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<p>This study introduces a plasmonic biosensor for rapid TB detection. The sensor operates on refractive index changes induced by Mycobacterium tuberculosis biomolecular interactions.The sensor achieved exceptional sensitivity of 1000&#xa0;GHz/RIU within the refractive index range of 1.343–1.351, with a figure of merit reaching 13.699 RIU⁻<sup>1</sup> and quality factor of 11.932. Electric field distribution analysis confirmed maximum field confinement at the resonance frequency of 0.87 THz, validating optimal plasmonic coupling. Random Forest machine learning models achieved 100% R<sup>2</sup> accuracy in predicting sensor responses, demonstrating excellent predictive capability. Comparative analysis with existing biosensors confirms competitive performance while offering advantages in material efficiency and targeted biomedical diagnostics. This innovative sensor design addresses the urgent need for portable, sensitive, and cost-effective TB diagnostic tools suitable for deployment in low-resource healthcare settings, potentially revolutionizing point-of-care TB detection and contributing to global TB control efforts.</p>

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Advanced MXene-Gold Hybrid Plasmonic Biosensor for Early Detection of Tuberculosis Biomarkers With Machine learning optimization

  • 期刊论文

  • 发表日期：

    2025年10月28日

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  • R. Senthamizh Selvi,
  • Rajeshwari Ramaiah Murugesan,
  • Jacob Wekalao,
  • Amuthakkannan Rajakannu

摘要

This study introduces a plasmonic biosensor for rapid TB detection. The sensor operates on refractive index changes induced by Mycobacterium tuberculosis biomolecular interactions.The sensor achieved exceptional sensitivity of 1000 GHz/RIU within the refractive index range of 1.343–1.351, with a figure of merit reaching 13.699 RIU⁻1 and quality factor of 11.932. Electric field distribution analysis confirmed maximum field confinement at the resonance frequency of 0.87 THz, validating optimal plasmonic coupling. Random Forest machine learning models achieved 100% R2 accuracy in predicting sensor responses, demonstrating excellent predictive capability. Comparative analysis with existing biosensors confirms competitive performance while offering advantages in material efficiency and targeted biomedical diagnostics. This innovative sensor design addresses the urgent need for portable, sensitive, and cost-effective TB diagnostic tools suitable for deployment in low-resource healthcare settings, potentially revolutionizing point-of-care TB detection and contributing to global TB control efforts.