Analytical Modelling and Simulation of Graphene Based Biosensor to Detect SARS-COV-2 from Aerosal Particles

Abstract

The health sector, focusing on the wellness of society, is advancing in the phases of diagnosis and treatment. Biosensor-based devices are used to diagnose a variety of human diseases. Recently, there was a sudden hike in the human mortality rate by chronic diseases caused by mutants of SARS-COV-2, on a global scale. It is important to detect these kinds of diseases at an early stage to reduce the risk of spreading. For the analysis of Covid – 19 influenza, tests such as rapid antigen tests, True NAT, CBNAAT, and the commonly done RPT PCR were utilized. This proposal describes a non-invasive, quick, and practical method for sensing at-risk or infected persons with SARS-COV-2, aiming at controlling the epidemic. The proposed method employs a breath sensing device consisting of a graphene field effect transistor biosensor which can identify disease-specific biomarkers in exhaled breath, hence allowing speedy and precise detection. This test aids screening of large populations as it is simple and quick and emerges as a promising candidate for SARS-COV-2 tests due to a high sensitivity. This work justifies the accurate diagnosis of Severe Acute Respiratory Syndrome COV 2 from aerosol particles by GFET Biosensor.