Ion Correlation and Collective Dynamics in BMIM/BF₄-Based Organic Electrolytes: From Dilute Solutions to the Ionic Liquid Limit

Abstract

Quantifying ion association and collective dynamical processes in organic electrolytes is essential for fundamental property interpretation and optimization for electrochemical applications. The extent of ion correlation depends on both the ion concentration and dielectric strength of the solvent; ions may be largely uncorrelated in sufficiently high dielectric solvents at low concentration, but properties of concentrated electrolytes are dictated by correlated and collective ion processes. In this work, we utilize molecular dynamics simulations to characterize ion association and collective ion dynamics in organic electrolytes composed of binary mixtures of 1-butyl-3-] and 1,2-dichloroethane, acetone, acetonitrile, and water solvents. We illustrate different physical regimes of characteristically distinct ion correlation for the systematic range of electrolyte concentrations and solvent dielectric strengths. Dilute electrolytes composed of low-dielectric solvents exhibit significant counterion correlation in the form of ion-pairing and clustering driven by both weak screening and relatively low solvation energies. This regime is characterized by enhanced ion coordination numbers and near equality of cation and anion diffusion coefficients, despite the significantly different ion sizes. In contrast, ion correlation in highly concentrated electrolytes is dominated by the anti-correlated motion of both like-charge and opposite-charge ions, approaching neat ionic liquid behavior. We show that the cross-over of these correlation regimes is clearly illuminated by quantifying the fractional self and distinct contributions to the net ionic conductivity. For organic electrolytes composed of low-dielectric solvents, we conclude that significant ion correlation exists at all concentrations, but the nature of the correlation changes markedly from the dilute electrolyte to the pure ionic liquid limit.

Ion Transport and the True Transference Number in Nonaqueous Polyelectrolyte Solutions for Lithium Ion Batteries

“…We further note that the trend of increasingly negative σ cat,an d at lower concentrations has been observed in MD simulations of systems other than polyelectrolytes, such as superconcentrated LiTFSI in tetraglyme as well as 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM + ][BF 4 – ]) electrolytes in a variety of solvents. 75,76 Similarly, Haskins et al 83 noted that the fraction of uncorrelated ionic motion (σ NE /σ, where σ NE is the Nernst–Einstein conductivity) increases with concentration for Li + -doped ionic liquid electrolytes (i.e., the distinct conductivity terms decrease as concentration increases). In agreement with our observations, they attribute this trend to a change in the diffusion mechanism from vehicular to more structural as concentration increases.…”

Tuning Water Networks via Ionic Liquid/Water Mixtures

“…Such trends may be difficult to infer from the collection of previously cited work, due to the high sensitivity of predicted water clustering/phase behavior to force field details [96][97][98]106]. McDaniel and coworkers have recently developed the ab initio, polarizable SAPT-FF force field for a variety of ILs [109,110], with property prediction benchmarks for neat ILs [111] as well as a variety of IL solvent mixtures [112]. For IL/water mixtures, the force field employs an ab initio description of cross-interactions [113] and water-water interactions are described by the well-benchmarked SWM4-NDP water model [114].…”

“…The IL/water mixtures were modeled utilizing the SAPT-FF force field for IL/IL and IL/water interactions [111,113] and SWM4-NDP water model [114] for water/water interactions. This explicitly polarizable, force field combination has been previously demonstrated to predict accurate conductivities of IL/water mixtures [112] and excess chemical potentials of water in ILs [28]. MD simulations were conducted using the OpenMM software package [116], on Nvidia GTX-1080-Ti GPU cards.…”

Proper Thermal Equilibration of Simulations with Drude Polarizable Models: Temperature-Grouped Dual-Nosé–Hoover Thermostat

“…39,40 The SAPT-FF was shown to predict both the static and dynamic properties of these systems accurately without any empirical adjustment and has also been successfully applied to highly charged and interfacial systems. 7,41 Details of composition and simulation parameters for the systems studied here are summarized in Supporting Information (SI).…”