Dynamic Pore Modulation of Stretchable Electrospun Nanofiber Filter for Adaptive Machine Learned Respiratory Protection

Autor:	Costas P. Grigoropoulos, Seongmin Jeong, Sukjoon Hong, Seung Hwan Ko, Yun Young Choi, Yoonsoo Rho, Munju Kim, Jung Il Choi, Jaeho Shin, Jae Gun Lee, Jinmo Kim, Joonhwa Choi, Seong-Yoon Kim
Rok vydání:	2021
Předmět:	business.product_category Computer science Nanofibers General Engineering General Physics and Astronomy Control engineering Air Filters Electrospun nanofibers Modulation Filter (video) Occupational Exposure General Materials Science Pneumatic flow control Respirator business Filtration Air filter
Zdroj:	ACS Nano. 15:15730-15740
ISSN:	1936-086X 1936-0851
DOI:	10.1021/acsnano.1c06204
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02378f37a487c0942449c85011dce643 https://doi.org/10.1021/acsnano.1c06204 Zobrazit plný text záznamu