Several computational studies involving molecular simulations, density functional theory (DFT) calculations, and machine learning techniques have been utilized to study the ionic conductivity of the ILs in different solvents. While these methods can be computationally intensive, the classical theory of ion dissociation can provide reasonably accurate estimations for IL/solvent mixtures with much lower computing power. For example, Li et al. correlated the thermodynamic properties such as densities and osmotic coefficients of aqueous solutions by using the equation of state where the association energies of anions and cations are the fitted parameters. Some of these classical theories can explain the dissociation behavior of electrolytes; however, these cannot be extended to describe the dissociation behavior and ionic conductivity of the RTILs for a full range of solvent concentrations. In recent years, ion dissociation of RTILs has been estimated by independently measuring the ionic conductivity and diffusion coefficients. Watanabe's research group used electrochemical impedance spectroscopy (EIS) and pulse field gradient spin-echo nuclear magnetic resonance (PG-NMR) to measure ionic conductivity and diffusivity, respectively, of neat ionic liquids. Diffusivity was measured to calculate the ionic conductivity with the help of the Nernst-Einstein relationship. They estimated ionicity from the ratio of ionic molar conductivity measured by EIS to that calculated from the Nernst-Einstein conductivity based on the ion-diffusion coefficient measured using PG-NMR. They reported that free ion fraction is directly correlated with ionicity, whereas, Lopes et al. estimated the ILs ionicity in binary mixtures with the help of solubility data.