Triboelectric nanogenerators for energy harvesting, self-powered sensing and high voltage applications

Tribooelectric nanogenerators;Energy harvesting;Self-powered sensors;High voltage applications

Zhang, Steven L. ; Wang, Zhong L. Materials Science and Engineering Alshareef, Husam N. Lin, Zhiqun Singh, Preet Liu, Meilin ; Wang, Zhong L.

University:Georgia Institute of Technology

Department:Materials Science and Engineering

关键词: Tribooelectric nanogenerators;    Energy harvesting;    Self-powered sensors;    High voltage applications;   

Others  :  https://smartech.gatech.edu/bitstream/1853/63633/1/ZHANG-DISSERTATION-2020.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

As we are currently entering an age of Internet of Things (IOTs), where electronic devices are able to perceive, interpret, and judge for themselves, intensive research efforts are needed on fabricating energy sources to power these electronic devices and on developing different sensor networks. Currently, different methods, such as using solar, thermal, nuclear, and mechanical motions, have been used to harvest the energy required to power these sensors. For the latter, triboelectric nanogenerators (TENGs), which are developed in Professor Zhong Lin Wang’s group in 2012, is one of the best choices to harvest mechanical vibrations, due to triboelectrification is an universal and ubiquitous effect with an abundant choice of materials. Also, not only the TENG could be used as an energy harvester, it could also be utilized as a self-powered active sensor, which is able to sense characteristics of different mechanical motions, expanding its ability to operate as a sensing network. Furthermore, TENGs due to their high voltage characteristics, have been utilized in various high voltage applications recently. In this thesis, three main research area relating TENGs are focused. The first is to develop a more selective and sensitive self-powered active triboelectric sensor through the use of novel materials. Also, by utilizing, signal processing and machine learning algorithms, different applications of self-powered sensors are investigated. The second is to expand the use of TENGs to harvest energy more effectively in harsh environments. This is done by investigating new theory of dielectric loss effects on TENG in different harsh environments. The third is to use TENG as a high voltage source for novel applications, such as a self-powered sensing system and a self-powered personal security device.