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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, Parvez Mahmud, MA and Kouzani, Abbas Z 2021, Design and numerical analysis of a graphene-coated SPR biosensor for rapid detection of the novel coronavirus, Sensors, vol. 21, no. 10, pp. 1-21, doi: 10.3390/s21103491.

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Title Design and numerical analysis of a graphene-coated SPR biosensor for rapid detection of the novel coronavirus
Author(s) Akib, Tarik Bin Abdul
Mou, Samia Ferdous
Rahman, Md Motiur
Rana, Md Masud
Islam, Md Rabiul
Mehedi, Ibrahim M
Parvez Mahmud, MA
Kouzani, Abbas ZORCID iD for Kouzani, Abbas Z orcid.org/0000-0002-6292-1214
Journal name Sensors
Volume number 21
Issue number 10
Article ID 3491
Start page 1
End page 21
Total pages 21
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2021-05-02
ISSN 1424-8220
Keyword(s) biosensor
coronavirus
COVID-19
molecular detection
rapid detection
SARS-CoV-2
sensor
spike receptor-binding domain
surface plasmon resonance
Summary 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 re-gion. 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.
Language eng
DOI 10.3390/s21103491
Indigenous content off
Field of Research 0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
0502 Environmental Science and Management
0602 Ecology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30151618

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.