Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session S40: Physics and Effects on Transport of Ion-Ion Correlation in Electrolyte Materials IFocus
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Sponsoring Units: DCOMP DCP DMP Chair: Nicola Molinari, Harvard University Room: 705 |
Thursday, March 5, 2020 11:15AM - 11:51AM |
S40.00001: Direct measurement of ion mobility by electrophoretic NMR and implications for correlated migration in liquid electrolytes Invited Speaker: Monika Schönhoff In studies of ion transport in electrolytes, multinuclear (1H, 7Li, 19F) electrophoretic NMR (eNMR) allows to directly measure electrophoretic mobilities, extracting them from the ion drift velocity in an electric field. |
Thursday, March 5, 2020 11:51AM - 12:03PM |
S40.00002: Modulation of ion transference number by dynamic hydrogen bond network Vera bocharova, Zaneta Wojnarowska, Andrew Erwin, Nishani Jayakody, Steven Greenbaum, Ivan Popov, catalin gainaru, Shiwang Cheng Ionic liquids (IL) are important materials with potential to be used in various technologies because of their non-toxicity, non-flammability, and high ionic conductivity. Although high conductivity in ionic liquids can be achieved, their relatively low transference number remains a problem. Ionic materials with high transference number are required to design high power batteries. In the present talk, we demonstrate that addition of small nanoparticles capable of formation of hydrogen bonds can reduce electrostatic interaction between ions in IL which improves ion dissociation. Furthermore, with increase in concentration of nanoparticle a clear evidence of network formation has been obtained from rheology. By studying various aspects of diffusion, conductivity, and dynamics in the network at ambient and elevated pressure, we found that formation of the network promotes decoupling between ion conductivity and structural relaxation and controls ion diffusion trough hydrogen bond network. Our studies provide fundamental insights into decoupling phenomena which is a major mechanism of conductivity in solid ion conductors. Furthermore, introduction of additional interaction into IL seems to be a promising direction to control diffusion of the ions. |
Thursday, March 5, 2020 12:03PM - 12:15PM |
S40.00003: Ionic Association, Solvation and Gelation in Super-Concentrated Electrolytes Michael McEldrew, Sheng Bi, Zachary Goodwin, Alexei A. Kornyshev, Martin Bazant In super-concentrated electrolytes, ion-ion association can become extensive and complex. When the extent of ion association reaches a certain threshold, infinite percolating ionic gel networks can be formed spontaneously. We developed a thermodynamic model of reversible ionic association and gelation in super-concentrated electrolytes accounting for the competition between ion solvation and ion association. Our model is able to predict the populations of ionic clusters of different sizes as a function of salt concentration, as well as capture the onset of ionic gelation. We benchmark our model against molecular dynamics simulations of aqueous LiTFSI electrolyte for a large range of salt concentrations. We find that the ionic association and gelation observed in the molecular simulations are captured nearly quantitatively by our theory. The extent of ionic association and gelation greatly affects both the fraction of “free" ions in solution that can carry ionic current, as well as the viscosity of the mixture. Thus, knowledge of what governs these phenomena is essential in understanding the transport properties of super concentrated electrolytes. |
Thursday, March 5, 2020 12:15PM - 12:27PM |
S40.00004: Unsupervised learning of ion dynamics in electrolytes using graph dynamical networks Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey C Grossman Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms, ions, or small molecules in condensed phases, which are difficult to understand due to the complexity of local environments. Here we present graph dynamical networks [1], an unsupervised learning approach for atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We apply the methodology to ion dynamics in polymer electrolytes, and show how important features related to ion-ion correlations can be automatically captured. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems. |
Thursday, March 5, 2020 12:27PM - 12:39PM |
S40.00005: Ion clustering in electrolytes: impact on correlated transport and voltage stability Eric Fadel, Nicola Molinari, Arthur France-Lanord, Boris Kozinsky, Jeffrey C Grossman The study of ionic transport is an important tool for the optimization of the performance of Lithium ion batteries. The diffusion of Lithium ions across the electrolyte system often exhibits complex correlated motion, which remain relatively poorly understood. Building on previous studies showing the existence of cluster motion in different electrolytes (solid polymers, ionic salts …) we study the fundamentals of the clustering behavior, to provide insights into cluster formation. In particular, we develop algorithms to describe conditions for clustering to appear, the nature and composition of these clusters, the distribution in size, composition and diffusion coefficient of clusters during transport. We also investigate the lifetime of cluster and relate it to the transport properties of the electrolyte, particularly with regard to the recently reported negative transference number in a variety of systems. This study is also linked back to our work on the voltage stability of organic electrolytes, that shows how the stability of anions in these systems is weakened by the presence of the solvent, and increased by the presence of cations. Therefore, clustering behavior would impact not only the diffusion properties but also the voltage window of the electrolyte. |
Thursday, March 5, 2020 12:39PM - 12:51PM |
S40.00006: Molecular Dynamics Simulations of FcMIM-based Ionic Liquids Qing Guo, Kahchun Lau, Ravindra Pandey Room temperature ionic liquids (RTILs) are salts in liquid state below 100 °C, and have attracted great attention as electrolytes candidates in battery systems due to properties such as low vapor pressure, high ionic-conductivity, and large operation temperature window, etc. In this talk, we will present the results of Molecular Dynamics simulations identifying the structural characteristics of the ferrocene-imidazolium-based ionic liquids and their aqueous solutions. We will also provide atomistic view of the system to facilitate fundamental understanding at molecular level which is critical for improvement in the electrolyte properties for battery systems. |
Thursday, March 5, 2020 12:51PM - 1:03PM |
S40.00007: Adaptive dimensionality reduction for accelerated calculations of ionic conductivity in correlated electrolytes Nicola Molinari, Yu Xie, Ian Leifer, Boris Kozinsky Molecular dynamics (MD) computation of the ionic conductivity of correlated electrolytes does not allow for the use of the familiar mean square displacement, instead requiring to collect statistics on the total ionic flux fluctuations, which leads to the need of much longer trajectories to obtain converged results. We propose a way to systematically reduce the noise in the conductivity and diffusivity calculations from MD in regimes of moderate correlation (Haven ration 0.5-2). For systems with a time-independent correlation structure, we use spectral decomposition of the short-time position covariance matrix to learn the optimal set of diffusion eigenmodes and perform the analysis of the full MD trajectory in that basis. The proposed method allows to significantly decrease the uncertainty of conductivity estimates. |
Thursday, March 5, 2020 1:03PM - 1:15PM |
S40.00008: Asymmetric composition of ionic cluster and correlated transference number in water-in-salt electrolytes Zhou Yu, Lei Cheng The recent research breakthrough on “water-in-salt” electrolytes opens up exciting new avenues for expanding the electrochemical window of aqueous electrolytes. Subsequent work from the electrolyte community found the solvation environment of ions in the “water-in-salt” systems dictates ion mobility. In this work, the crosslinked heterogeneous ion and water domains were captured by molecular dynamics (MD) simulations and small-angle X-ray scattering techniques. The asymmetric composition of ionic clusters composed of more TFSI- ion than Li+ ion was first observed in the water-in-salt electrolyte. The decay of the Li-TFSI association correlation function is faster than that of the residence correlation function for the large percolated ionic clusters with asymmetric composition, which implies that the Li+ ion can hop through the TFSI- ions in the ionic cluster. Furthermore, a reasonably high correlated transference number (i.e., ~0.32) can be maintained even in 20 m electrolytes due to a weak negative correlation between the motion of cations and anions featuring heterogeneous ionic regions. |
Thursday, March 5, 2020 1:15PM - 1:27PM |
S40.00009: Ion-ion correlations from aggregation and the Nernst-Einstein equation Arthur France-Lanord, Jeffrey C Grossman We present a new approximation [1] to ionic conductivity well suited to dynamical atomic-scale simulations, based on the Nernst-Einstein equation. Ionic aggregates constitute the elementary charge carriers, and are considered as non-interacting species. This approach conveniently captures a dominant effect of ion-ion correlations on conductivity, namely short range interactions in the form of clustering. In addition to providing better estimates to the conductivity at a lower computational cost than exact approaches, this new method allows to understand the physical mechanisms driving ion conduction in concentrated electrolytes. As an example, we consider Li+ conduction in poly(ethylene oxide), a standard solid-state polymer electrolyte. Using our newly developed approach, we are able to reproduce recent experimental results reporting negative cation transference numbers at high salt concentrations, and to confirm that this effect can be caused by a large population of negatively charged clusters involving cations. |
Thursday, March 5, 2020 1:27PM - 1:39PM |
S40.00010: Transport anomalies in electrolytes emerging from strong ionic correlation Nicola Molinari, Jonathan Pradana Mailoa, Boris Kozinsky Electrolytes control battery recharge time and efficiency, anode/cathode stability, and ultimately safety, consequently electrolyte optimization is crucial for the design of modern energy storage device. Electrolytes containing ionic liquids (ILs) possess superior chemical stability, however, poor transport properties are hindering their applicability. These systems possess high degrees of ion-ion correlation, therefore posing a non-trivial yet crucial and interesting challenge to understanding their transport properties. |
Thursday, March 5, 2020 1:39PM - 1:51PM |
S40.00011: Exploring the ion solvation environments in solid-state polymer electrolytes at different concentrations through free-energy sampling. Siddharth Sundarararaman, David Prendergast A major obstacle to improving the performance of Li ion solid state polymer electrolyte batteries is that the exact mechanism of ion conduction in such systems is not well understood. A deeper understanding of solvation from atomistic simulations would prove to be a great stride towards overcoming this challenge. Unfortunately, systematic differences were observed in local forces predicted by various classical force fields in literature and ab-initio results for the system of interest (PEO/LiTFSI). Hence, parameters in the GAFF potential were modified to predict various structural features like bond length, angles and dihedrals in closer agreement with ab-initio estimates, resulting in significant improvements in the local interactions between atoms. These accurate classical force fields were then applied to the study of ion solvation cages and transport in these electrolytes at different ion concentrations. Accelerated molecular dynamics and free-energy sampling techniques were employed to explore the distribution of solvation cages within these electrolytes, their variation with concentration and hopping between them as a possible mechanism of transport. These insights will go a long way towards understanding the mechanism of ion solvation and conduction. |
Thursday, March 5, 2020 1:51PM - 2:03PM |
S40.00012: Ion Correlation and Collective Dynamics in Organic Electrolytes and Ionic Liquid Mixtures: From Dilute Solutions to the Ionic Liquid Limit Chang Yun Son, Jesse G. McDaniel Quantifying ion association and collective dynamical processes in 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 electrolytes composed of binary mixtures of BMIM+BF4− and various organic solvents, water, and LiTFSI salt. We illustrate different physical regimes of characteristically distinct ion correlations for the systematic range of electrolyte concentrations and solvent dielectric strengths. Electronic polarization and solvent dielectric controls the extent of ion pairng and clustering, changing the dominant ion correlation mechanism characterized by quantifying the fractional self and distinct contributions to the net ionic conductivity. The analysis also shed light on understanding the negative transference number observed in ionic liquid mixtures and concentrated polymer electrolytes. |
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