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

The health sector is focusing on the wellness of the society, is advancing in the phases of diagnosis and treatment. Biosensors 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 global scale. It is important to detect these kinds of diseases on an early stage to reduce the risk of spreading. For the analysis of Covid-19 influenza, tests such as Rapid Antigen Test (RAT), True NAT, CBNAAT and the commonly done RPT PCR were utilised. This proposal describes a non-invasive, quick and practical method for sensing the 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 from exhaled sniff, 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.