Developing a Predictive Solubility Model for Monomeric and Oligomeric Cyclopropenium-Based Flow Battery Catholytes

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Work performed at University of Utah (JCESR partner) and University of Michigan (JCESR partner) by S.G. Robinson, Y. Yan, K. H. Hendriks, M. S. Sanford, and M. S. Sigman. Developing a Predictive Solubility Model for Monomeric and Oligomeric Cyclopropenium-Based Flow Battery Catholytes. J. Am. Chem. Soc. 2019.

Scientific Achievement

Two important field-wide goals are met through this study: the development of a statistical model for the solubility of conformationally flexible molecules in acetonitrile and the operation of a high concentration (1 M) nonaqueous organic symmetric flow cell.

Significance and Impact

Methods for forecasting nonaqueous solubility would be valuable for streamlining the identification of promising organic electrolytes.

Research Details

  • A statistical, predictive model for solubility in acetonitrile is developed through training on monomeric tris(dialkylamino)cyclopropenium (CP) catholytes.
  • The model is demonstrated to effectively translate the model features to enable prediction of CP oligomer solubility. Ultimately, this model is employed to identify a novel CP dimer that is soluble at over 1 M in both redox states, representing a 30% higher charge capacity than the parent monomer.
  • The most soluble CP monomer exhibits high stability to electrochemical cycling at 1 M in acetonitrile without supporting electrolyte in a symmetrical flow cell.

DOI:10.1021/jacs.9b04270

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