Scholarlink

<p>This study presents the design and analysis of a biosensor for COVID-19 detection, integrating graphene metasurfaces with gold, silver, and GST materials. The proposed sensor architecture combines a square ring resonator with a circular ring resonator, optimized through COMSOL Multiphysics simulations in the infrared regime. The sensor demonstrates exceptional performance characteristics, with absorption values exceeding 99.5% in the primary detection band (4.2–4.6&#xa0;μm) and approximately 97.5% in the secondary band (5.0–5.5&#xa0;μm). The device exhibits high sensitivity (4000&#xa0;nm/RIU), a detection limit of 0.078, and a figure of merit of 16.000 RIU⁻¹ when utilizing crystalline GST as the substrate material. The sensor’s performance was further enhanced through machine learning optimization using XGBoost regression, achieving perfect correlation (R² = 100%) between predicted and experimental values across various operational parameters. The dual-band detection mechanism, combined with the integration of advanced materials and machine learning optimization, offers a promising platform for rapid, label-free, and highly sensitive COVID-19 detection. This research contributes to the development of next-generation biosensing technologies for viral detection and disease diagnosis.</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

Graphene metasurfaces biosensor for COVID-19 detection in the infra-red regime

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月12日

查看全文
  • Hussein A. Elsayed,
  • Jacob Wekalao,
  • Ahmed Mehaney,
  • Haifa E. Alfassam,
  • Mostafa R. Abukhadra,
  • Ali Hajjiah,
  • Wail Al Zoubi

摘要

This study presents the design and analysis of a biosensor for COVID-19 detection, integrating graphene metasurfaces with gold, silver, and GST materials. The proposed sensor architecture combines a square ring resonator with a circular ring resonator, optimized through COMSOL Multiphysics simulations in the infrared regime. The sensor demonstrates exceptional performance characteristics, with absorption values exceeding 99.5% in the primary detection band (4.2–4.6 μm) and approximately 97.5% in the secondary band (5.0–5.5 μm). The device exhibits high sensitivity (4000 nm/RIU), a detection limit of 0.078, and a figure of merit of 16.000 RIU⁻¹ when utilizing crystalline GST as the substrate material. The sensor’s performance was further enhanced through machine learning optimization using XGBoost regression, achieving perfect correlation (R² = 100%) between predicted and experimental values across various operational parameters. The dual-band detection mechanism, combined with the integration of advanced materials and machine learning optimization, offers a promising platform for rapid, label-free, and highly sensitive COVID-19 detection. This research contributes to the development of next-generation biosensing technologies for viral detection and disease diagnosis.