Bayesian optimization of dynamic Ce(III) electrooxidation

• Published in: AIChE journal Vol. 68; no. 12
• Main Authors: Frey, Daniel, Shin, Ju Hee, Akin, Adeola, Zhang, Xinshu, Modestino, Miguel A.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2022; American Institute of Chemical Engineers
• Subjects: artificial intelligence; Batteries; Bayesian analysis; Cerium; diffusion (mass transfer, heat transfer); electrochemistry; Electrodes; Engineering; mathematical modeling; Optimization; Oxidation; Rechargeable batteries; Silver chloride
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.17930

Cerium(III/IV) is a redox couple with promising applications in redox flow batteries and redox‐mediated water electrolyzers, but due to its high oxidation potential, parasitic oxygen evolution can occur and decrease the Faradaic efficiency (FE) of the oxidation reaction. Dynamic potential dosing can help balance the transport limitations and the electrooxidation rates, allowing for control over the cerium(III) concentration near the electrode, enhancing FE. Due to the large number of possible pulsing operating conditions, Bayesian optimization (BO) was used to rapidly identify the optimal potential pulses. With an active pulse voltage of 2.5 V vs. Ag/AgCl, the maximum FE achieved was 0.91 (active pulse time = 5 ms, resting pulse time = 136 ms), underscoring the need for long inactive times for cerium(III) diffusion to the electrode. Furthermore, a multiobjective BO was performed to identify optimal trade‐offs between FE and the partial current density to achieve improvements over constant current operation.