Selective Electrochemical Detection of SARS-CoV-2 Using Deep Learning.

COVID-19 has been in the headlines for the past two years. Diagnosing this infection with minimal false rates is still an issue even with the advent of multiple rapid antigen tests. Enormous data are being collected every day that could provide insight into reducing the false diagnosis.

SARS-CoV-2 Surveillance in Indoor Air Using Electrochemical Sensor for Continuous Monitoring and Real-Time Alerts.

The severe acute respiratory syndrome related coronavirus 2 (SARS-CoV-2) has spread globally and there is still a lack of rapid detection techniques for SARS-CoV-2 surveillance in indoor air. In this work, two test rigs were developed that enable continuous air monitoring for the detection of SARS-CoV-2 by sample collection and testing. The collected samples from simulated SARS-CoV-2 contaminated air were analyzed using an ultra-fast COVID-19 diagnostic sensor (UFC-19).

Rapid electrochemical detection of using nickel oxidation reaction on a rotating disk electrode.

A standalone electrochemical method for detecting the bacterium in water was developed using a nickel electrode and no biorecognition element. Electric current responses from different concentrations were recorded based on their interaction with a locally formed electrocatalyst. A rotating disk electrode was used to minimize the mass transport limitations at the interface.