A theoretical approach for optimizing sliding-mode triboelectric nanogenerator based on multi-parameter analysis

Optimized design of sliding-mode triboelectric nanogenerator (TENG) that is triggered by relative sliding between dielectric layers through a friction process forms one of the key focuses in this community since it may enhance the feasibility of real applications in industries. We propose here a theoretical model based on multi-parameter analysis to provide a rational optimization strategy for design of sliding mode TENG. By combining multiple parameters into dimensionless variables, the normalized output performance is expressed by virtue of two compound parameters. The scaling laws are achieved between the normalized electric output and these compound parameters, involving device dimensions (sizes, dielectric layer thickness), electrical properties of the electrode and dielectric materials, loading conditions (loading force, frequency and motor process), and the circuit conditions (open/short circuit and load resistance). The scaling laws may provide a more comprehensive and rational optimization strategy for sliding-mode TENG based on multi-parameter analysis and may help to enhance the output performance of the device as either a smart sensor or an energy harvester.