Insights from machine learning of carbon electrodes for electric double layer capacitors

Publication date: Available online 10 October 2019Source: CarbonAuthor(s): Musen Zhou, Alejandro Gallegos, Kun Liu, Sheng Dai, Jianzhong WuAbstractRecent 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.Graphical abstract
Source: Carbon - Category: Materials Science Source Type: research

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