Optical-Based Aqueous Solution Detection by Graphene Metasurface Surface Plasmon Resonance Biosensor with Behavior Prediction Using Polynomial Regression

Abstract

Aqueous solutions are fundamental to a wide range of chemical and biological processes, serving as a critical medium for both natural phenomena and technological advancements. This study presents the design and modelling of a metasurface-based biosensor for aqueous solution detection. The sensor architecture comprises multiple resonators deposited on a silicon dioxide substrate, with materials selected for their specific optical properties. Finite element analysis was employed to simulate the sensor’s signal transduction mechanisms. The optimized design exhibits a sensitivity of 500 GHzRIU⁻¹ and a figure of merit of 10.638 RIU⁻¹. Comprehensive characterization of the sensor’s performance includes evaluation of its detection limit, dynamic range, and signal-to-noise ratio, all of which demonstrate superior target detection accuracy. The sensor’s versatility is further illustrated through its application in encoding operations, leveraging on the transmittance values to perform logic functions. A polynomial regression model was developed to interpolate absorption values at intermediate frequencies, achieving an R² value of 1.0, indicating perfect correlation between predicted and simulated data. These results suggest significant potential for the sensor’s application in high-precision biomolecular detection across various fields, including biomedical diagnostics and environmental monitoring.