Large variability and complexity of isothermal solubility for a series of redox-active phenothiazines

Anton S. Perera, T. Malsha Suduwella, N. Harsha Attanayake, Rahul Kant Jha, William L. Eubanks, Ilya A. Shkrob, Chad Risko, Aman Preet Kaur, Susan A. Odom

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)8705-8715
Number of pages11
JournalMaterials Advances
Issue number23
StatePublished - Oct 4 2022

Bibliographical note

Funding Information:
This work was supported as part of the Joint Center for Energy Storage Research (JCESR), an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. C. R. acknowledges support from the National Science Foundation through the Established Program to Stimulate Competitive Research (EPSCoR) Track 2 program under cooperative agreement number 2019574. We acknowledge the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for their fantastic support and collaboration and use of the Lipscomb Compute Cluster and associated research computing resources.

Publisher Copyright:
© 2022 RSC.

ASJC Scopus subject areas

  • Chemistry (miscellaneous)
  • Materials Science (all)


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