Journal
CARBON
Volume 157, Issue -, Pages 147-152Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.carbon.2019.08.090
Keywords
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Funding
- Fluid Interface Reactions, Structures, and Transport (FIRST) Center, an Energy Frontier Research Center - U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
- National Science Foundation [DGE-1326120]
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Recent years have witnessed the broad use of carbon electrodes for electric double layer capacitors (EDLCs) because of large surface area, high porosity and low cost. Whereas experimental investigations are mostly focused on the device performance, computational studies have been rarely concerned with electrochemical properties at conditions remote from equilibrium, limiting their direct applications to materials design. Through a comprehensive analysis of extensive experimental data with various machine-learning methods, we report herein quantitative correlations between the structural features of carbon electrodes and the in-operando behavior of EDLCs including energy and power density. Machine learning allows us to identify important characteristics of activated carbons useful to optimize their efficiency in energy storage. (C) 2019 Elsevier Ltd. All rights reserved.
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