posted on 2021-09-29, 12:05authored byJaeho Shin, Seongmin Jeong, Jinmo Kim, Yun Young Choi, Joonhwa Choi, Jae Gun Lee, Seongyoon Kim, Munju Kim, Yoonsoo Rho, Sukjoon Hong, Jung-Il Choi, Costas P. Grigoropoulos, Seung Hwan Ko
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.