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Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator

  • Jun Zhao
  • , Di Wang
  • , Fan Zhang
  • , Yuan Liu
  • , Baodong Chen
  • , Zhong Lin Wang*
  • , Jinshan Pan*
  • , Roland Larsson
  • , Yijun Shi*
  • *此作品的通讯作者
  • Luleå University of Technology
  • Beijing University of Chemical Technology
  • University of Sussex
  • Chinese Academy of Sciences
  • KTH Royal Institute of Technology

科研成果: 期刊稿件文章同行评审

摘要

An intelligent monitoring lubricant is essential for the development of smart machines because unexpected and fatal failures of critical dynamic components in the machines happen every day, threatening the life and health of humans. Inspired by the triboelectric nanogenerators (TENGs) work on water, we present a feasible way to prepare a self-powered triboelectric sensor for real-time monitoring of lubricating oils via the contact electrification process of oil-solid contact (O-S TENG). Typical intruding contaminants in pure base oils can be successfully monitored. The O-S TENG has very good sensitivity, which even can respectively detect at least 1 mg mL-1 debris and 0.01 wt % water contaminants. Furthermore, the real-time monitoring of formulated engine lubricating oil in a real engine oil tank is achieved. Our results show that electron transfer is possible from an oil to solid surface during contact electrification. The electrical output characteristic depends on the screen effect from such as wear debris, deposited carbons, and age-induced organic molecules in oils. Previous work only qualitatively identified that the output ability of liquid can be improved by leaving less liquid adsorbed on the TENG surface, but the adsorption mass and adsorption speed of liquid and its consequences for the output performance were not studied. We quantitatively study the internal relationship between output ability and adsorbing behavior of lubricating oils by quartz crystal microbalance with dissipation (QCM-D) for liquid-solid contact interfaces. This study provides a real-time, online, self-powered strategy for intelligent diagnosis of lubricating oils.

源语言英语
页(从-至)11869-11879
页数11
期刊ACS Nano
15
7
DOI
出版状态已出版 - 27 7月 2021
已对外发布

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