Brownian motor inspired monodirectional continuous spinning triboelectric nanogenerators for extracting energy from irregular gentle water waves. Issue 2 (13th December 2022)

• Record Type: Journal Article
• Authors: Qiu, Huijing; Wang, Huamei; Xu, Liang; Zheng, Mingli; Wang, Zhong Lin
• Is Part Of: Energy & environmental science. Volume 16:Issue 2(2023)
• Page Start: 473
• Page End: 483
• Publication Date: 2022-12-13
• Subjects: Energy conversion -- Periodicals; Fuel switching -- Periodicals; Environmental sciences -- Periodicals; Environmental chemistry -- Periodicals; 333.79
• DOI: 10.1039/d2ee03395j
• Languages: English
• ISSNs: 1754-5692

Abstract

Abstract : A TENG intrinsically adaptive to chaotic agitations like the Brownian motors is designed based on dual symmetry breakings. Abstract : Artificial or biological Brownian motors can achieve directional motion by extracting energy from a chaotic but non-thermodynamic-equilibrium environment based on asymmetry in the potential landscape. It may inspire macroscopic mechanical energy harvesting from the environment, especially water waves, which present similar characteristics with irregular and usually small-amplitude chaotic force, imposing great challenges for conventional bulky generators that need a deterministic driving force. Here, we demonstrate a macroscopic analogue of Brownian motors for effective water wave energy harvesting based on the triboelectric nanogenerator and its networks. The design adopts a novel strategy of dual symmetry breakings to convert irregular wave excitation to internal unidirectional rotation. Meanwhile, inertia wheels are utilized to cache energy from each transient excitation for a prolonged motion. Consequently, a monodirectional continuous spinning triboelectric nanogenerator is realized for effectively extracting energy from irregular water waves, performing much superiorly to previous designs. Due to the generality of the strategies, this work points out an important paradigm for harvesting energy from random excitations on different scales in the environment by learning from microscopic molecular systems which are intrinsically adaptive to chaotic force and can have high efficiency.