ABSTRACT
This paper presents a performance comparison of heterostructure surface plasmon resonance (SPR) biosensors for the application of Novel Coronavirus SARS-CoV-2 diagnosis. The comparison is performed and compared with the existing literature based on the performance parameters in terms of several prisms such as BaF2, BK7, CaF2, CsF, SF6, and SiO2, several adhesion layers such as TiO2, Chromium, plasmonic metals such as Ag, Au, and two-dimensional (2D) transition metal dichalcogenides materials such as BP, Graphene, PtSe2 MoS2, MoSe2, WS2, WSe2. To study the performance of the heterostructure SPR sensor, the transfer matrix method is applied, and to analyses, the electric field intensity near the graphene-sensing layer contact, the finite-difference time-domain approach is utilized. Numerical results show that the heterostructure comprised of CaF2/TiO2/Ag/BP/Graphene/Sensing-layer has the best sensitivity and detection accuracy. The proposed sensor has an angle shift sensitivity of 390°/refractive index unit (RIU). Furthermore, the sensor achieved a detection accuracy of 0.464, a quality factor of 92.86/RIU, a figure of merit of 87.95, and a combined sensitive factor of 85.28. Furthermore, varied concentrations (0-1000 nM) of biomolecule binding interactions between ligands and analytes have been observed for the prospects of diagnosis of the SARS-CoV-2 virus. Results demonstrate that the proposed sensor is well suited for real-time and label-free detection particularly SARS-CoV-2 virus detection.
ABSTRACT
In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.