Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics-Reference-Cited by-同舟云学术

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Phase-field modeling and machine learning of electric-thermal-mechanical breakdown of polymer-based dielectrics

Published:2019-04-23 Issue:1 Volume:10 Page:
ISSN:2041-1723
Container-title:Nature Communications
language:en
Short-container-title:Nat Commun

Author:

Shen Zhong-Hui,Wang Jian-Jun,Jiang Jian-Yong,Huang Sharon X.,Lin Yuan-Hua,Nan Ce-Wen,Chen Long-Qing,Shen Yang

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry

Link

http://www.nature.com/articles/s41467-019-09874-8.pdf

Reference46 articles.

1. Chu, B. et al. A dielectric polymer with high electric energy density and fast discharge speed. Science 313, 334–336 (2006).

2. Dang, Z. M., Yuan, J. K., Yao, S. H. & Liao, R. J. Flexible nanodielectric materials with high permittivity for power energy storage. Adv. Mater. 25, 6334–6365 (2013).

3. Guo, N. et al. Nanoparticle, size, shape, and interfacial effects on leakage current density, permittivity, and breakdown strength of metal oxide-polyolefin nanocomposites: experiment and theory. Chem. Mater. 22, 1567–1578 (2010).

4. Li, Q. et al. High-temperature dielectric materials for electrical energy storage. Annu. Rev. Mater. Res. 48, 219–243 (2018).

5. Shen, Y., Lin, Y. & Zhang, Q. Polymer nanocomposites with high energy storage densities. MRS Bull. 40, 753–759 (2015).

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