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Multifunctional Energy Storage and Conversion Devices
Abstract
Multifunctional energy storage and conversion devices that incorporate novel features and functions in intelligent and interactive modes, represent a radical advance in consumer products, such as wearable electronics, healthcare devices, artificial intelligence, electric vehicles, smart household, and space satellites, etc. Here, smart energy devices are defined to be energy devices that are responsive to changes in configurational integrity, voltage, mechanical deformation, light, and temperature, called self‐healability, electrochromism, shape memory, photodetection, and thermal responsivity. Advisable materials, device designs, and performances are crucial for the development of energy electronics endowed with these smart functions. Integrating these smart functions in energy storage and conversion devices gives rise to great challenges from the viewpoint of both understanding the fundamental mechanisms and practical implementation. Current state‐of‐art examples of these smart multifunctional energy devices, pertinent to materials, fabrication strategies, and performances, are highlighted. In addition, current challenges and potential solutions from materials synthesis to device performances are discussed. Finally, some important directions in this fast developing field are considered to further expand their application.
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Metadata
- Authors
- Huang, Y., Zhu, M., Huang, Y., Pei, Z., Li, H., Wang, Z., Xue, Q., & Zhi, C.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2016-07-19
- Volume
- 28
- Issue
- 38
- Pages
- 8344-8364
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2497239744
- Cited by
- 544
- References
- 161
- FWCI
- 27.81
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.