High Sensitivity Terahertz Biosensor Based on Graphene/Methylammonium Lead Halide Metasurface with Machine Learning-Enhanced Pathogen Detection

This investigation presents an advanced biosensing platform integrating a Methylammonium lead halide metasurfaces with a dual-resonator architecture for high-precision waterborne pathogen detection. The engineered design incorporates a square ring resonator coupled with a graphene-functionalized circular resonator, optimized through systematic parametric analysis to achieve maximum sensitivity and specificity. Comprehensive electromagnetic simulations were performed utilizing COMSOL Multiphysics to characterize the sensor's electromagnetic response across multiple parameters, including graphene chemical potential modulation, incident wave angle variation, and resonator geometric configurations. The platform exhibited exceptional sensitivity to bacterial concentration-induced refractive index variations, demonstrating quantitative performance metrics of 488 GHzRIU-1 sensitivity, 0.234 RIU detection limit, and a quality factor of 12. 914. Implementation of the XGBoost machine learning algorithm for sensor response optimization yielded optimal prediction accuracy (R2 = 1.000) across all investigated parameters within the terahertz regime. These quantitative findings demonstrate the potential for integration of this sensing platform into high-throughput water quality monitoring systems, with significant implications for environmental surveillance and public health infrastructure applications.