The Aqueous Aluminium-Ion Battery: Optimising the Electrode Compression Ratio through Image-Based Modelling
Authors: Le Houx James , Melzack Nicole , James Andrew , Dehyle Hans , Aslani Navid , Pimblott Matthew , Ahmed Sharif , Wills Richard G A 
Published: August 2024

The aqueous Al-ion battery potentially has improved safety and environmental advantages over the incumbent lithium-ion technology [1,2]. Using 3D carbon-felt matrix electrodes, the performance of these batteries can be optimised. Typically, the electrodes are compressed to improve electrical conductivity by improving contact between adjacent fibres, which also closes the electrolyte pores, reducing the ionic diffusivity [3]. In this work, we set out to understand the role of electrode compression on the interplay between these key performance parameters for the aq. Al-ion battery.

Synchrotron X-ray computed tomography was used to examine the 3D morphology of the carbon felt electrodes under 11 different compression ratios [4]. A bespoke in-situ tensile/compression rig was used to measure displacement and loading for three electrode types: raw carbon felt, positive electrodes loaded with a copper hexacyanoferrate ink and negative electrodes loaded with TiO2 anatase powder. Data was acquired using two voxel sizes (330nm, and 540nm) to compare the effects of spatial resolution and imaged volume size on subsequent image analysis. The tomograms were reconstructed using Paganin phase retrieval, to improve the contrast of the weakly attenuating carbon, and a filtered back projection algorithm. A U-net convolutional neural network was trained on uncompressed, fully compressed and partially compressed (three volumes) data, and then used to fully segment all tomograms.

A high-throughput, image-based model was then used to analyse how compression affects the porosity, tortuosity, volume-specific area, ionic diffusivity, and electrical conduction of the electrodes. A finite differences-based model was used to solve the equilibrium partial differential equations directly on the voxel datasets, with no additional regularisation [5]. The heterogeneity of the electrode samples is quantified by comparing the effect of representative elementary volume on the value of the computed parameters [6]. Finally, aq. Al-ion batteries are manufactured using the predicted optimal compression ratio and compared to the simulated results.

This is the first in-situ study of the compression effect on the aqueous aluminium-ion battery, and the largest XCT-based modelling study known to the authors (99 XCT tomograms). This work aims to improve the understanding of the effect of manufacturing parameters on the aqueous Al-ion battery and other similar batteries, and ultimately, its performance.

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