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<p>Rapid detection of bacterial contamination in water remains a critical challenge for public health due to the time, cost, and complexity of conventional diagnostic techniques. This study presents a label-free THz metasurface biosensor based on a hybrid MXene–copper–graphene architecture for sensitive detection of waterborne bacteria. The proposed design employs a symmetrical Y-shaped resonator array integrated with graphene tuning to achieve strong electromagnetic field confinement and active spectral control in the THz regime. Numerical analysis shows that the optimized structure exhibits a high refractive-index sensitivity of up to 488&#xa0;GHz·RIU⁻¹ within the biologically relevant range of 1.33–1.3921. The sensor demonstrates a linear resonance frequency shift with refractive index variation and achieves a maximum figure of merit of 5.360 RIU<sup>-1</sup>. Electric-field analysis confirms strong localization at the resonant frequency, enabling enhanced interaction with the sensing medium. In addition, a machine-learning regression framework is employed to accurately predict angular-dependent sensor behavior with near-unity accuracy, significantly reducing computational cost. These results indicate that the proposed metasurface offers a rapid, sensitive, and cost-effective platform for real-time monitoring of bacterial contamination in water.</p>

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Symmetrical Y-Resonator Plasmonic Metasurface with Machine-Learning-Assisted THz Sensing for Ultra-sensitive and Label-Free Terahertz Detection of Bacterial Contamination in Water

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Dinesh Paramathi Mani,
  • C. Kotteeswaran,
  • A. Anbu Megelin Star,
  • Arun Anthonisamy,
  • Manjunathan Alagarsamy

摘要

Rapid detection of bacterial contamination in water remains a critical challenge for public health due to the time, cost, and complexity of conventional diagnostic techniques. This study presents a label-free THz metasurface biosensor based on a hybrid MXene–copper–graphene architecture for sensitive detection of waterborne bacteria. The proposed design employs a symmetrical Y-shaped resonator array integrated with graphene tuning to achieve strong electromagnetic field confinement and active spectral control in the THz regime. Numerical analysis shows that the optimized structure exhibits a high refractive-index sensitivity of up to 488 GHz·RIU⁻¹ within the biologically relevant range of 1.33–1.3921. The sensor demonstrates a linear resonance frequency shift with refractive index variation and achieves a maximum figure of merit of 5.360 RIU-1. Electric-field analysis confirms strong localization at the resonant frequency, enabling enhanced interaction with the sensing medium. In addition, a machine-learning regression framework is employed to accurately predict angular-dependent sensor behavior with near-unity accuracy, significantly reducing computational cost. These results indicate that the proposed metasurface offers a rapid, sensitive, and cost-effective platform for real-time monitoring of bacterial contamination in water.