The recent emergence of highly contagious respiratory disease and the underlying issues of worldwide air pollution jointly heighten the importance of the personal respirator. However, the incongruence between the dynamic environment and nonadaptive respirators imposes physiological and psychological adverse effects, which hinder the public dissemination of respirators. To address this issue, we introduce adaptive respiratory protection based on a dynamic air filter (DAF) driven by machine learning (ML) algorithms. The stretchable elastomer fiber membrane of the DAF affords immediate adjustment of filtration characteristics through active rescaling of the micropores by simple pneumatic control, enabling seamless and constructive transition of filtration characteristics. The resultant DAF-respirator (DAF-R), made possible by ML algorithms, successfully demonstrates real-time predictive adapting maneuvers, enabling personalizable and continuously optimized respiratory protection under changing circumstances.
|Number of pages||11|
|Publication status||Published - 2021 Oct 26|
Bibliographical noteFunding Information:
This work is supported by the National Research Foundation of Korea (No. 2021R1A2B5B03001691). All experiments in this research associated with the human experiment were consulted and approved by the institutional review board (IRB) of Seoul National University (Approval number, 2008/003-023). Informed consent was obtained from the volunteers of the human experiments prior to participation in this study. The person displayed in and (S.J.) acknowledges and agrees with the use of his image in this article.
© 2021 American Chemical Society.
All Science Journal Classification (ASJC) codes
- Materials Science(all)
- Physics and Astronomy(all)