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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]

During these last years, the substantially biological field effect transistors (BioFET) are one of the most abundant classes of electronic sensors for biomolecular detection. The determination of glucose levels using these biosensors, especially in the medical diagnosis and food industries, is gaining popularity. Among them, ion-sensitive field effect transistor (ISFET) is considered one of the most intriguing approaches in electrical biosensitivity technology. The glucose sensor ISFET detects the glucose molecule by catalyzing glucose to gluconic acid and hydrogen peroxide in the presence of oxygen. In this paper, first of all we examine some of the main advantages in this field, the perspective of applications and the main issues in order to stimulate a broader interest in the development of biosensors based on ISFET and to extend their applications for a reliable and sensitive glucose analysis. Thereafter, a biosensor with field effect sensitive to the ions for the detection of glucose is modeled analytically. In the proposed model, the glucose concentration is presented according to the gate voltage. The simulated data show that the analytical model can be used with an electrochemical glucose sensor to predict mechanism's behavior of detection in the biosensors. [ABSTRACT FROM AUTHOR]