Numerical Analysis of Coronavirus Detection Using Photonic Crystal Fibre-Based SPR Sensor

Coronavirus disease (COVID-19) is a worldwide health emergency caused by the coronavirus 2 (severe acute respiratory illness) (SARS-CoV-2). COVID-19 has a wide range of symptoms, making a definitive diagnosis difficult. The shortage of equipment for testing technology COVID-19 has resulted in long queues for COVID-19 testing, which is a major problem. COVID-19 testing is currently performed using sluggish and costly technology like single-photon emission computed tomography (SPECT), computed tomography (CT), positron emission tomography (PET), and enzyme-linked immunosorbent assay (ELISA). The gold standard test for diagnosing COVID-19 is real-time reverse transcriptase-polymerase chain reaction (RT-PCR), which necessitates highly skilled workers and has a lengthy turnaround time. However, rapid and affordable immunodiagnostic techniques (antigen or antibody tests) are also available with some trade off accuracy. Optical sensors are frequently employed in a variety of applications, because of their increased sensitivity, strong selectivity, rapid reaction times, and outstanding resolution. The use of photonic crystal fibre (PCF) is advantageous for the quick detection of the new coronavirus and is suggested with the use of a PCF-based (Au/BaTiO3/graphene) multilayered surface plasmon resonance (SPR) biosensor. The proposed sensor can quickly detect the COVID-19 virus in two different ligand-analyte environments: (i) the virus spike receptor-binding domain (RBD) as an analyte and monoclonal antibodies (mAbs) as a probe ligand, and (ii) monoclonal antibodies (IgG or IgM) as an analyte and the virus spike RBD as a probe ligand. The finite element method (FEM) is used to quantitatively examine the performance of the PCF-based multilayered SPR sensor.