Dynamic Pore Modulation of Stretchable Electrospun Nanofiber Filter for Adaptive Machine Learned Respiratory Protection
- Authors
- Shin J.; Jeong S.; Kim J.; Choi Y.Y.; Choi J.; Lee J.G.; Kim, Seongyoon; Kim M.; Rho Y.; Hong S.; JUNG-IL CHOI; Grigoropoulos C.P.; Ko S.H.
- Issue Date
- Oct-2021
- Publisher
- AMER CHEMICAL SOC
- Keywords
- Algorithms; Fibers; Atmospheric chemistry
- Citation
- ACS NANO, v.15, no.10, pp 15730 - 15740
- Pages
- 11
- Journal Title
- ACS NANO
- Volume
- 15
- Number
- 10
- Start Page
- 15730
- End Page
- 15740
- URI
- https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6420
- DOI
- 10.1021/acsnano.1c06204
- ISSN
- 1936-0851
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 일반대학원 > 일반대학원 계산과학공학과 > 1. Journal Articles
- College of Science > 이과대학 수학 > 1. Journal Articles
Items in Scholar Hub are protected by copyright, with all rights reserved, unless otherwise indicated.