Ion Transport in Polymerized Ionic Liquid-Ionic Liquid Blends


Mogurampelly, Santosh
Ganesan, Venkat

KAUST Grant Number


We used atomistic molecular dynamics simulations to study ion mobilities and the molecular mechanisms of transport in blends of poly(1-butyl-3-vinylimidazolium hexafluorophosphate) electrolytes with 1-butyl-3-methylimidazolium hexafluorophosphate ionic liquids. We characterized the structural and dynamical properties of the blend electrolyte systems through radial distribution functions, diffusivities, conductivities, and different time scales of relaxation and probed the correlations underlying such characteristics. Our results indicate that many features of ion transport in such blend systems mirror those observed in our earlier study of pure polymerized ionic liquids [Mogurampelly et al. J. Am. Chem. Soc. 2017, 139, 9511]. Explicitly, we observe that the anions associated with the polymerized cation move along the polymer backbone via the formation and breakup of ion pairs involving polymerized cationic monomers of different polymer chains. Interestingly, for all blend systems excepting pure polymeric ionic liquids, the anion mobilities were seen to be correlated to the ion-pair relaxation times of the free cation-anion pairs. Both the transference numbers and the deviations from the Nernst-Einstein conductivities exhibited minimal variations when examined as a function of the blend compositions. However, as a result of the influence of mobile cations, an optimal blending composition achieves the highest conductivities at a temperature normalized by the glass transition temperature.

Mogurampelly, S., & Ganesan, V. (2018). Ion Transport in Polymerized Ionic Liquid–Ionic Liquid Blends. Macromolecules, 51(23), 9471–9483. doi:10.1021/acs.macromol.8b01460

We thank Prof. Michael L. Klein for insightful discussions and Dr. Vaidya Sethuraman for assistance in computing the normal modes of polymer dynamics (results not included in this article). We acknowledge funding in part by grants from the Robert A. Welch Foundation (grant F1599), the National Science Foundation (CBET-17069698 and DMR-1721512), and King Abdullah University of Science and Technology (OSR-2016-CRG5-2993-1). Acknowledgment is also made to the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research (56715-ND9). The authors acknowledge the Texas Advanced Computing Center (TACC) for computing resources. This research also includes calculations performed on Temple University's HPC resources and thus was supported in part by the National Science Foundation through major research instrumentation grant 1625061 and by the US Army Research Laboratory under contract W911NF-16-2-0189.




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