SPR Biosensor Based on Bilayer MoS2 for SARS-CoV-2 Sensing

Abstract

The COVID-19 pandemic has highlighted the urgent need for rapid, sensitive, and reliable diagnostic tools for detecting SARS-CoV-2. In this study, we developed and optimized a surface plasmon resonance (SPR) biosensor incorporating advanced materials to enhance its sensitivity and specificity. Key parameters, including the thickness of the silver layer, silicon nitride dielectric layer, molybdenum disulfide (MoS₂) layers, and ssDNA recognition layer, were systematically optimized to achieve the best balance between sensitivity, resolution, and attenuation. The optimized configuration, consisting of a 45 nm silver layer, a 13 nm silicon nitride layer, 2 MoS₂ layers, and a 5 nm ssDNA layer, demonstrated superior performance for detecting SARS-CoV-2 in PBS solution. The biosensor exhibited high sensitivity at low viral concentrations, achieving a sensitivity of 375.01°/RIU, a detection accuracy of 0.002, and a quality factor of 38.34 at 1.0 mM SARS-CoV-2 concentration. Performance metrics validated the sensor's capability for reliable detection, particularly in early-stage diagnostics where timely intervention is critical. Moreover, the biosensor's linear response to refractive index changes confirmed its potential for quantitative viral concentration analysis. This study underlines the significance of integrating advanced materials, such as MoS₂ and silicon nitride, to enhance SPR biosensor performance. The findings establish the proposed biosensor as a robust and precise diagnostic tool for SARS-CoV-2 detection, with potential applications in clinical diagnostics and epidemiological monitoring.

Keywords: MoS2; SARS-CoV-2; biosensor; silicon nitride; surface plasmon resonance.

Figure 1

Schematic representation of the proposed SPR Biosensor for detecting the novel coronavirus. (a): SPR biosensor based on bilayer MoS₂; (b): Baseline biosensor.

Figure 2

Analysis of SPR biosensor performance for different configurations studied in this work. (a) Reflectance curves as a function of the angle of incidence for all systems, highlighting the variation in SPR dip characteristics based on the materials used. (b) Attenuation percentage, showing the efficiency of light confinement for each configuration. (c) Full-width at half maximum (FWHM) of the SPR curves, indicating the sharpness of the resonance peaks and overall resolution. (d) Sensitivity enhancement relative to the baseline configuration (Sys₀), emphasizing the impact of advanced materials on improving detection capabilities. These results demonstrate the progression from the basic system in water (Sys₀) to configurations with enhanced sensitivity and optimized SPR properties.

Figure 3

Optimization of silver layer thickness based on Sys₈. (a) Reflectance curves as a function of the angle of incidence for different Ag thicknesses (40–65 nm) in PBS solution, compared to the baseline system (Ag_base) with initial parameters from Table S2 in water. (b) Attenuation percentage, indicating the efficiency of light confinement for each Ag thickness. (c) Full-width at half maximum (FWHM) of the SPR resonance curves, highlighting the trade-off between resolution and plasmonic confinement as the Ag thickness increases. (d) Sensitivity enhancement relative to the baseline system, showing improved sensitivity with increasing Ag thickness.

Figure 4

Optimization of silicon nitride (Si₃N₄) thickness for the SPR biosensor configuration based on Sys₈. (a) Reflectance curves as a function of the angle of incidence for different Si₃N₄ thicknesses (5–20 nm) in PBS solution, compared to the baseline system (Si₃N_{4_base}) with initial parameters from Table S2 in water. (b) Attenuation percentage for each thickness. (c) Full-width at half maximum (FWHM) of the SPR resonance curves, indicating changes in resolution as the thickness increases. (d) Sensitivity enhancement, which is relative to the baseline system.

Figure 5

Optimization of the number of MoS₂ layers for the SPR biosensor. (a) Reflectance curves as a function of the angle of incidence for systems with varying numbers of MoS₂ layers (1–6) in PBS solution, compared to the baseline system (L_1base) with optimized silver and silicon nitride thicknesses in water. (b) Attenuation percentage, showing the impact of increasing MoS₂ layers on energy confinement. (c) Full-width at half maximum (FWHM) of the SPR resonance curves, indicating changes in resolution with additional MoS₂ layers. (d) Sensitivity enhancement relative to the baseline system, showing the effect of the number of MoS₂ layers on sensor sensitivity.

Figure 6

Optimization of ssDNA layer thickness for the SPR biosensor. (a) Reflectance curves as a function of the angle of incidence for varying ssDNA thicknesses (3.2–50 nm) in PBS solution, compared to the baseline system (ssDNA_{3.2nm_base}) with the initial ssDNA parameters, optimized silver and silicon nitride thicknesses, and two MoS₂ layers in water. (b) Attenuation percentage, demonstrating the effect of increasing ssDNA thickness on energy confinement. (c) Full-width at half maximum (FWHM) of the SPR resonance curves, highlighting changes in resolution as the ssDNA layer thickness increases. (d) Sensitivity enhancement relative to the baseline system, showing the relationship between ssDNA thickness and biosensor sensitivity.

Figure 7

Performance of the optimized SPR biosensor for SARS-CoV-2 detection at varying viral concentrations. (a) SPR reflectance curves as a function of the angle of incidence, showing shifts in the resonance dip due to changes in refractive index at different SARS-CoV-2 concentrations. (b) Attenuation percentage, highlighting the effect of viral concentration on energy confinement within the biosensor. (c) Full-width at half maximum (FWHM) of the SPR resonance curves, demonstrating the resolution changes as the virus concentration increases. (d) Sensitivity enhancement relative to the baseline system (n = 1.334 in PBS), validating the biosensor’s capacity to detect and differentiate SARS-CoV-2 concentrations through refractive index variations.

Figure 8

Key performance metrics of the optimized SPR biosensor for SARS-CoV-2 detection at varying viral concentrations. (a) Angular variation (Δθ), indicating the shift in the resonance angle corresponds to refractive index changes induced by viral adsorption. (b) Sensitivity to refractive index changes (°/RUI), highlighting the biosensor’s responsiveness to varying SARS-CoV-2 concentrations. (c) Detection accuracy, representing the precision of resonance angle determination under different viral loads. (d) Quality factor (RIU⁻¹), illustrating the balance between sensitivity and resolution as a function of virus concentration.

Figure 9

Key performance metrics of the optimized SPR biosensor for SARS-CoV-2 detection at varying very-low viral concentrations from 0.01 to 10 mM. (a) Angular variation (Δθ). (b) Sensitivity to refractive index changes (°/RUI). (c) Detection accuracy. (d) Quality factor (RIU⁻¹).