Effective conductivity of particulate polymer composite electrolytes using random resistor network method

Sergiy Kalnaus; Adrian S. Sabau; Sarah Newman; Wyatt E. Tenhaeff; Claus Daniel; Nancy J. Dudney

doi:10.1016/j.ssi.2011.07.016

Effective conductivity of particulate polymer composite electrolytes using random resistor network method

Sergiy Kalnaus, Adrian S. Sabau, Sarah Newman, Wyatt E. Tenhaeff, Claus Daniel, Nancy J. Dudney

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

Abstract

The effective DC conductivity of particulate composite electrolytes was obtained by solving electrostatics equations using random resistors network method in three dimensions. The composite structure was considered to consist of three phases: matrix, particulate filler, and conductive shell that surrounded each particle; each phase possessing a different conductivity. Different particle size distributions were generated using Monte Carlo simulations. Unlike effective medium formulations, it was shown that the random resistors network method was able to predict percolation thresholds for the effective composite conductivity. It was found that the mean particle radius has a higher influence on the effective composite conductivity compared to the effect of type of the particle size distributions that were considered. The effect of the shell thickness on the composite conductivity has been investigated. It was found that the conductivity enhancement due to the presence of the conductive shell phase becomes less evident as the shell thickness increases.

Original language	English
Pages (from-to)	44-53
Number of pages	10
Journal	Solid State Ionics
Volume	199-200
Issue number	1
DOIs	https://doi.org/10.1016/j.ssi.2011.07.016
State	Published - Sep 28 2011

Funding

This research was sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory (ORNL) , managed by UT-Battelle, LLC for the U. S. Department of Energy under Contract No. DE-AC05-00OR22725 . Sarah Newman would like to acknowledge the support from Volkswagen Distinguished Scholar Program, a research internship administered for Volkswagen Group of America by Oak Ridge Associated Universities (ORAU) on behalf of ORNL.

Funders	Funder number
U. S. Department of Energy
UT-Battelle
Volkswagen Distinguished Scholar Program
Oak Ridge Associated Universities
Oak Ridge National Laboratory

Keywords

Effective conductivity
Electrolyte
Li ion battery
Modeling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ssi.2011.07.016

Cite this

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abstract = "The effective DC conductivity of particulate composite electrolytes was obtained by solving electrostatics equations using random resistors network method in three dimensions. The composite structure was considered to consist of three phases: matrix, particulate filler, and conductive shell that surrounded each particle; each phase possessing a different conductivity. Different particle size distributions were generated using Monte Carlo simulations. Unlike effective medium formulations, it was shown that the random resistors network method was able to predict percolation thresholds for the effective composite conductivity. It was found that the mean particle radius has a higher influence on the effective composite conductivity compared to the effect of type of the particle size distributions that were considered. The effect of the shell thickness on the composite conductivity has been investigated. It was found that the conductivity enhancement due to the presence of the conductive shell phase becomes less evident as the shell thickness increases.",

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AU - Kalnaus, Sergiy

AU - Sabau, Adrian S.

AU - Newman, Sarah

AU - Tenhaeff, Wyatt E.

AU - Daniel, Claus

AU - Dudney, Nancy J.

PY - 2011/9/28

Y1 - 2011/9/28

N2 - The effective DC conductivity of particulate composite electrolytes was obtained by solving electrostatics equations using random resistors network method in three dimensions. The composite structure was considered to consist of three phases: matrix, particulate filler, and conductive shell that surrounded each particle; each phase possessing a different conductivity. Different particle size distributions were generated using Monte Carlo simulations. Unlike effective medium formulations, it was shown that the random resistors network method was able to predict percolation thresholds for the effective composite conductivity. It was found that the mean particle radius has a higher influence on the effective composite conductivity compared to the effect of type of the particle size distributions that were considered. The effect of the shell thickness on the composite conductivity has been investigated. It was found that the conductivity enhancement due to the presence of the conductive shell phase becomes less evident as the shell thickness increases.

AB - The effective DC conductivity of particulate composite electrolytes was obtained by solving electrostatics equations using random resistors network method in three dimensions. The composite structure was considered to consist of three phases: matrix, particulate filler, and conductive shell that surrounded each particle; each phase possessing a different conductivity. Different particle size distributions were generated using Monte Carlo simulations. Unlike effective medium formulations, it was shown that the random resistors network method was able to predict percolation thresholds for the effective composite conductivity. It was found that the mean particle radius has a higher influence on the effective composite conductivity compared to the effect of type of the particle size distributions that were considered. The effect of the shell thickness on the composite conductivity has been investigated. It was found that the conductivity enhancement due to the presence of the conductive shell phase becomes less evident as the shell thickness increases.

KW - Effective conductivity

KW - Electrolyte

KW - Li ion battery

KW - Modeling

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Effective conductivity of particulate polymer composite electrolytes using random resistor network method

Abstract

Funding

Keywords

UN SDGs

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