Ultra-sensitive terahertz biochemical detector via gold–graphene synergistic metastructures enhanced by machine learning optimization

Khaled Aliqab, Jacob Wekalao, Ammar Armghan, Meshari Alsharari, Shobhit K. Patel,
Ultra-sensitive terahertz biochemical detector via gold–graphene synergistic metastructures enhanced by machine learning optimization,
Ain Shams Engineering Journal,
Volume 16, Issue 12,
2025,
103749,
ISSN 2090-4479,
https://doi.org/10.1016/j.asej.2025.103749.
(https://www.sciencedirect.com/science/article/pii/S2090447925004903)
Abstract: The research introduces gold and graphene in a metasurface engineered to identify substances with subtle refractive index variations. The sensor features a multi-layered resonator configuration comprising a central circular resonator and concentric square rings fabricated on a SiO2 substrate. Through systematic optimization using COMSOL Multiphysics and machine learning technique, the final design achieves exceptional sensitivity of 800 GHz/RIU within the refractive index range of 1.29–1.38 RIU. The sensor demonstrates remarkable performance metrics, including 8.000 RIU–1, 0.157, and 13.140 as FOM (figure of merit), detection limit and quality factor. A stacking ensemble regressor approach reduced computational requirements by 88 % while maintaining 96–100 % prediction accuracy. Combining high sensitivity with reliable operation, this design excels in detecting low refractive indices with precision in the THz spectrum. Optimization in machine learning enhances sensitivity by fine-tuning parameters, reducing errors, improving accuracy, refining models, and boosting overall performance effectively.
Keywords: Graphene: Metasurface; Machine learning; Gold nanostructures; Nanofabrication; Biosensor