An Intelligent Wearable Filtration System for Health Management

Shuo Shi, Yifan Si, Zihua Li, Shuo Meng, Shuai Zhang, Hanbai Wu, Chuanwei Zhi, Weng Fu Io, Yang Ming, Dong Wang, Bin Fei, Haitao Huang, Jianhua Hao, Jinlian Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)

Abstract

To develop intelligent wearable protection systems is of great significance to human health engineering. An ideal intelligent air filtration system should possess reliable filtration efficiency, low pressure drop, healthcare monitoring function, and man-machine interactive capability. However, no existing intelligent protection system covers all these essential aspects. Herein, we developed an intelligent wearable filtration system (IWFS) via advanced nanotechnology and machine learning. Based on the triboelectric mechanism, the fabricated IWFS exhibits a long-lasting high particle filtration efficiency and bacteria protection efficiency of 99% and 100%, respectively, with a low-pressure drop of 5.8 mmH2O. Correspondingly, the charge accumulation of the optimized IWFS (87 nC) increased to 3.5 times that of the pristine nanomesh, providing a significant enhancement of the particle filtration efficiency. Theoretical principles, including the enhancement of the β-phase and the lower surface potential of the modified nanomesh, were quantitatively investigated by molecular dynamics simulation, band theory, and Kelvin probe force microscopy. Furthermore, we endowed the IWFS with a healthcare monitoring function and man-machine interactive capability through machine learning and wireless transmission technology. Crucial physiological signals of people, including breath, cough, and speaking signals, were detected and classified, with a high recognition rate of 92%; the fabricated IWFS can collect healthcare data and transmit voice commands in real time without hindrance by portable electronic devices. The achieved IWFS not only has practical significance for human health management but also has great theoretical value for advanced wearable systems.

Original languageEnglish
Pages (from-to)7035-7046
Number of pages12
JournalACS Nano
Volume17
Issue number7
DOIs
Publication statusPublished - 11 Apr 2023

Keywords

  • electrostatic enhancement
  • intelligent wearables
  • machine learning
  • nanotechnology
  • smart mask

ASJC Scopus subject areas

  • General Materials Science
  • General Engineering
  • General Physics and Astronomy

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