achieving an independent test-set coefficient of determination of R² > 0.9997 with substantially reduced computational cost. The proposed hybrid metasurface integrated with machine-learning-assisted prediction offers a promising platform for high-sensitivity THz glucose biosensing, supporting rapid diabetes screening, early diagnosis, continuous disease management, and future point-of-care healthcare applications.