Name
Title | ||
|---|---|---|
Automating decontamination of N95 masks for frontline workers in COVID-19 pandemic: poster abstract |
Abstract | ||
|---|---|---|
In response to the N95 mask shortage caused by the COVID-19 pandemic, the US 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 proper decontamination conditions. To this end, we developed a wireless sensor platform for moist-heat decontamination process verification, capable of monitoring hundreds of masks simultaneously in commercially available heating systems. Our easy-to-use, low-power, low-cost, scalable platform can be broadly deployed to protect front-line healthcare workers by lowering their risk of infection from reused N95 masks.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3384419.3430613 | SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems
Virtual Event
Japan
November, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7590-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Yan | 1 | 209 | 32.64 |
| Alexander Curtiss | 2 | 0 | 0.68 |
| Sara Rampazzi | 3 | 6 | 2.65 |
| Josiah D. Hester | 4 | 138 | 18.13 |
| Kevin Fu | 5 | 2281 | 167.38 |