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<p>This work introduces a highly sensitive and tunable THz biosensor designed for detecting breast cancer cells. The proposed sensor is based on a ring resonator with a centrally positioned sample carrier, utilizing a hybrid structure of black phosphorus (BP) and graphene. To optimize the interaction between Electronic Chemical Potential of Graphene and BP’s electron doping, a machine learning approach employing the K-Nearest Neighbors (KNN) model was implemented. Electromagnetic simulations demonstrate exceptional sensitivity, reaching 24.165 THz/RIU for healthy cells and 30.534 THz/RIU for cancerous cells. This design, characterized by its high sensitivity, structural simplicity, and tunability, highlights significant potential for applications in THz biomedical diagnostics, particularly in early breast cancer detection.</p>

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Advanced Optimization of 2D Material-Based Biosensor Through Machine Learning

  • 期刊论文

  • 发表日期：

    2025年6月16日

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  • Aymen Hlali,
  • Hassen Zairi

摘要

This work introduces a highly sensitive and tunable THz biosensor designed for detecting breast cancer cells. The proposed sensor is based on a ring resonator with a centrally positioned sample carrier, utilizing a hybrid structure of black phosphorus (BP) and graphene. To optimize the interaction between Electronic Chemical Potential of Graphene and BP’s electron doping, a machine learning approach employing the K-Nearest Neighbors (KNN) model was implemented. Electromagnetic simulations demonstrate exceptional sensitivity, reaching 24.165 THz/RIU for healthy cells and 30.534 THz/RIU for cancerous cells. This design, characterized by its high sensitivity, structural simplicity, and tunability, highlights significant potential for applications in THz biomedical diagnostics, particularly in early breast cancer detection.