(501e) Leveraging Non-Ideal Behavior in Binary Ionic-Liquid Mixtures to Design Efficient Electrolytes

Binary mixtures consisting of ionic and molecular solvents are known to exhibit composition dependent non-monotonic thermophysical and transport properties. These deviations from ideality are more pronounced in some mixtures and may be highly beneficial for specific applications. For example, ionic conductivity of binary mixtures can be increased multifold by suitable choice of solvents, providing a powerful approach for electrolyte design in electrochemical systems and energy storage applications. This nonideality has often been attributed to changes in degree of solvation of the ions, or relative dominance between polar or non-polar interactions. In this work, we implement molecular dynamic simulations to examine a large number of binary mixtures formed using a palette of anions, cations, and molecular solvents, and identify systems that exhibit enhanced ionic conductivities due to mixing. We estimate ionic conductivities of these mixtures at different mole ratios of its constituent solvents and compare them with experimental values. We examine trends in peak conductivity and self-diffusion coefficients of individual species to gauge concentration dependence of transport properties, and identify the specific molecular features such as size, shape, charge-distribution etc. significantly impacting the nonideality. We also analyze changes in spatial molecular organization and formation of mesoscale features such as ion-rich channels, micelle-like or reverse-micelle-like structures, and non-polar globules that may facilitate or hinder ion transport across the electrolytes. This work probes into the causes of nonideality in ionic conductivity values of binary mixtures and proposes a strategy to design efficient electrolytes by tailoring molecular features and solvent composition.