@article{Qian2026, 
author = {Guang Qian and Jinzhi Zhu and Lu Dong and Yinhu Qiao and Xuejing Sun and Jianyang Zhu and Tinghai Cheng},
title = {Deep-learning-assisted design of a gray shark-inspired bionic triboelectric nanogenerator for water-flow energy harvesting},
year = {2026},
journal = {Nano Research},
keywords = {deep learning, triboelectric nanogenerator, bionic foil, hydrokinetic energy harvesting},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94908973},
doi = {10.26599/NR.2026.94908973},
abstract = {Distributed hydrokinetic energy harvesting is essential for enabling sustainable self-powered sensing in aquatic environments. However, natural water flows are typically low-frequency, weak, and highly stochastic, which limits the efficiency of conventional energy harvesters. Here, a gray shark–inspired parallel flapping triboelectric nanogenerator (GS-TENG) is proposed for efficient hydrokinetic energy harvesting and environmental monitoring. A biomimetic flapping hydrofoil inspired by the streamlined morphology of the shark is developed and coupled with an energy storage–release mechanism to enhance energy capture under low-flow conditions. In addition, a deep-learning-assisted framework is employed to optimize key structural parameters of the flapping foil. The optimized biomimetic hydrofoil improves average power by 92.4% compared with the conventional NACA0015 airfoil. The energy storage–release mechanism converts irregular low-frequency flow excitation into stable rotational motion, resulting in a 71.7% enhancement in electrical output. The GS-TENG achieves a peak power of 46.65 mW and a power density of 29.32 W m-3. Laboratory and open-channel experiments demonstrate that the GS-TENG can continuously power water temperature and water level sensors, highlighting its potential for self-powered water-environment monitoring.}
}