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Redoxmers are organic molecules that serve as charge carriers in redox flow batteries. While these materials are affordable and easy to source, insufficient stability of their charged states (radical ions) remains a challenge. A common reaction of these species is their disproportionation. This reversible reaction yields unstable multiply charged states, shifting the overall charge transfer equilibrium toward the decomposition products. Here we show how kinetic controls can be engineered into a redoxmer molecule to suppress these unwanted charge transfer reactions. This approach is used to transform Wurster’s blue, which is historically the first example of a stable radical ion in organic chemistry, into an exceptionally durable redoxmer molecule that persists over thousands of electrochemical cycles. 

Due to their almost unlimited scalability, redox flow batteries can make versatile and affordable energy storage systems. Redox active materials (redoxmers) in these batteries largely define their electrochemical performance, including the life span of the battery that depends on the stability of charged redoxmers. In this study, we examine the effects of expanding the π-system in the arene rings on the chemical stability of dialkoxyarene redoxmers that are used to store positive charge in RFBs. When 1,4-dimethoxybenzene is π-extended to 1,4-dimethoxynaphthalene, a lower redox potential, improved kinetic stability, and longer cycling life are observed. However, when an additional ring is fused to make 9,10-dimethoxyanthracene, the radical cation undergoes rapid O-dealkylation possibly due to increased steric strain that drives methoxy out of the arene plane thus breaking the π-conjugation with O 2p orbitals. On the other hand, the planar structure of 1,4-dimethoxynaphthalene may facilitate second-order reactions of radical cations leading to their neutralization in the bulk. Our study suggests that extending the π-system changes reactivity in multiple (sometimes, opposite) ways, so lowering the oxidation potential through π-conjugation to improve redoxmer stability should be pursued with caution. 

Redox-active molecules, or redoxmers, in nonaqueous redox flow batteries often suffer from membrane crossover and low electrochemical stability. Transforming inorganic polyionic redoxmers established for aqueous batteries into nonaqueous candidates is an attractive strategy to address these challenges. Here we demonstrate such tailoring for hexacyanoferrate (HCF) by pairing the anions with tetra-n-butylammonium cation (TBA+). TBA3HCF has good solubility in acetonitrile and >1 V lower redox potential vs the aqueous counterpart; thus, the familiar aqueous catholyte becomes a new nonaqueous anolyte. The lowering of redox potential correlates with replacement of water by acetonitrile in the solvation shell of HCF, which can be traced to H-bond formation between water and cyanide ligands. Symmetric flow cells indicate exceptional stability of HCF polyanions in nonaqueous electrolytes and Nafion membranes completely block HCF crossover in full cells. Ion pairing of metal complexes with organic counterions can be effective for developing promising redoxmers for nonaqueous flow batteries. 

The development of redox-active organic molecules (ROM) with large solubilities in all states of charge in organic electrolytes is imperative to the continued development of non-aqueous redox flow batteries. The capability to a priori predict ROM solubility would be a game changer, allowing for a move away from time and resource consuming trial-and-error approaches to materials design and deployment. However, it is not presently clear that such predictions are generally possible, even for chemically related ROM, given the large number of physicochemical factors in play. Here we use quantitative structure–property relationships (QSPR) to examine solubility trends for a set of thirty phenothiazine derivatives. The solubility in all states of charge (neutral and charged forms) of these molecules were obtained experimentally, and multiple linear regression models were used to correlate these properties with a large set (>100) of molecular descriptors. Minimal QSPR models rationalizing these data include four-to-six molecular descriptors, and cannot be further reduced. However, even such relatively complex models show limited ability to predict solubility of an unknown homologous compound. Thus, even in the controlled experimental environment, “predicting” the solubility may not be easy, suggesting the need for high-throughput measurements to develop the large data sets required for machine-informed materials design. The NMR method presented in this study is promising in this regard as it lends itself to automation.