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Researchers Develop COVID-19 Sensor for Wearables

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A Johns Hopkins University-developed COVID-19 sensor could revolutionise virus testing by improving accuracy and speeding up a process that many people found frustrating during the pandemic. The sensor, according to the researchers, combines PCR-like accuracy with the speed of fast antigen tests and might be used for mass testing in airports, schools, and hospitals.

The sensor, which requires no sample preparation and little operator knowledge, offers a significant advantage over conventional testing methods, particularly for population-wide testing, according to the researchers.

“The technique is as simple as putting a drop of saliva on our device and getting a negative or a positive result,” claimed the senior authors of the study, Associate Professor of Mechanical Engineering, Ishan Barman and Professor of Chemical and Biomolecular Engineering, David Gracias.

According to Barman, the new method, which is not yet on the market, addresses the shortcomings of the two most often used COVID-19 tests -the PCR and fast tests. The approach is unique in that it is label-free, requiring no further chemical changes such as molecular labelling or antibody functionalisation. As a result, the sensor could be used in wearable gadgets in the future.

PCR tests are extremely accurate, but they need a time-consuming sample preparation, with results taking hours or even days to analyse in a lab. Rapid tests, on the other hand, which check for the presence of antigens, are less effective in detecting early infections and asymptomatic cases and can result in incorrect results.

The sensor is almost as sensitive as a PCR test and just as quick as a fast antigen test. The sensor achieved 92 per cent accuracy in detecting SARS-COV-2 in saliva samples during the first testing, which is comparable to PCR techniques. The sensor was also extremely effective at detecting the presence of other viruses, such as H1N1 and Zika.

Large area nanoimprint lithography, surface-enhanced Raman spectroscopy (SERS), and machine learning are used to create the sensor. It can be used for mass testing on rigid or flexible surfaces in disposable chip formats.

Another significant feature of the technology is its use of modern machine learning algorithms to detect very tiny signs in spectroscopic data, allowing researchers to pinpoint the virus’s presence and concentration.

The large-area, flexible field enhancing metal-insulator antenna (FEMIA) array is the essential component of the method. Within this array, a sample of saliva is applied to the material and analysed using surface-enhanced Raman spectroscopy. This technique makes use of laser light to investigate the manner in which the molecules of the examined specimen vibrate.

The nanostructured FEMIA significantly strengthens the virus’s Raman signal, allowing the system to detect the presence of a virus even if only minor traces exist in the sample. From doorknobs and building entrances to masks and textiles, the sensor material can be applied to any surface. It might also be used with a hand-held testing device for quick checks in congested areas such as airports or stadiums.

Researchers are still striving to improve the technique and test it with patient samples. The intellectual property connected with it has been patented by Johns Hopkins Technology Ventures, and the team is looking for licencing and commercialisation prospects.

The study was funded by the National Science Foundation’s Early-concept Grants for Exploratory Research (EAGER) programme and the Director’s New Innovator award from the National Institute of Health.

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