Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques

Sanat Vibhas Modak, Wanggang Shen, Siddhant Singh, Dylan Herrera, Fairooz Oudeif, Bryan R. Goldsmith, Xun Huan and David G. Kwabi ()
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Sanat Vibhas Modak: University of Michigan
Wanggang Shen: University of Michigan
Siddhant Singh: University of Michigan
Dylan Herrera: University of Michigan
Fairooz Oudeif: University of Michigan
Bryan R. Goldsmith: University of Michigan
Xun Huan: University of Michigan
David G. Kwabi: University of Michigan

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable properties. Unfortunately, many lab-scale flow cells experience rapid material degradation (from chemical and electrochemical decay mechanisms) and capacity fade during cycling (>0.1%/day) hindering their commercial deployment. In this work, we combine ultraviolet-visible spectrophotometry and statistical inference techniques to elucidate the Michael attack decay mechanism for 4,5-dihydroxy-1,3-benzenedisulfonic acid (BQDS), a once-promising positive electrolyte reactant for aqueous organic redox-flow batteries. We use Bayesian inference and multivariate curve resolution on the spectroscopic data to derive uncertainty-quantified reaction orders and rates for Michael attack, estimate the spectra of intermediate species and establish a quantitative connection between molecular decay and capacity fade. Our work illustrates the promise of using statistical inference to elucidate chemical and electrochemical mechanisms of capacity fade in organic redox-flow battery together with uncertainty quantification, in flow cell-based electrochemical systems.

Date: 2023
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DOI: 10.1038/s41467-023-39257-z

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