A Load-Dependent Model of Triboelectric Nanogenerators for Surface Roughness Sensing

Abstract

The triboelectric nanogenerator (TENG) has the advantages of low cost, high efficiency, flexibility, lightness, and portability. Therefore, it has caused more and more attention as an electronic skin sensor. Generally, the research on intelligent components mainly focused on flexible pressure or temperature sensors. However, the surface roughness recognition of object material still remains a challenge. Meanwhile, most of the current surface roughness recognition methods need secondary processing of the sensor signal, and rarely use the signal generated by the sensor to directly identify the surface roughness. In this paper, the load-dependent electric field model is transplanted to the conductor-dielectric contact-separation TENG (CS-TENG), and the surface roughness of conductor can be differentiated theoretically based on the output of CS-TENG. To verify its effectiveness, the TENG devices were fabricated and the home-processed steels of different roughness act as the conductor layers. By contacting the conductor layer with the dielectric layer, the electric output signal based on contact induced electrification can be generated, which can be used to quantitatively estimate the surface roughness of steels. The test results demonstrate that the output of CS-TENG decreases with the increase of surface roughness, which coincides with the simulation prediction. This method is low cost and easy to implement, which provides a new design idea to extend the functions of TENGs as tactile electronic skins.