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Diffusion-Induced Ingress of Angiotensin-Converting Enzyme 2 into the Charge Conducting Path of a Pentacene Channel for Efficient Detection of SARS-CoV-2 in Saliva Samples
- Source :
- ACS Sensors. 7:3006-3013
- Publication Year :
- 2022
- Publisher :
- American Chemical Society (ACS), 2022.
-
Abstract
- Rapid and accurate identification of a pathogen is crucial for disease control and prevention of the epidemic of emerging infectious like SARS-CoV-2. However, no foolproof gold standard assay exists to date. Nucleic acid-based molecular diagnostic tests have been established for identifying COVID-19. However, viral RNAs are highly unstable in handling with poor laboratory procedures, leading to a false negative that accelerates the spread of the disease. Detection of the spike protein (S1) of the SARS-CoV-2 virus through a proper receptor, commonly used in antigen-based rapid testing kits, also suffers from false-negative predictions due to decreasing viral titers in clinical specimens. Organic field-effect transistor (OFET)-based sensors can be highly sensitive upon properly integrating receptors in the conducting channel. This work demonstrates how angiotensin-converting enzyme 2 (ACE2) molecules can be used as receptor molecules of the SARS-CoV-2 virus in the OFET platform. Integration of ACE2 molecules into pentacene grain boundaries has been studied through the statistical analysis of rough surfaces in terms of lateral correlation length and interface width. The uniform coating of ACE2 molecules has been confirmed through growth studies to achieve better ingress of the receptors into the conducting channel at the semiconductor/dielectric interface of OFETs. We have observed less than a minute detection time with 94% sensitivity, which is the highest reported value. The sensor works with a saliva sample, requiring no sample preparation or virus transfer medium. A prototype module developed for remote monitoring confirms the suitability for point-of-care (POC) application at large-scale testing in more crowded areas like airports, railway stations, shopping malls, etc.
Details
- ISSN :
- 23793694
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- ACS Sensors
- Accession number :
- edsair.doi.dedup.....4f945abe6fc1bc15ee0998dccacd884d