Interfacial Polarization Strategy to Electroactive Poly(lactic acid) Nanofibers for Humidity-Resistant Respiratory Protection and Machine Learning-Assisted Monitoring

• Published in: ACS applied materials & interfaces 2024-08, Vol.16 (34), p.45078-45090
• Main Authors: Tang, Mengke, Song, Xinyi, Wang, Cunmin, Jiang, Liang, Zhou, Yuhong, Wang, Yuanchunzhi, Zhu, Jintuo, Wang, Yanqing, Gao, Jiefeng, He, Xinjian, Xu, Huan
• Format: Article
• Language: English
• Subjects: Energy, Environmental, and Catalysis Applications; Humans; Humidity; Machine Learning; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; Nanofibers - chemistry; Polyesters - chemistry
• ISSN: 1944-8244; 1944-8252; 1944-8252
• DOI: 10.1021/acsami.4c12653

The advancement of intelligent and biodegradable respiratory protection equipment is pivotal in the realm of human health engineering. Despite significant progress, achieving a balance between efficient filtration and intelligent monitoring remains a great challenge, especially under conditions of high relative humidity (RH) and high airflow rate (AR). Herein, we proposed an interfacial stereocomplexation (ISC) strategy to facilitate intensive interfacial polarization for poly­(lactic acid) (PLA) nanofibrous membranes, which were customized for machine learning-assisted respiratory diagnosis. Theoretical principles underlying the facilitated formation of the electroactive phase and aligned PLA chains were quantitatively depicted in the ISC-PLA nanofibers, contributing to the increased dielectric constant and surface potential (as high as 2.2 and 5.1 kV, respectively). Benefiting from the respiration-driven triboelectric mechanisms, the ISC-PLA demonstrated a high PM0.3 filtration efficiency of over 99% with an ultralow pressure drop (75 Pa), even in challenging circumstances (95 ± 5% RH, AR of 85 L/min). Furthermore, we implemented the ISC-PLA with multifunction respiratory monitoring (response time of 0.56 s and recovery time of 0.25 s) and wireless transmission technology, yielding a high recognition rate of 83% for personal breath states. This innovation has practical implications for health management and theoretical advancements in respiratory protection equipment.