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Research Article | Open Access

Software for Evaluating Ionic Conductivity of Inorganic–Polymer Composite Solid Electrolytes

Yuqing Ding1, Bing He2, Da Wang1, Maxim Avdeev3,4, Yajie Li1, Siqi Shi1,5( )
School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Australian Nuclear Science and Technology Organisation, Sydney 2232, Australia
School of Chemistry, The University of Sydney, Sydney 2006, Australia
Materials Genome Institute, Shanghai University, Shanghai 200444, China
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Abstract

Inorganic–polymer composite solid electrolytes (IPCSEs) obtained by filling the polymer matrix with inorganic materials usually have higher ionic conductivity compared with individual phases. This important increase in ionic conductivity is explained in terms of the new percolation paths formed by the highly conductive interface between inorganic filler and polymer. The conduction in such systems can be investigated using the effective medium theory (EMT) and random resistance model (RRM). EMT can be used to analyze the effect of filler size on the ionic conductivity of disordered IPCSEs, while RRM can describe the composites with inorganic fillers of various shapes (nano-particles, nano-wires, nano-sheets, and nano-networks) in ordered or disordered arrangement. Herein, we present software evaluating the ionic conductivity in IPCSEs by combining EMT and RRM. The approach is illustrated by considering the size, shapes, and arrangements of inorganic fillers. The ionic conductivities of different types of IPCSEs are predicted theoretically and found in good agreement with the experimental values. The software can be used as an auxiliary tool to design composite electrolytes.

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Energy Material Advances
Volume 4,
2023
Article number: 0041
DOI: 10.34133/energymatadv.0041
Cite this article:
Ding Y, He B, Wang D, et al. Software for Evaluating Ionic Conductivity of Inorganic–Polymer Composite Solid Electrolytes. Energy Material Advances, 2023, 4: 0041. https://doi.org/10.34133/energymatadv.0041

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Received: 16 May 2023
Accepted: 14 June 2023
Published: 10 July 2023
© 2023 Yuqing Ding et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
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