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Mask to detect Covid within 90 mins: Thanks to MIT, Harvard

A team of scientists was already working on wearable sensors, such as masks, when the coronavirus pandemic hit last year, prompting them to refocus their efforts towards Covid-19.

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A team of researchers from the Massachusetts Institute of Technology (MIT) and Harvard University have designed a prototype of a mask that can detect coronavirus particles in the wearer’s breath.

The technology is modeled on paper-based diagnostics for the Ebola and Zika viruses developed by MIT engineering professor James Collins in the mid-2010s, MIT News reported.

Read more: Test and trace method helps curb spread of COVID-19: study

Collins later designed a sensor system called SHERLOCK, which is used to detect nucleic acids. A team of scientists was already working on wearable sensors, such as masks, when the coronavirus pandemic hit last year, prompting them to refocus their efforts towards Covid-19. The description of the prototype of a virus-detecting mask was published in the Nature Biotechnology journal on Monday.

According to the researchers, tiny freeze-dried SHERLOCK sensors were embedded inside a paper mask, so they could spot viral particles in the wearer’s breath. The wearer has to push a button that releases water from a small reservoir in the mask to activate the test. The result is ready within 90 minutes and only visible on the inside of the mask – for user privacy.

“We envision that this platform could enable next-generation wearable biosensors for first responders, healthcare personnel, and military personnel,” Collins told MIT News.

The scientists were also working on incorporating the new testing method in the lab coats of healthcare workers. The team tested many different types of fabric, including cotton, polyester, wool, and silk.

Read more: The beginning of the age of bioterrorism

“We ended up identifying a couple that are very widely used in the fashion industry for making garments,” Luis Soenksen, a venture builder at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health and one of the design’s co-developers, said. “The one that was the best was a combination of polyester and other synthetic fibers.”

RT with additional input by GVS News Desk

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