Machine learning-enhanced terahertz plasmonic biosensor based on MXene-gold nanostructures for tuberculosis detection

This study presents a terahertz hybrid plasmonic biosensor utilizing MXene‑gold nanocomposites for tuberculosis detection. COMSOL Multiphysics simulations were employed to optimize sensor performance across varying chemical potential, incident angle, and resonator dimensions. The optimized configuration achieved a sensitivity of 1000 GHzRIU −1 and figure of merit of 22.22 RIU −1 , with a strong inverse linear relationship between resonance frequency and TB biomarker refractive indices (R 2 = 0.981). A machine learning framework based on decision tree regression was developed to predict sensor behavior, achieving R 2 values of 0.96, 0.92, and 0.88 for resonator dimensions, refractive index, and incident angle variations, respectively. The sensor platform offers significant potential for rapid, sensitive TB diagnostics in resource-limited settings.

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Publication Details

Journal
Sensing and Bio-Sensing Research
Published
2025-07-20
DOI
https://doi.org/10.1016/j.sbsr.2025.100852
Citations
14
Primary Topic
Advanced biosensing and bioanalysis techniques
Type
article
Field-Weighted Citation Impact
11.28

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article

Machine learning-enhanced terahertz plasmonic biosensor based on MXene-gold nanostructures for tuberculosis detection

Hussein A. Elsayed, Ahmed Mehaney, Mostafa R. Abukhadra, Stefano Bellucci et al.
14 citations
Sensing and Bio-Sensing Research
Advanced biosensing and bioanalysis techniques
11.28
article

Machine learning-enhanced terahertz plasmonic biosensor based on MXene-gold nanostructures for tuberculosis detection

Hussein A. Elsayed, Ahmed Mehaney, Mostafa R. Abukhadra, Stefano Bellucci, Haifa A. Alqhtani, Amuthakkannan Rajakannu, Jacob Wekalao, Mayi bin Jumah
article en
14 citations

Abstract

This study presents a terahertz hybrid plasmonic biosensor utilizing MXene‑gold nanocomposites for tuberculosis detection. COMSOL Multiphysics simulations were employed to optimize sensor performance across varying chemical potential, incident angle, and resonator dimensions. The optimized configuration achieved a sensitivity of 1000 GHzRIU −1 and figure of merit of 22.22 RIU −1 , with a strong inverse linear relationship between resonance frequency and TB biomarker refractive indices (R 2 = 0.981). A machine learning framework based on decision tree regression was developed to predict sensor behavior, achieving R 2 values of 0.96, 0.92, and 0.88 for resonator dimensions, refractive index, and incident angle variations, respectively. The sensor platform offers significant potential for rapid, sensitive TB diagnostics in resource-limited settings.

Sensing and Bio-Sensing ResearchVol. 49
Princess Nourah bint Abdulrahman University (SA), Beni-Suef University (EG), University of Science and Technology of China (CN), Applied Science Private University (JO), University of Ha'il (SA), Muscat College (OM), Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati (IT)
Princess Nourah Bint Abdulrahman University
Openalex Percentile: Top 1%
Advanced biosensing and bioanalysis techniques
11.28
DOIOpen Access PDFOpenAlex
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Machine learning-enhanced terahertz plasmonic biosensor based on MXene-gold nanostructures for tuberculosis detection — Hussein A. Elsayed, Ahmed Mehaney, et al. · Sensing and Bio-Sensing Research (2025) | TGRS Research Map | TGRS