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<p>This study presents a surface plasmon resonance (SPR) biosensor design that integrates graphene-coated resonators with multi-metallic layers (Au, Ag, Cu, Al) for an enhanced breast cancer biomarker detection in the terahertz frequency range. Computational modelling using COMSOL Multiphysics software demonstrates exceptional performance metrics, including sensitivity’s values reaching 500&#xa0;GHz/RIU, figure-of-merit up to 5.263 RIU<sup>−1</sup>, and detection accuracy of 10.526 across the operational frequency range of 0.1–0.6 THz. In addition, the biosensor’s performance is further optimized through locally weighted linear regression (LWLR) machine learning algorithms, achieving prediction accuracies with <i>R</i><sup>2</sup> values ranging from 86% to 93% for various operational parameters including graphene chemical potential (0.1–0.9&#xa0;eV) and incident angles (0–80°). The electromagnetic field analysis reveals maximum energy confinement at 0.25 THz, with reflectance values stabilizing between 72% and 76% under optimal conditions. This innovative approach addresses critical limitations of conventional mammography and invasive biopsy procedures, offering a noninvasive, highly sensitive platform for detecting breast cancer biomarkers (CA 15-3, HER2, circulating tumor DNA) from picomolar to femtomolar concentrations, potentially revolutionizing early-stage breast cancer screening protocols.</p>

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Enhanced Multi-Layer Graphene-Metal Terahertz Biosensor with Machine Learning Optimization for Early-Stage Breast Cancer Detection

  • 期刊论文

  • 发表日期：

    2025年10月24日

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  • Hussein A. Elsayed,
  • Ahmed Mehaney,
  • Amuthakkannan Rajakannu,
  • Jacob Wekalao,
  • Haifa A. Alqhtani,
  • May Bin-Jumah,
  • Pelluce Kabarokole

摘要

This study presents a surface plasmon resonance (SPR) biosensor design that integrates graphene-coated resonators with multi-metallic layers (Au, Ag, Cu, Al) for an enhanced breast cancer biomarker detection in the terahertz frequency range. Computational modelling using COMSOL Multiphysics software demonstrates exceptional performance metrics, including sensitivity’s values reaching 500 GHz/RIU, figure-of-merit up to 5.263 RIU−1, and detection accuracy of 10.526 across the operational frequency range of 0.1–0.6 THz. In addition, the biosensor’s performance is further optimized through locally weighted linear regression (LWLR) machine learning algorithms, achieving prediction accuracies with R2 values ranging from 86% to 93% for various operational parameters including graphene chemical potential (0.1–0.9 eV) and incident angles (0–80°). The electromagnetic field analysis reveals maximum energy confinement at 0.25 THz, with reflectance values stabilizing between 72% and 76% under optimal conditions. This innovative approach addresses critical limitations of conventional mammography and invasive biopsy procedures, offering a noninvasive, highly sensitive platform for detecting breast cancer biomarkers (CA 15-3, HER2, circulating tumor DNA) from picomolar to femtomolar concentrations, potentially revolutionizing early-stage breast cancer screening protocols.