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  5. Ultra‐High Sensitivity Real‐Time Monitoring of Landslide Surface Deformation via Triboelectric Nanogenerator

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Article
en
2024

Ultra‐High Sensitivity Real‐Time Monitoring of Landslide Surface Deformation via Triboelectric Nanogenerator

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en
2024
DOI: 10.1002/adma.202410471

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Zhong Lin Wang
Zhong Lin Wang

Beijing Institute of Technology

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Chao Wang
Yang Yu
Xiaosong Zhang
+5 more

Abstract

Abstract Monitoring surface deformation is crucial for the early warning of landslides, facilitating timely preventive measures. Triboelectric nanogenerator (TENG) demonstrates great potential for self‐powered distributed monitoring in remote and power‐scarce landslide areas. However, landslides deform typically at a rate of a few millimeters per day (mm d −1 ), making it challenging for TENG to directly monitor the deformation process. Herein, a method for monitoring surface deformation of landslides by constructing an ultra‐low‐speed triboelectric displacement sensor (US‐TDS) is reported. Utilizing a force storage‐release device and an accelerator, the US‐TDS can produce obvious sensing signals at a linear input speed of 4.32 mm d −1 . The coefficient of determination ( R 2 ) for the fitting curve of the pulse signals within the speed range of 21.6 to 129.6 mm d −1 reaches 0.999. Moreover, US‐TDS can detect deformation displacement as small as 0.0382 mm. The stability of US‐TDS displacement measurements is confirmed at a speed of 108 mm d −1 , with relative errors under 1%. Ultimately, a real‐time monitoring and early warning system for landslide surface deformation is constructed and verified through a combination of indoor simulations and outdoor experiments. This work provides a feasible solution for the scientific monitoring and early warning of the landslide development.

How to cite this publication

Chao Wang, Yang Yu, Xiaosong Zhang, Pengfei Wang, X. J. Bi, Hengyu Li, Zhong Lin Wang, Tinghai Cheng (2024). Ultra‐High Sensitivity Real‐Time Monitoring of Landslide Surface Deformation via Triboelectric Nanogenerator. , DOI: https://doi.org/10.1002/adma.202410471.

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Publication Details

Type

Article

Year

2024

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/adma.202410471

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