Dynamic Pore Modulation of Stretchable Electrospun Nanofiber Filter for Adaptive Machine Learned Respiratory Protection
Jaeho Shin, Seong‐Min Jeong, Jin‐Mo 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
Abstract
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.