Intelligent Data-Driven Modeling of SARS-CoV-2 Interactions in BP–MXene–BP Heterostructure SPR Biosensors using Ridge Regression Model

¹ Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.
² Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India.

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The global SARS-CoV-2 pandemic has emphasized the urgent need for rapid, accurate, and scalable diagnostic technologies suitable for widespread screening. Conventional laboratory methods such as RT-PCR and ELISA, although reliable, suffer from long turnaround times, high operational cost, and dependence on specialized personnel, limiting their applicability in resource-constrained environments. Surface Plasmon Resonance (SPR) biosensors have emerged as promising alternatives, offering real-time, label-free molecular detection with high sensitivity and specificity. Recent advances highlight that integrating 2D nanomaterials—particularly BP/MXene multilayer heterostructures—significantly enhances plasmonic field confinement, signal strength, sensitivity, and detection accuracy compared to traditional metal-only configurations. However, modeling and optimizing such advanced SPR architectures typically depend on computationally intensive analytical methods, such as the Trans- fer Matrix Method and Fresnel formulations, which rely on idealized material parameters and are difficult to scale for real-time optimization. To address these limitations, this work introduces an intelligent machine- learning-based prediction framework for CaF2/Ag/BP/MXene/BP SPR biosensors using regression models to learn nonlinear relationships between structural parameters and performance metrics, including sensitivity and resonance wavelength. The proposed data-driven approach enables faster and more accurate performance estimation without exhaustive simulations, supporting rapid optimization across diverse operating scenarios. By combining plasmonic nanostructures with AI- assisted predictive modeling, this study establishes a foundation for intelligent, self-optimizing SPR diagnostic platforms suitable for next-generation biomedical applications.