Scholarlink

<p>This study presents a terahertz-based biosensor for tuberculosis detection, incorporating a unique metasurfaces configuration. The sensor’s architecture features multiple resonating elements: a silver-based circular ring resonator, a black phosphorus square ring structure, a graphene platform, and symmetrically positioned gold rectangular resonators. Finite element method analysis through COMSOL Multiphysics was employed for modelling and optimization. The sensor demonstrates an exceptional sensitivity of 1000 GHzRIU<sup>−1</sup> and a detection limit of 0.310 RIU. In addition, the designed sensor maintains a high-quality factor of 8.176 and exhibits stable performance across its operational frequency range (0.1–1.4 THz). Moreover, the performance analysis under varying conditions showed consistent transmission patterns and frequency-dependent characteristics, with the graphene chemical potential that could be significantly influencing the sensor’s response. Furthermore, the integration of polynomial regression-based machine learning optimization yielded remarkably accurate predictions (<i>R</i><sup>2</sup> &gt; 0.97) across various operational parameters. Therefore, the investigated numerical findings and machine learning optimization prove that this sensor represents a significant advancement in tuberculosis detection technology, offering improved sensitivity and reliability compared to conventional methods, whilst maintaining fabrication feasibility through standard cleanroom processes.</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

A High Sensitivity Terahertz Biosensor with Hybrid Metasurfaces for Tuberculosis Detection Leveraging the Integration of Machine Learning and Multi-Material Resonators

  • 期刊论文

  • 发表日期：

    2025年6月17日

查看全文
  • Amuthakkannan Rajakannu,
  • Jacob Wekalao,
  • Hussein A. Elsayed,
  • Ahmed M. El-Sherbeeny,
  • Mostafa R. Abukhadra,
  • Ali Hajjiah,
  • Nassir Saad Alarifi,
  • Ahmed Mehaney

摘要

This study presents a terahertz-based biosensor for tuberculosis detection, incorporating a unique metasurfaces configuration. The sensor’s architecture features multiple resonating elements: a silver-based circular ring resonator, a black phosphorus square ring structure, a graphene platform, and symmetrically positioned gold rectangular resonators. Finite element method analysis through COMSOL Multiphysics was employed for modelling and optimization. The sensor demonstrates an exceptional sensitivity of 1000 GHzRIU−1 and a detection limit of 0.310 RIU. In addition, the designed sensor maintains a high-quality factor of 8.176 and exhibits stable performance across its operational frequency range (0.1–1.4 THz). Moreover, the performance analysis under varying conditions showed consistent transmission patterns and frequency-dependent characteristics, with the graphene chemical potential that could be significantly influencing the sensor’s response. Furthermore, the integration of polynomial regression-based machine learning optimization yielded remarkably accurate predictions (R2 > 0.97) across various operational parameters. Therefore, the investigated numerical findings and machine learning optimization prove that this sensor represents a significant advancement in tuberculosis detection technology, offering improved sensitivity and reliability compared to conventional methods, whilst maintaining fabrication feasibility through standard cleanroom processes.