Stratified topography theory to understand frictional electrification for sliding triboelectric nanogenerators

Abstract

Researchers are used to investigating the influence of surface topography on the frictional electrification of sliding triboelectric nanogenerators TENGs from the perspective of a single-stratum topography; however, a stratified feature has shared reality in closer relationships. Here, we characterize the stratified feature of the topographies for the sliding TENGs, and link them to the electrification voltages, finding that the frictional electrification strongly depends on the characteristics of the small-scale component in a stratified topography, which suggest us to develop a stratified electrification model for mechanism reveal. Based on the dependence, we also succeed in identifying the stratified topographic characteristics with frictional electrification signals by machine learning including support vector machine and convolutional neural network, which can be envisioned as a tool for topography measurement. This is the first demonstration of a stratified topography theory for sliding TENGs, providing new insights into the mechanism reveal and functional application of frictional electrification.

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