High Sensitivity and Specificity in Healthcare: Design and Validation of a Novel SiNW-FET Biosensor for Viral Detection

• Published in: IEEE access Vol. 12; pp. 112308 - 112319
• Main Authors: Hadded, Ahmed, Ayed, Mossaad Ben, Abdullah Alshaya, Shaya
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antibodies; Biological system modeling; Biomolecules; Biosensor; Biosensors; Detection algorithms; Effectiveness; Electrodes; Equipment costs; Health care; Impedance; Infections; Medical services; Monitoring; nanoscale; Nanoscale technology; Real time; Sensitivity; Sensors; simulation; Telemedicine; Viral infections; virus detection
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3442428

Impedance biosensing offers a highly sensitive and non-invasive method for detecting biomolecules and monitoring cellular activities, which is crucial for timely diagnosis and management of viral infections. Traditional methods, although effective, often involve costly and cumbersome equipment and require frequent hospital visits, making them less practical for continuous monitoring. This study introduces a novel biosensor based on SiNW-FET coupled with an advanced preamplifier designed to detect viruses through impedance changes caused by the interaction of antibodies and antigens. Utilizing COMSOL/MATLAB simulations, this research accurately models the sensor's response to electrode functionalization with antibodies, and evaluates how nanoscale adjustments in electrode size and spacing can enhance biosensing capabilities. The proposed system promises continuous patient monitoring, alerting healthcare providers to critical changes that might indicate viral infections or significant shifts in cellular behavior. The performance of the sensor, validated through detailed simulations, demonstrates its potential as an effective tool for healthcare, ensuring timely interventions and improved patient outcomes. The proposed sensor design achieved an accuracy of approximately 92%, a sensitivity of about 85%, and a specificity of roughly 99%, demonstrating its high effectiveness in detecting viral infections and ensuring accurate monitoring. The sensor highlighted its potential as an effective tool for real-time, non-invasive healthcare monitoring.