High-sensitivity detection of SARS-CoV-2 using optimized carbon nanotube field-effect transistor (CNTFET) geometry: a numerical approach.

This study presents a comprehensive numerical analysis of carbon nanotube (CNT)-based fieldeffect transistor (FET) sensors designed to detect SARS-CoV-2, the virus responsible for COVID-19. While graphene-based FETs have been extensively studied, our research emphasizes the unique advantages of CNTs, including enhanced sensitivity and seamless integration into existing technologies. Through numerical simulations, we evaluated the influence of CNT geometry, specifically length and diameter, on FET sensor performance. The results demonstrate that optimized CNT geometries significantly enhance sensor sensitivity and accuracy, providing superior capabilities for the rapid and precise detection of SARS-CoV-2. These findings underscore the potential for developing reliable, fast, and cost-effective CNT-based FET biosensors for effective pandemic response and broader public health applications. [ABSTRACT FROM AUTHOR]