Machine learning-enhanced terahertz plasmonic biosensor based on MXene-gold nanostructures for tuberculosis detection
Year: 2025
Type: article
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.
Source: Sensing and Bio-Sensing Research
Institutions: University of Ha'il, University of Science and Technology of China, Princess Nourah bint Abdulrahman University, Beni-Suef University, Applied Science Private University, Istituto Nazionale di Fisica Nucleare, Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Frascati, National University of Science and Technology
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,850
Award: G2695157095
OpenAlex ID: W4412495526