Mathematical modeling of a MoSe2-based SPR biosensor for detecting SARS-CoV-2 at nM concentrations

The rapid and accurate detection of SARS-CoV-2 remains a critical challenge in biosensing technology, necessitating the development of highly sensitive and selective platforms. In this study, we present a mathematical modeling approach to optimize a MoSe2-based Surface Plasmon Resonance (SPR) biosensor for detecting the novel coronavirus at nM scale. Using the Transfer Matrix Method (TMM), we systematically optimize the biosensor's structural parameters, including silver (Ag), silicon nitride (Si3N4), molybdenum diselenide (MoSe2), and thiol-tethered single-stranded DNA (ssDNA) layers, to enhance sensitivity, detection accuracy, and optical performance. The results indicate that an optimized 45 nm Ag layer, 10 nm Si3N4 layer, and monolayer MoSe2 configuration achieves a resonance shift (Delta theta) of 0.3 degrees at 100 nM, with a sensitivity of 197.70 degrees/RIU and a detection accuracy of 5.24 x 10(-)2. Additionally, the incorporation of a 10 nm ssDNA functionalization layer significantly enhances molecular recognition, lowering the limit of detection (LoD) to 2.53 x 10(-)5 and improving overall biosensing efficiency. Sys(5) (MoSe2 + ssDNA) outperforms Sys(4) (MoSe2 without ssDNA) in terms of specificity and reliability, making it more suitable for practical applications. These findings establish the MoSe2-based SPR biosensor as a highly promising candidate for SARS-CoV-2 detection, offering a balance between high sensitivity, optical stability, and molecular selectivity, crucial for effective viral diagnostics.