Self‐Powered Rockslide Multimodal Monitoring System Based on Charge‐Retention and Self‐Polarization Synergistically Enhanced Non‐Contact Triboelectric Nanogenerator

ABSTRACT Rockslide monitoring requires high‐sensitivity detection of precursor signals such as micro‐vibrations, rainfall, and the direction of vibration sources. Most existing technologies struggle to simultaneously capture the aforementioned multimodal precursor information, which limits the accuracy and timeliness of early warnings. This work proposed a self‐powered multi‐modal vibration‐rainfall rockslide monitoring node (MVRN) based on a non‐contact triboelectric nanogenerator (NC‐TENG). By optimizing the BaTiO 3 /PVDF charge blocking layer composition and the thickness ratio of blocking/generation layers, charge‐retention and self‐polarization synergistically enhanced the output performance of NC‐TENG. The optimized NC‐TENG delivers a 31.8 V output at 0.1 mm gap, with an 8 V/mm sensitivity, 84 ms fast response (vs. 3 V/mm, 127.09 ms before optimization), and excellent adaptability to −20°C to 60°C temperatures and 25%–70% humidity. Thanks to the outstanding sensitivity, the multi‐directional NC‐TENG could detect vibration amplitudes as low as 0.1 mm and identify the direction of the vibration source. The self‐powered MVRN comprised multi‐directional NC‐TENG, the rainfall monitoring module, and a solar cell, enabling more accurate rockslide prediction through real‐time data acquisition, multi‐parameter threshold analysis, and cloud‐based early warning. This work provided a viable paradigm for developing smart multimodal geological hazard monitoring nodes.