TY - JOUR
T1 - Large variability and complexity of isothermal solubility for a series of redox-active phenothiazines
AU - Perera, Anton S.
AU - Suduwella, T. Malsha
AU - Attanayake, N. Harsha
AU - Jha, Rahul Kant
AU - Eubanks, William L.
AU - Shkrob, Ilya A.
AU - Risko, Chad
AU - Kaur, Aman Preet
AU - Odom, Susan A.
N1 - Publisher Copyright:
© 2022 RSC.
PY - 2022/10/4
Y1 - 2022/10/4
N2 - 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.
AB - 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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U2 - 10.1039/d2ma00598k
DO - 10.1039/d2ma00598k
M3 - Article
AN - SCOPUS:85141590827
VL - 3
SP - 8705
EP - 8715
JO - Materials Advances
JF - Materials Advances
IS - 23
ER -