Substituent Impact on Quinoxaline Performance and Degradation in Redox Flow Batteries.

Aqueous redox flow batteries (RFBs) are attractive candidates for low-cost, grid-scale storage of energy from renewable sources. Quinoxaline derivatives represent a promising but underexplored class of charge-storing materials on account of poor chemical stability in prior studies (with capacity fade rates >20%/day). Here, we establish that 2,3-dimethylquinoxaline-6-carboxylic acid (DMeQUIC) is vulnerable to tautomerization in its reduced form under alkaline conditions. We obtain kinetic rate constants for tautomerization by applying Bayesian inference to ultraviolet-visible spectroscopic data from operating flow cells and show that these rate constants quantitatively account for capacity fade measured in cycled cells. We use density functional theory (DFT) modeling to identify structural and chemical predictors of tautomerization resistance and demonstrate that they qualitatively explain stability trends for several commercially available and synthesized derivatives. Among these, quinoxaline-2-carboxylic acid shows a dramatic increase in stability over DMeQUIC and does not exhibit capacity fade in mixed symmetric cell cycling. The molecular design principles identified in this work set the stage for further development of quinoxalines in practical, aqueous organic RFBs.

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

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.