Roadmap on nanogenerators and piezotronics

Philippe Basset, Stephen Paul Beeby, Chris Bowen, Zheng Jun Chew, Ahmad Delbani, R. D.Ishara G. Dharmasena, Bhaskar Dudem, Feng Ru Fan, Dimitri Galayko, Hengyu Guo, Jianhua Hao, Yuchen Hou, Chenguo Hu, Qingshen Jing, Young Hoon Jung, Sumanta Kumar Karan, Sohini Kar-Narayan, Miso Kim, Sang Woo Kim, Yang KuangKeon Jae Lee, Jialu Li, Zhaoling Li, Yin Long, Shashank Priya, Xianjie Pu, Tingwen Ruan, S. Ravi P. Silva, Hee Seung Wang, Kai Wang, Xudong Wang, Zhong Lin Wang, Wenzhuo Wu, Wei Xu, Hemin Zhang, Yan Zhang, Meiling Zhu

Research output: Contribution to journalArticlepeer-review

22 Citations (SciVal)

Abstract

Triboelectric nanogenerators (TENGs) are one of the most promising energy harvesting methods available for next-generation wearables, autonomous devices and sensors, and the Internet-of-things (IoT), which can efficiently convert ambient mechanical energy into useful electricity. It can be implemented in clothing, shoes, walkways, and moving parts in automobiles, harvest suitable energy to drive many types of portable/wearable/implantable electronics that at present, and are predominantly powered by batteries. In order to move the current state-of-the-art in practical devices to realistic technologies, much development is still needed. These requirements we envisage will be accelerated with the help of theoretical models and simulations, which can be verified and refined using an empirical route to best fit experimental data. This will give rise to self-validated models that allow for predictive design of TENG devices for specific applications using computer-aided design (CAD) and simulators.
Original languageEnglish
Article number109201
JournalAPL Materials
Volume10
Issue number10
Early online date31 Oct 2022
DOIs
Publication statusPublished - 31 Oct 2022

Bibliographical note

Funding Information:
This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 52073051, 51873030, and 51703022), the National Key R&D Program of China (Grant No. 2018YFC2000900), the Natural Science Foundation of Shanghai (Grant No. 18ZR1402100), and Shanghai Committee of Science and Technology (Grant No. 19QA1400100).

Funding Information:
The authors would like to acknowledge the support from the EPSRC Research Project (Grant No. EP/S02106X/1) for funding this work. This work was also supported by the Royal Academy of Engineering under the Research Fellowship Scheme.

Funding Information:
W.W. acknowledges the College of Engineering and School of Industrial Engineering at Purdue University for the startup support and the Ravi and Eleanor Talwar Rising Star Assistant Professorship. F.R.F. acknowledges Nanqiang Young Top-notch Talent Fellowship from Xiamen University.

Funding Information:
This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 52073051, 51873030, and 51703022), the National Key R&D Program of China (Grant No. 2018YFC2000900), the Natural Science Foundation of Shanghai (Grant No. 18ZR1402100), and Shanghai Committee of Science and Technology (Grant No. 19QA1400100). This work was supported by the Academy of Medical Sciences GCRF Fund (Grant No. GCRFNGR2-10059), ERC project (ERC-2017-PoC-ERC-Proof of Concept, Grant No. 789863), and The Leverhulme Trust (Grant No. RGP-2018-290). The authors acknowledge the Key Research and Development Project of Hunan Province (Grant No. 2020WK2004), Overseas Talent Introduction Project of China, and Hundred Youth Talents Program of Hunan.

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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