With the threatening of global warming and energy crises, searching for renewable and green energy resources with reduced carbon emissions is one of the most urgent challenges to the sustainable development of human civilization. In the past decades, increasing research efforts have been committed to seek for clean and renewable energy sources as well as to develop renewable energy technologies.
Mechanical motion ubiquitously exists in ambient environment and people’s daily life. In recent years, it becomes an attractive target for energy harvesting as a promising supplement to traditional fuel sources and a potentially alternative power source to battery-operated electronics. Until recently, the mechanisms of mechanical energy harvesting are limited to transductions based on piezoelectric effect, electromagnetic effect, electrostatic effect and magnetostrictive effect. Widespread usage of these techniques is likely to be shadowed by possible limitations, such as structure complexity, low power output, fabrication of high-quality materials, reliance on external power sources and little adaptability on structural design for different applications. In 2012, triboelectric nanogenerator (TENG), a creative invention for harvesting ambient mechanical energy based on the coupling between triboelectric effect and electrostatic effect has been launched as a new and renewable energy technology. The concept and design presented in this thesis research can greatly promote the development of TENG as both sustainable power sources and self-powered active sensors. And it will greatly help to define the TENG as a fundamentally new green energy technology, featured as being simple, reliable, cost-effective as well as high efficiency.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/54956 |
| Date | 27 May 2016 |
| Creators | Chen, Jun |
| Contributors | Wang, Zhong Lin |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | application/pdf |
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