Enhanced energy harvesting in rotational triboelectric nanogenerator via Gaussian process regression-based Bayesian optimization - Yoon, Jiyoung; Lee, Junhyeong; Ryu, Seunghwa; Park, Jinhyoung (Nano energy, 2025)
Triboelectric nanogenerators (TENGs) technology is a mechanical energy-harvesting technology with several advantages. Among the various TENG designs, the TENG with a rotational grating structure enables continuous power generation and features a simple design, making it the subject of extensive research. Until now, TENGs with fan-shaped gratings have mainly been investigated. In this study, we propose a methodology to first analyze the output performance of TENGs with various grating shapes through simulations and then apply a Gaussian process regression-based Bayesian optimization technique to derive the optimal grating shape that maximizes the output performance. Various grating shapes, derived using the proposed optimization methodology, were fabricated and specifically tested to validate this methodology. It was confirmed that the output performance trends, based on the grating shapes, were consistent between the optimization predictions and the experimental results. To investigate the optimal grating shapes for different target load resistances, the optimal grating shapes were also derived and examined at 10M, 100M, and 1,000MΩ. This work provides a new design approach for rotational grating-structure TENG, presenting innovative concept for designing high-output TENG with broad application potential in the field of energy harvesting. [Display omitted] •Bayesian optimization boosts energy by optimizing rotational TENG grating shapes.•Output of TENG with various grating shapes was evaluated via V-Q-α relation and FEM.•Gaussian regression-based Bayesian optimization derived the optimal grating shape.•Our optimization tailored grating shape depending on the target load resistance.