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.
Original language | English |
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Article number | 144995 |
Journal | Chemical Engineering Journal |
Volume | 472 |
DOIs | |
Publication status | Published - 15 Sept 2023 |
Keywords
- Aluminum batteries
- Ionic liquid electrolytes
- Physics-based model
- Sensitivity analysis
ASJC Scopus subject areas
- General Chemistry
- Environmental Chemistry
- General Chemical Engineering
- Industrial and Manufacturing Engineering
- Electrochemistry
Fields of Expertise
- Advanced Materials Science
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)
- Experimental
- Theoretical