Pore-Scale Prediction of the oxygen effective diffusivity in porous battery electrodes using the random walk theory

Abstract

This study developed a predictive model to calculate the effective diffusivity of oxygen in porous electrodes. The model integrates the random walk method and the tomographic technique. We reconstructed pore-scale structures of battery electrodes, and quantitatively studied the effective diffusivity of oxygen in non-wetted and wetted digital geometry. Both the reconstructed geometry and the random walk model were well validated. This model can simulate effective diffusivity with reasonable accuracy and low computational time. Results show that the effective diffusivity of oxygen is very sensitive to the porosity and the electrolyte saturation. A higher porosity improves the oxygen diffusion in a porous medium. On the contrary, the oxygen diffusivity drops significantly when the porous medium is wetted by the electrolyte. At higher saturation (> 0.79), the effective diffusivity is similar to the diffusivity in the liquid. The findings from this study have a strong potential to enhance oxygen mass transfer in porous electrodes and will accelerate the deployment of metal-air batteries. In addition, the advantages of low computational cost, real-time capture of material structures, and wide applicability make this model a powerful and convenient tool to predict transport properties of porous media in a wide range of science and engineering fields.