Käärik, M.; Maran, U.; Arulepp, M.; Perkson, A.; Leis, J. Quantitative nano-structure-property relationships for the nanoporous carbon: Predicting the performance of energy storage materials. ACS Appl. Energy Mater. 2018, 1, 4016-4024

Abstract

Nanoporous carbon-based energy storage is a fast-growing research field thanks to high energy densities of carbon electrodes with nanoporous amorphous texture. To support the developments on electrical double-layer based ultra-capacitors it is necessary to improve understanding about relationships between the porous structure and energy storage behavior of carbon materials. This can be facilitated by the analysis of complex data sets and the development of corresponding descriptive and predictive models. Related to that this paper presents a in silico regression model to predict the suitability of various carbon materials for energy storage, thus being probably the first time a quantitative nano-structure-property relationship (QnSPR) approach is applied to the nanoporous carbon materials. With this study, which is based on the experimental data of 100 carbide-derived carbon materials, it has been shown that the electrical double-layer capacitance of carbon electrode in a nonaqueous electrolyte can be predicted using experimentally determined specific surface area and a volume of certain pore size fraction of carbon and a bulk density of carbon electrode. The three-parameter QnSPR model for volumetric cathodic capacitance of carbon in triethylmethylammonium tetrafluoroborate / propylene carbonate electrolyte, Cv,neg = f(SBET, Vd<1.14, Del), comprising the above-mentioned parameters and characterized by R2=0.94 and s2=8.7, confirms the important role of carbon pore size for the double layer capacitance. It was shown that carbon pores with a size below 1.1 nm have the most significance for achieving high energy densities in the nonaqueous electrochemical systems studied. Putting the results of this research into wider perspective, it has been shown that the QnSPR approach provides a useful tool for describing and predicting the variable performance-related physical properties of nanoporous carbon and nanomaterial properties in general. The models are available in the QsarDB repository.