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Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus.
Akib, Tarik Bin Abdul; Mou, Samia Ferdous; Rahman, Md Motiur; Rana, Md Masud; Islam, Md Rabiul; Mehedi, Ibrahim M; Mahmud, M A Parvez; Kouzani, Abbas Z.
  • Akib TBA; Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.
  • Mou SF; Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.
  • Rahman MM; Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.
  • Rana MM; Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.
  • Islam MR; Faculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, Australia.
  • Mehedi IM; Department of Electrical and Computer Engineering (ECE) and Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia.
  • Mahmud MAP; School of Engineering, Deakin University, Geelong, VIC 3216, Australia.
  • Kouzani AZ; School of Engineering, Deakin University, Geelong, VIC 3216, Australia.
Sensors (Basel) ; 21(10)2021 May 17.
Article in English | MEDLINE | ID: covidwho-1234803
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Biosensing Techniques / COVID-19 / Graphite Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21103491

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Biosensing Techniques / COVID-19 / Graphite Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21103491