Curvature effects on electric-double-layer capacitance

doi:10.1016/j.cjche.2020.10.039

Abstract

Abstract: Understanding the microscopic structure and thermodynamic properties of electrode/electrolyte interfaces is central to the rational design of electric-double-layer capacitors (EDLCs). Whereas practical applications often entail electrodes with complicated pore structures, theoretical studies are mostly restricted to EDLCs of simple geometry such as planar or slit pores ignoring the curvature effects of the electrode surface. Significant gaps exist regarding the EDLC performance and the interfacial structure. Herein the classical density functional theory (CDFT) is used to study the capacitance and interfacial behavior of spherical electric double layers within a coarse-grained model. The capacitive performance is associated with electrode curvature, surface potential, and electrolyte concentration and can be correlated with a regression-tree (RT) model. The combination of CDFT with machine-learning methods provides a promising quantitative framework useful for the computational screening of porous electrodes and novel electrolytes.

Key words: Electric double layer, Electrodes/electrolyte interface, Curvature effects, Classical density functional theory, Machine learning

摘要： Understanding the microscopic structure and thermodynamic properties of electrode/electrolyte interfaces is central to the rational design of electric-double-layer capacitors (EDLCs). Whereas practical applications often entail electrodes with complicated pore structures, theoretical studies are mostly restricted to EDLCs of simple geometry such as planar or slit pores ignoring the curvature effects of the electrode surface. Significant gaps exist regarding the EDLC performance and the interfacial structure. Herein the classical density functional theory (CDFT) is used to study the capacitance and interfacial behavior of spherical electric double layers within a coarse-grained model. The capacitive performance is associated with electrode curvature, surface potential, and electrolyte concentration and can be correlated with a regression-tree (RT) model. The combination of CDFT with machine-learning methods provides a promising quantitative framework useful for the computational screening of porous electrodes and novel electrolytes.

关键词: Electric double layer, Electrodes/electrolyte interface, Curvature effects, Classical density functional theory, Machine learning

Jie Yang, Alejandro Gallegos, Cheng Lian, Shengwei Deng, Honglai Liu, Jianzhong Wu. Curvature effects on electric-double-layer capacitance[J]. Chinese Journal of Chemical Engineering, 2021, 29(3): 145-152.

Jie Yang, Alejandro Gallegos, Cheng Lian, Shengwei Deng, Honglai Liu, Jianzhong Wu. Curvature effects on electric-double-layer capacitance[J]. 中国化学工程学报, 2021, 29(3): 145-152.

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