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<p>Early and non-invasive detection of brain tumors remains a critical challenge in biomedical diagnostics, motivating the development of highly sensitive terahertz (THz) biosensing platforms. In this work, a high-efficiency graphene–copper-based THz metasurface biosensor is proposed for refractive-index-based identification of brain tumors. The sensor architecture uses a nested resonator configuration combined with graphene’s tunable surface conductivity, achieved through chemical potential modulation from 0.1 to 0.9&#xa0;eV, to enhance electromagnetic field confinement and resonance sensitivity. Numerical analysis demonstrates that the sensor operates effectively over a refractive index range of 1.3333–1.4833 RIU, exhibiting a clear resonance redshift from 0.714 to 0.684&#xa0;THz. A maximum sensitivity of 1538.462&#xa0;GHz/RIU and a figure of merit of 15.86&#xa0;RIU⁻<sup>1</sup> are achieved near an optimal refractive index of 1.3425&#xa0;RIU. Machine-learning-based regression models are further employed to support predictive performance evaluation, yielding a coefficient of determination (R<sup>2</sup>) of 0.88. Comparative analysis confirms that the proposed sensor outperforms existing biosensors in terms of sensitivity. This machine-learning-assisted approach enables rapid, robust inference under experimental variability, surpassing conventional simulation models or interpolation methods and highlighting its potential for practical clinical deployment.</p>

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High-performance graphene–copper-based terahertz metasurface biosensor for early detection of brain tumors: a machine learning-enhanced approach

  • 期刊论文

  • 发表日期：

    2026年2月28日

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  • Rajeshwari Ramaiah Murugesan,
  • Sandeep Prabhu,
  • U. Arun Kumar,
  • Taha Sheheryar

摘要

Early and non-invasive detection of brain tumors remains a critical challenge in biomedical diagnostics, motivating the development of highly sensitive terahertz (THz) biosensing platforms. In this work, a high-efficiency graphene–copper-based THz metasurface biosensor is proposed for refractive-index-based identification of brain tumors. The sensor architecture uses a nested resonator configuration combined with graphene’s tunable surface conductivity, achieved through chemical potential modulation from 0.1 to 0.9 eV, to enhance electromagnetic field confinement and resonance sensitivity. Numerical analysis demonstrates that the sensor operates effectively over a refractive index range of 1.3333–1.4833 RIU, exhibiting a clear resonance redshift from 0.714 to 0.684 THz. A maximum sensitivity of 1538.462 GHz/RIU and a figure of merit of 15.86 RIU⁻1 are achieved near an optimal refractive index of 1.3425 RIU. Machine-learning-based regression models are further employed to support predictive performance evaluation, yielding a coefficient of determination (R2) of 0.88. Comparative analysis confirms that the proposed sensor outperforms existing biosensors in terms of sensitivity. This machine-learning-assisted approach enables rapid, robust inference under experimental variability, surpassing conventional simulation models or interpolation methods and highlighting its potential for practical clinical deployment.