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Recent progress of triboelectric nanogenerators: From fundamental theory to practical applications
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For the development of the internet of things (IoTs), big data, and artificial intelligence, widely distributed sensing network is the most essential element, which has to be driven by the energy storage unit, with a limited lifetime and environmental concerns. Given that the wide distribution and high mobility of these numerous sensors, the success of the IoTs and sustainable development of human society call for renewable distributed energy sources. Since triboelectrification effect is ubiquitous and universal in our living environment, the triboelectric nanogenerator (TENG) for mechanical energy harvesting and self‐powered sensing developed by Wang and co‐workers is one of the best choices for this energy for the new era. In this review, the recent progress of TENGs from fundamental theory to practical applications is systematically summarized. First, the mechanism of contact electrification, first principle theory, working principle, working modes, and figure of merits of the TENG are introduced. Furthermore, recent important progress in four major TENG applications, including micro/nano power sources, active self‐powered sensors, large‐scale blue energy, and direct high‐voltage power sources are reviewed. In the end, some perspectives and challenges for the future development of TENG are also discussed. image
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Self-citation
Metadata
- Authors
- Luo, J., & Wang, Z. L.
- Venue
- EcoMat
- Publisher
- Wiley
- Published
- 2020-10-22
- Volume
- 2
- Issue
- 4
- ISSN
- 2567-3173 (ISSN-L)
- OpenAlex
- W3094302847
- Cited by
- 451
- References
- 159
- FWCI
- 14.67
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
