Abstract

The research on the rotational triboelectric nanogenerators (R-TENGs) is actively conducted by numerous groups, but its friction-based operation mechanism makes it challenging to simultaneously ensure electrical performance (including output voltage and power) and mechanical performance (such as stability and durability). In this study, a physically intelligent Hidden regulator-assisted design-based R-TENG (HR-TENG) is developed based on the self-adaptive mechanical kinetic design. The relationships between engaged forces and the output performance of R-TENG are investigated, and the normal force acting on the contact interface is unveiled as a hidden core of the output performance of R-TENG. Here, HR-TENG traces the optimal working conditions in real-time according to input energy conditions by itself with spontaneously adjusted friction by controlling the normal force with mechanically self-adjustable components. HR-TENG can be operated in a wide range of conditions of input energy source, and exhibit self-adaptive electrical performance while maintaining mechanical performance depending on the input energy conditions without human intervention. The behaviors of the mechanically self-adjustable components depending on the operation of HR-TENG are analyzed theoretically and experimentally based on kinetic dynamics, and the effect of the normal force on the electrical and mechanical output performances is investigated. The average coefficient of inner friction of HR-TENG is 0.27, and the maximum output voltage and peak power are 254 V and 0.726 mW, respectively. As a proof-of-concept demonstration, the electrical output of HR-TENG under a wide range of input wind conditions (wind speed of 4–22 m/s) is investigated, and the real-time tracing ability of HR-TENG to the optimal working conditions in the continuously changing wind environment is analyzed. The kinetic design of HR-TENG with the hidden regulator (normal force), which affects multiple direct factors of electrical and mechanical performances, can be introduced to various R-TENGs as a key strategy, and thus, it can be expected to serve as a design guideline for futuristic physical intelligence-assisted smart energy harvesters.