Wirelessly sensor technology for verifiable decontamination of N95 respirator masks

In response to the N95 mask shortage caused by the COVID-19 pandemic, the CDC has recognized moist-heat as one of the most effective and accessible methods for decontaminating N95 masks for reuse. However, it is challenging to reliably deploy this technique in healthcare settings due to a lack of specialized equipment capable of ensuring properdecontamination conditions. This NSF RAPID SaTC founded research project (Award #2031077) tackles the urgent research needs to assure and inform healthcare workers of the conditions necessary for mask decontamination processes in ovens with known risks of non-uniform heating, and rapidly encourage the deployment of highly scalable and trustworthy sensor network technology for monitoring and verification of individual mask decontamination. The research has particular focus to small clinics, rural facilities, and developing countries that lack convenient access to dedicated N95 mask decontamination systems.


Last News

Our project full paper has been accepted to ACM IMWUT !
Prof. Josiah Hester release an interview on the project updates
Prof. Kevin Fu has been interviewed by Tech Time - Click on Detroit news. Here the interview video
We won the best poster runner-up award for COVID-19 Response Research at Sensys 2020!
N95 award image
Ph.D. student Yan Long presents the research at Sensys2020
Prof. Sara Rampazzi presents the project at the COVID-19 Research Lightning Round Webinar organized by COVID Information Commons
Our project poster has been accepted to SenSys 2020 COVID-19 Pandemic Response track!


  • PI: Prof. Kevin Fu (University of Michigan)

  • Co-PI: Prof. Sara Rampazzi (University of Florida)

  • Prof. Josiah Hester (Northwestern University)

  • Yan Long, Ph.D. student (University of Michigan)

  • Alex Curtiss, Ph.D. student (Northwestern University)