Dataset from Minimal False-Alarm Touch-Based Detection of SARS-CoV-2 Virus Particles Using Poly-Aptamers

Background: The purpose of this study was to address the challenges of real-time SARS-CoV-2 detection, as existing diagnostic tools required extensive sample preparation and expensive laboratory equipment to produce accurate results. The study aimed to develop a rapid, reliable, and accessible detection method capable of identifying the virus with minimal false positives.

Materials/Methods: The study focused on building a touch-screen sensor array designed to directly capture, detect, and identify model SARS-CoV-2 virus particles without complex processing. The system integrated advanced sensing and engineering technologies developed by an interdisciplinary team of General Electric (GE) research scientists and engineers. The design leveraged the team’s prior scientific and engineering work to enhance detection sensitivity and specificity while maintaining simplicity and real-time capability.

Outcome/Impact: The study successfully demonstrated a proof-of-concept touch-screen sensor array capable of real-time detection of SARS-CoV-2 particles. The intended outcome was to provide an efficient, low-cost, and scalable diagnostic platform suitable for widespread deployment in both clinical and community settings.