(103f) Systems Approaches to Predict the Aqueous Solubility of Quinone Molecules for Flow Battery Applications

Flow batteries are electrochemical devices in which electrical energy is stored in the form of chemical energy in its electrolytes. During operation, the electrolytes are circulated through the flow battery cell and the stored chemical energy is converted to electrical energy through an electrochemical reaction. The major disadvantage of flow batteries over other storage devices is their low energy density. Energy densities of existing electrolytes can be improved by increasing their solubility in solvents. Identifying new electrolyte chemistries with reasonable energy densities can make flow batteries economically competent. Quinones are gaining interest as electrolytes for flow batteries in the past few years due to their ability to supply two electrons and impressive solubility characteristics, which result in relatively higher energy densities compared to vanadium redox couples. Suleyman et. al. [1] computationally explored the prospects of using Quinone molecules for both positive and negative electrolytes of RFB by substituting hydrogen atoms of Quinone molecules with different functional groups.

The present work focuses on identifying generic correlations to predict the solvation free-energy of Quinone derivatives [1] using the structural features of the molecules. One approach is to identify the solvation free-energy through group contribution method. Group contribution methods involve two steps. The contribution of each fragment of a structure is first identified using linear regression from the available data. Then the fragments of the given test molecule is counted and the end property is evaluated. Another approach is to predict solvation free-energy using Quantitative Structural Property Relationship (QSPR) methods. QSPR approach involves three steps. First step is to identify the structural features that can affect solvation free-energy such as polar surface area, refractivity etc. Then a linear/nonlinear relationship between the structural features and end property is identified. Finally, the structural feature values for the given test molecule is obtained and the end property is evaluated using the identified relationship. These relationships help in further exploring novel Quinone derivatives having higher aqueous solubilities.

References:

[1] S. Er, C. Suh, M. P. Marshak, and A. Aspuru-Guzik, “Computational design of molecules for an all-quinone redox flow battery,” Chem. Sci., vol. 6, no. 2, pp. 885–893, 2015.