Modeling tensorial conductivity of particle suspension networks

TL;DR

This paper introduces an analytical model for the tensorial conductivity of attractive particle suspensions, capturing microstructural anisotropy and network properties to predict electrical behavior relevant to flow-battery technology.

Contribution

The paper presents a novel analytical model linking microstructural fabric to tensorial conductivity in particle suspensions, validated against diverse computer-generated networks.

Findings

Model accurately predicts tensorial conductivity invariants.

Captures mean directionality of conductivity.

Validated against multiple suspension network protocols.

Abstract

Significant microstructural anisotropy is known to develop during shearing flow of attractive particle suspensions. These suspensions, and their capacity to form conductive networks, play a key role in flow-battery technology, among other applications. Herein, we present and test an analytical model for the tensorial conductivity of attractive particle suspensions. The model utilizes the mean fabric of the network to characterize the structure, and the relationship to the conductivity is inspired by a lattice argument. We test the accuracy of our model against a large number of computer-generated suspension networks, based on multiple in-house generation protocols, giving rise to particle networks that emulate the physical system. The model is shown to adequately capture the tensorial conductivity, both in terms of its invariants and its mean directionality.