# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 4 Conclusions  
In summary, the optimized graphene-based SPR biosensor configuration developed in this study, denoted as \(\text{Sys}_{8}\), demonstrates significant potential for the sensitive detection of SARS-CoV-2 across various concentrations. By incorporating layers of silver, silicon nitride, single-layer graphene, and a thiol-tethered ssDNA layer, the sensor effectively utilizes surface plasmon resonance to enhance sensitivity, specificity, and stability. The combination of these materials, particularly the use of graphene and silicon nitride, enhances light-matter interaction at the metal-dielectric interface, thereby achieving a balanced response in metrics critical for biosensing applications, including sensitivity enhancement, attenuation control, and FWHM management. Particularly, the optimized \(\text{Sys}_{8}\) configuration exhibited a high sensitivity of 315.91\({}^{\circ}\) / RIU at moderate virus concentrations (275 mM), proving especially suitable for early-detection scenarios. Additionally, it achieved a maximum shift (\(\Delta\theta\)) of 4.2\({}^{\circ}\) and a peak detection accuracy of 0.60, both of which indicate precise responsiveness to refractive index changes induced by virus binding events. The sensor maintained a high figure of merit (54.66 RIU\({}^{-1}\)) at lower virus concentrations (150 mM), further supporting its application in scenarios where early detection and minimal viral loads are vital. A linear response was observed in sensitivity enhancement up to a SARS-CoV-2 concentration of 400 mM, beyond which a plateau effect suggested sensor saturation. This highlights the sensor's effective operational range, which is particularly relevant for detecting clinically significant viral loads. Despite challenges associated with high attenuation at elevated concentrations, the performance metrics across different virus concentrations confirm \(\text{Sys}_{8}\) as a robust candidate for SARS-CoV-2 detection in point-of-care diagnostics. Future studies should explore further material refinements and real-world testing to validate the applicability of Syss in clinical settings. **Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app142210724/s1](https://www.mdpi.com/article/10.3390/app142210724/s1)]([https://www.mdpi.com/article/10.3390/app142210724/s1](https://www.mdpi.com/article/10.3390/app142210724/s1)), Figure S1. Ag thickness: Linear fit of the sensitivity enhancement data, Figure S2. Silicon nitride thickness: Linear fit of the sensitivity enhancement data, Figure S3. Graphene layers: Linear fit of the sensitivity enhancement data, Figure S4. ssDNA layer: Linear fit of the sensitivity enhancement data, Figure S5. SARS-CoV-2: Linear fit of the sensitivity enhancement data. Table S1. The configuration of the systems analyzed in this study, Table S2. Initial parameters of the proposed SPR biosensor for sensing SARS-CoV-2, Table S3. Metric values of proposed SPR Biosensors, Table S4. Metric values of Sys8 configuration by changing the silver thickness, Table S5. Metric values of Sys8 by changing the silicon nitride thickness, Table S6. Metric values of Sys8 by increasing the number of graphene layers, Table S7. Metric values of Sys8 by changing the ssDNA thickness, Table S8. Optimized parameters of Sys8 and refractive index at different SARS-CoV-2 concentrations, Table S9. Metric values of optimized Sys8 configuration after SARS-CoV-2 adsorption at different concentrations, Table S10. Performance metrics of optimized Sys8 configuration after the SARS-CoV-2 adsorption at different concentrations. T.T. : conceptualization, funding acquisition, writing--original draft. F.A.A. : formal analysis, methodology. K.I.P.-P.: formal analysis, methodology, resources. C.H.-B. : validation, resources. C.V.G. : conceptualization, software, investigation, writing--original draft. All authors have read and agreed to the published version of the manuscript.