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  5. Self-Powered Sensing for Non-Full Pipe Fluidic Flow Based on Triboelectric Nanogenerators

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Article
en
2022

Self-Powered Sensing for Non-Full Pipe Fluidic Flow Based on Triboelectric Nanogenerators

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0 Files

en
2022
Vol 14 (2)
Vol. 14
DOI: 10.1021/acsami.1c20509

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Siyang He
Zheng Wang
Xiaosong Zhang
+4 more

Abstract

Fluidic flow monitoring of a non-full pipe is of great significance in the field of energy measurement and pipeline transportation. In this work, a monitoring method based on triboelectric nanogenerators for non-full pipe fluidic flow of large pipelines is proposed. Specifically, a triboelectric non-full pipe flow sensor (TNPFS) is fabricated, which can monitor the flow velocity and the liquid level simultaneously, and then the flow can be obtained by conversion. For flow velocity monitoring, the flexible blades slide between electrodes, generating periodic electrical signals. Interestingly, the frequencies of the voltage and flow velocities show a good linear relationship. For liquid level monitoring, according to the principle of liquid-solid contact electrification, a variable area interdigital electrode with a stable signal distributed on a polytetrafluoroethylene tube is designed. The experiments demonstrate that the peak number and trend of the voltage derivative curve are related to the liquid level. Finally, a real-time flow-monitoring system is established to effectively monitor the flow from 94 to 264 L/min. Compared with the actual measured flow, the error rate is under 1.95%. In addition to this, the TNPFS also has good responsiveness in sewage. This work provides a novel method for fluidic flow monitoring, especially the non-full pipe flow of large pipelines.

How to cite this publication

Siyang He, Zheng Wang, Xiaosong Zhang, Zitang Yuan, Yushan Sun, Tinghai Cheng, Zhong Lin Wang (2022). Self-Powered Sensing for Non-Full Pipe Fluidic Flow Based on Triboelectric Nanogenerators. , 14(2), DOI: https://doi.org/10.1021/acsami.1c20509.

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Publication Details

Type

Article

Year

2022

Authors

7

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1021/acsami.1c20509

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