Rationally designed high-temperature polymer dielectrics for capacitive energy storage: An experimental and computational alliance

被引:0
作者
Aklujkar, Pritish S. [1 ]
Gurnani, Rishi [2 ,6 ]
Rout, Pragati [3 ]
Khomane, Ashish R. [1 ]
Mutegi, Irene [3 ]
Desai, Mohak [1 ]
Pollock, Amy [1 ]
Toribio, John M. [3 ]
Hao, Jing [4 ]
Cao, Yang [4 ,5 ]
Ramprasad, Rampi [2 ,6 ]
Sotzing, Gregory [1 ,3 ]
机构
[1] Univ Connecticut, Inst Mat Sci, Storrs, CT 06279 USA
[2] Georgia Inst Technol, Sch Mat Sci & Engn, Atlanta, GA 30332 USA
[3] Univ Connecticut, Dept Chem, Storrs, CT 06269 USA
[4] Univ Connecticut, Inst Mat Sci, Elect Insulat Res Ctr, Storrs, CT 06269 USA
[5] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT 06269 USA
[6] Matmerize Inc, Atlanta, GA 30308 USA
关键词
Capacitor; High Tg polymer; Energy storage; Polymer dielectric; Polymer genome; AI-based polymer dielectric; Co-design; CO-DESIGN; DENSITY; STRENGTH; NANOCOMPOSITES; POLYPROPYLENE; POLYIMIDES; CONDUCTION; EFFICIENCY; BREAKDOWN; CONSTANT;
D O I
10.1016/j.progpolymsci.2025.101931
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
Polymer-based electrostatic capacitors find critical use in high-temperature applications such as electrified aircraft, automobiles, space exploration, geothermal/nuclear power plants, wind pitch control, and pulsed power systems. However, existing commercial all-organic polymer dielectrics suffer from significant degradation and failure at elevated temperatures due to their limited thermal stability. Consequently, these capacitors require additional cooling systems, that require increased system load and costs. Traditionally, an inability to directly predict or model key properties- such as thermal stability, breakdown strength, and energy density has been an impediment to the design of such polymers. To enhance the experimentation and instinctive-driven approach to polymer discovery there has been recent progress in developing a modern co-design approach. This review highlights the advancements in a synergistic rational co-design approach for all-organic polymer dielectrics that combines artificial intelligence (AI), experimental synthesis, and electrical characterization. A particular focus is given to the identification of polymer structural parameters that improve the capacitive energy storage performance. Important structural elements, also known as proxies, are recognized with the rational co-design approach. The central constituents of AI and their influence on accelerating the discovery of new proxies, and polymers are presented in detail. Recent success and critical next steps in the field showcase the potential of the co-design approach. (c) 2025 Published by Elsevier Ltd.
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页数:24
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