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<p>This study presents a tunable graphene-metasurface biosensor for cancer cell detection. The design features four gold-coated square resonators within silver-coated rectangular elements on a graphene substrate, optimizing electromagnetic field confinement and plasmonic response.Parametric optimization of graphene chemical potential, incident angle, and resonator dimensions yields maximum sensitivity of 2000&#xa0;GHz/RIU and figure of merit of 24.096 RIU⁻<sup>1</sup>. Linear correlation analysis demonstrates strong relationship between resonance frequency and cancer cell concentration (R<sup>2</sup> = 0.95276). Field distribution modeling confirms optimal localization at 0.88 THz.XGBoost regression modeling achieves 91% predictive accuracy for absorption measurements. The sensor demonstrates superior performance compared to existing technologies, providing enhanced sensitivity and reliability for early cancer detection applications.</p>

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High-Sensitivity Graphene-Metasurface THz Biosensor for Label-Free Cancer Cell Detection with XG Boost Regression Optimization

  • 期刊论文

  • 发表日期：

    2025年6月2日

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  • Sathiya R,
  • Lakshmi R,
  • Arun Kumar U,
  • Dhivya R

摘要

This study presents a tunable graphene-metasurface biosensor for cancer cell detection. The design features four gold-coated square resonators within silver-coated rectangular elements on a graphene substrate, optimizing electromagnetic field confinement and plasmonic response.Parametric optimization of graphene chemical potential, incident angle, and resonator dimensions yields maximum sensitivity of 2000 GHz/RIU and figure of merit of 24.096 RIU⁻1. Linear correlation analysis demonstrates strong relationship between resonance frequency and cancer cell concentration (R2 = 0.95276). Field distribution modeling confirms optimal localization at 0.88 THz.XGBoost regression modeling achieves 91% predictive accuracy for absorption measurements. The sensor demonstrates superior performance compared to existing technologies, providing enhanced sensitivity and reliability for early cancer detection applications.