Please use this identifier to cite or link to this item: https://apo.ansto.gov.au/dspace/handle/10238/11083
Title: Identifying chemical factors affecting reaction kinetics in Li-air battery via ab initio calculations and machine learning
Authors: Wang, AP
Zou, ZY
Wang, D
Liu, Y
Li, YJ
Wu, JM
Avdeev, M
Shi, S
Keywords: Redox flow batteries
Solvent properties
Thermodynamic activity
Electric discharges
Reaction kinetics
Machine learning
Issue Date: 1-Mar-2021
Publisher: Elsevier
Citation: Wang, A., Zou, Z., Wang, D., Liu, Y., Li, Y., Wu, J., Avdeev, M., & Shi, S. (2021). Identifying chemical factors affecting reaction kinetics in Li-air battery via ab initio calculations and machine learning. Energy Storage Materials, 35, 595-601. doi:10.1016/j.ensm.2020.10.022
Abstract: Redox mediators are promised to thermodynamically resolve the cathode irreversibility of Li-air battery. However, the sluggish chemical reaction between mediators and discharge products severely restrains fast charging. Here, we combine ab initio calculations and machine learning method to investigate the reaction kinetics between LiOH and I2, and demonstrate the critical role of the disorder degree of LiOH and the solvent effect. The Li+ desorption is identified as the rate determining step (rds) of the reaction. While LiOH turns from the crystalline to disordered/amorphous structure, the rds energy barrier will be reduced by ∼500 meV. The functional group of the solvent is detected as the key to regulating the solvation effect and phosphate-based solvent is predicted to accelerate the decomposition kinetics most with the strongest solvation capability. These findings indicate that the faster reaction kinetics between mediators and the discharge products can be achieved by rational discharge product structure regulation and appropriate solvent selection. © 2020 Elsevier B.V.
URI: https://doi.org/10.1016/j.ensm.2020.10.022
https://apo.ansto.gov.au/dspace/handle/10238/11083
ISSN: 2405-8297
Appears in Collections:Journal Articles

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