The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Polymer Nanocomposites with Ultrahigh Energy Density and High Discharge Efficiency by Modulating their Nanostructures in Three Dimensions
Abstract
Manipulating microstructures of composites in three dimensions has been a long standing challenge. An approach is proposed and demonstrated to fabricate artificial nanocomposites by controlling the 3D distribution and orientation of oxide nanoparticles in a polymer matrix. In addition to possessing much enhanced mechanical properties, these nanocomposites can sustain extremely high voltages up to ≈10 kV, exhibiting high dielectric breakdown strength and low leakage current. These nanocomposites show great promise in resolving the paradox between dielectric constant and breakdown strength, leading to ultrahigh electrical energy density (over 2000% higher than that of the bench‐mark polymer dielectrics) and discharge efficiency. This approach opens up a new avenue for the design and modulation of nanocomposites. It is adaptable to the roll‐to‐roll fabrication process and could be employed as a general technique for the mass production of composites with intricate nanostructures, which is otherwise not possible using conventional polymer processing techniques.
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Cites
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Self-citationTough, Bio-Inspired Hybrid Materials
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Metadata
- Authors
- Zhang, X., Jiang, J., Shen, Z., Dan, Z., Li, M., Lin, Y., Nan, C., Chen, L., & Shen, Y.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2018-03-13
- Volume
- 30
- Issue
- 16
- Pages
- e1707269-e1707269
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2793213596
- Cited by
- 322
- References
- 26
- FWCI
- 14.44
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, X., Jiang, J., Shen, Z., Dan, Z., Li, M., Lin, Y., Nan, C., Chen, L., & Shen, Y. (2018). Polymer Nanocomposites with Ultrahigh Energy Density and High Discharge Efficiency by Modulating their Nanostructures in Three Dimensions. Advanced Materials. https://doi.org/10.1002/adma.201707269
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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.