Scholarlink

<p>Pathogenic bacteria in water systems present a significant epidemiological burden, with substantial mortality rates observed predominantly in regions lacking advanced water purification infrastructure. This investigation presents the development and characterization of a novel metasurfaces-based biosensor utilizing refractive index (RI) modulation for rapid bacterial detection in aqueous media. The sensor architecture incorporates a hybrid nanocomposite structure comprising graphene sheets and metallic nanoparticles crucial for ultra-low-threshold bacterial detection. Finite element analysis through COMSOL Multiphysics demonstrates significant electromagnetic coupling, manifesting as pronounced transmittance attenuation across the 0.3–0.7 THz spectral region, with minimal transmission coefficients of 47.349%. The proposed sensor design exhibits exceptional metrological parameters, achieving a sensitivity coefficient of 1707 GHzRIU<sup>−1</sup> and a limit of detection (LOD) of 0.028. Implementation of machine learning optimization protocols particularly stacking ensemble algorithms, enhances the sensor’s analytical capabilities, yielding coefficient of determination (R<sup>2</sup>) values ranging from 0.96 to 1.00 across varying refractive indices corresponding to distinct bacterial species. Quantitative comparison with contemporary biosensing platforms depicts the superior analytical performance of the proposed sensor architecture, suggesting significant potential for implementation in water quality monitoring systems, particularly in regions susceptible to waterborne pathogen proliferation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a High-Sensitivity Graphene-Metal Nanocomposite Metasurfaces Biosensor with Machine Learning Integration for Rapid Detection of Waterborne Pathogens with Behaviour Prediction Leveraging Stacking Ensemble Regressor

  • 期刊论文

  • 发表日期：

    2025年1月7日

查看全文
  • Jacob Wekalao

摘要

Pathogenic bacteria in water systems present a significant epidemiological burden, with substantial mortality rates observed predominantly in regions lacking advanced water purification infrastructure. This investigation presents the development and characterization of a novel metasurfaces-based biosensor utilizing refractive index (RI) modulation for rapid bacterial detection in aqueous media. The sensor architecture incorporates a hybrid nanocomposite structure comprising graphene sheets and metallic nanoparticles crucial for ultra-low-threshold bacterial detection. Finite element analysis through COMSOL Multiphysics demonstrates significant electromagnetic coupling, manifesting as pronounced transmittance attenuation across the 0.3–0.7 THz spectral region, with minimal transmission coefficients of 47.349%. The proposed sensor design exhibits exceptional metrological parameters, achieving a sensitivity coefficient of 1707 GHzRIU−1 and a limit of detection (LOD) of 0.028. Implementation of machine learning optimization protocols particularly stacking ensemble algorithms, enhances the sensor’s analytical capabilities, yielding coefficient of determination (R2) values ranging from 0.96 to 1.00 across varying refractive indices corresponding to distinct bacterial species. Quantitative comparison with contemporary biosensing platforms depicts the superior analytical performance of the proposed sensor architecture, suggesting significant potential for implementation in water quality monitoring systems, particularly in regions susceptible to waterborne pathogen proliferation.