0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessAbstract 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.
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.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2024
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1002/adma.202410471
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access