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Nonaqueous redox flow batteries (NARFBs) offer a promising solution for large-scale storage of renewable energy. However, crossover of redox active molecules between the two sides of the cell is a major factor limiting their development, as most selective separators are designed for deployment in water, rather than organic solvents. This report describes a systematic investigation of the crossover rates of redox active organic molecules through an anion exchange separator under RFB-relevant non-aqueous conditions (in acetonitrile/KPF6) using a combination of experimental and computational methods. A structurally diverse set of neutral and cationic molecules was selected, and their rates of crossover were determined experimentally with the organic solvent-compatible anion exchange separator Fumasep FAP-375-PP. The resulting data were then fit to various descriptors of molecular size, charge, and hydrophobicity (overall charge, solution diffusion coefficient, globularity, dynamic volume, dynamic surface area, clogP). This analysis resulted in multiple statistical models of crossover rates for this separator. These models were then used to predict tether groups that dramatically slow the crossover of small organic molecules in this system.
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Large variability and complexity of isothermal solubility for a series of redox-active phenothiazines
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.
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- Award ID(s):
- 2019574
- PAR ID:
- 10416211
- Date Published:
- Journal Name:
- Materials Advances
- Volume:
- 3
- Issue:
- 23
- ISSN:
- 2633-5409
- Page Range / eLocation ID:
- 8705 to 8715
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D2MA00598K
