Harnessing energy from low-frequency and low-amplitude vibrating sources using triboelectric nano generator

Abstract

The ability to transform mechanical energy into electrical power is an innovative feature of Dielectric Elastomer Generators (DEGs) that have emerged as promising electromechanical devices for harvesting energy from unexpected sources. DEGs are different from conventional energy harvesting techniques in that they are compact, have an easy-to-fabricate structure, and are devoid of any revolving parts. One self-powered subclass of DEGs that excels in extracting energy from low-frequency and low-amplitude mechanical sources is the triboelectric Nano generator (TENG). In order to fully examine the performance of TENGs in practical circumstances, this work presents a modified model that accounts for variations in amplitude, frequency, and the relative permittivity of the layers of elastomer. This study investigates the performance of a modified triboelectric nanogenerator (TENG) using both experimental and simulation methods. A custom-designed TENG prototype was fabricated using elastomer materials Silk fibroin as top layer and PET as bottom layer with varying dielectric constants. Experimental assessments were carried out using a low-frequency mechanical shaker, while COMSOL Multiphysics and MATLAB were employed for simulations. Key parameters affecting TENG performance—frequency, relative permittivity, and separation distance were analyzed. Results indicate that output voltage increases with frequency up to 65 Hz, beyond which it stabilizes. Higher relative permittivity materials significantly enhance charge storage, leading to improved voltage and power generation. An optimal separation distance of 0.2 mm was identified for maximizing electrostatic interactions. Comparative analysis with existing models confirms the predictive accuracy of the modified performance model. These findings highlight the potential of TENGs for efficient low-frequency energy harvesting in wearable and environmental applications.