The DOI matches its Crossref record; 171 of the 171 references it cites were found in a registry or an index.
Moisture‐Enabled Electricity Generation: From Physics and Materials to Self‐Powered Applications
Abstract
The exploration of the utilization of sustainable, green energy represents one way in which it is possible to ameliorate the growing threat of the global environmental issues and the crisis in energy. Moisture, which is ubiquitous on Earth, contains a vast reservoir of low‐grade energy in the form of gaseous water molecules and water droplets. It has now been found that a number of functionalized materials can generate electricity directly from their interaction with moisture. This suggests that electrical energy can be harvested from atmospheric moisture and enables the creation of a new range of self‐powered devices. Herein, the basic mechanisms of moisture‐induced electricity generation are discussed, the recent advances in materials (including carbon nanoparticles, graphene materials, metal oxide nanomaterials, biofibers, and polymers) for harvesting electrical energy from moisture are summarized, and some strategies for improving energy conversion efficiency and output power in these devices are provided. The potential applications of moisture electrical generators in self‐powered electronics, healthcare, security, information storage, artificial intelligence, and Internet‐of‐things are also discussed. Some remaining challenges are also considered, together with a number of suggestions for potential new developments of this emerging technology.
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Cited by
Wang, X., Xia, X., Wang, H., Yang, Y., Yang, S., Zhang, A., Yuan, R., Zhu, H., Wang, B., Zhang, Y., et al. (2024) · Nano Energy · doi:10.1016/j.nanoen.2023.109185 · Cited by 22
Metadata
- Authors
- Shen, D., Duley, W. W., Peng, P., Xiao, M., Feng, J., Liu, L., Zou, G., & Zhou, Y. N.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2020-11-13
- Volume
- 32
- Issue
- 52
- Pages
- e2003722-e2003722
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W3103100888
- Cited by
- 428
- References
- 171
- FWCI
- 12.16
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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