Tracking Zn Particle Movement in Zn–MnO2 Batteries and Incorporating Granular Flow Into a Computational Battery Model

Abstract

During discharge of alkaline Zn–MnO₂ batteries, nonuniform precipitation of low-density ZnO creates a force that physically pushes Zn particles within the anode. While Zn movement has been qualitatively described in Zn–air batteries, it has not been quantitatively characterized or incorporated into computational models. We present quantitative measurements of Zn particle movement in a discharging cylindrical AA Zn–MnO₂ battery using pseudo-operando X-ray micro-computed tomography and individual particle tracking. Using a custom alignment and machine learning-enabled segmentation workflow, we track individual Zn particles from 0% to 52% depth of discharge (DOD) under a pulsed protocol. Zn particles move inward toward the current collector at an average rate of −5.1×10⁻⁴/%DOD driven by the spatial gradient of ZnO precipitation. This discharge behavior is reproduced by a computational model in which convective Zn transport is described as granular flow via the Navier–Stokes equation. The modeled movement rate of −5.7×10⁻⁴/%DOD is within 12% of the experimental value. Because the spatial distribution of Zn determines whether material remains active in the electrode, accurate prediction of Zn movement has direct consequences for estimating usable Zn–MnO₂ battery capacity relevant to real-world use.