• Lysgaard, Steen
• Appiah, Williams Agyei
• Gollas, Bernhard
• Stark, Anna
• García Lastra, Juan Maria
• Chang, Jin Hyun
• Jankowski, Piotr
• Bhowmik, Arghya
• Busk, Jonas

Abstract

Aluminum batteries with imidazolium-based electrolytes present a promising avenue toward the post-lithium-ion battery era. A critical bottleneck is the development of reversible aluminum metal anodes, which is hindered by sluggish battery charge–discharge characteristics due to the reversible/irreversible side reactions on the anodic and cathodic sides. The indispensable discernment of the stripping-plating mechanisms at the electrode–electrolyte interface is not well explored due to the complexity of the various reactions occurring at the surface of the aluminum anode. Herein, a high-fidelity physics-based model is coupled with density functional theory to explain the stripping-plating mechanisms that occur on the surface of the aluminum anode at different current densities. Sensitivity analysis is performed on the experimentally validated physics-based model using a machine-learning Gaussian process regression model to identify the most significant parameters for the plating-stripping mechanism of aluminum. The electrodeposition of aluminum is controlled by both diffusion and kinetics and is limited by the kinetics of the electrochemical reactions at a high current density. This work highlights the assurance of combining models at different scales, machine learning algorithms, and experiments to analyze the behavior of complex electrochemical systems.

Topics

• density
• impedance spectroscopy
• surface
• theory
• experiment
• aluminium
• density functional theory
• Lithium
• current density
• electrodeposition
• machine learning