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<p>Breast cancer detection has advanced significantly with the application of optical sensing methods, particularly through Surface Plasmon Resonance (SPR). This paper presents an innovative sensor design based on a composite material configuration of phosphorene, MXene, and graphene. By leveraging the unique properties of these materials and employing advanced metasurface resonators, the sensor operates in the terahertz (THz) spectrum, achieving a high sensitivity of 500&#xa0;GHz/RIU in detecting subtle refractive index variations in breast tissue, along with a quality factor of 10.119. The proposed design features a simple resonator arrangement to enhance electromagnetic coupling and optimize sensor performance. Simulation results demonstrate the sensor’s remarkable sensitivity and precision, further enhanced by machine learning, with an R<sup>2</sup> score exceeding 90%. This makes it a promising tool for early breast cancer detection, establishing a reliable platform for clinical diagnostics.</p>

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High-sensitivity terahertz biosensor for breast cancer detection using nanostructured metasurfaces and machine learning

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Jacob Wekalao

摘要

Breast cancer detection has advanced significantly with the application of optical sensing methods, particularly through Surface Plasmon Resonance (SPR). This paper presents an innovative sensor design based on a composite material configuration of phosphorene, MXene, and graphene. By leveraging the unique properties of these materials and employing advanced metasurface resonators, the sensor operates in the terahertz (THz) spectrum, achieving a high sensitivity of 500 GHz/RIU in detecting subtle refractive index variations in breast tissue, along with a quality factor of 10.119. The proposed design features a simple resonator arrangement to enhance electromagnetic coupling and optimize sensor performance. Simulation results demonstrate the sensor’s remarkable sensitivity and precision, further enhanced by machine learning, with an R2 score exceeding 90%. This makes it a promising tool for early breast cancer detection, establishing a reliable platform for clinical diagnostics.