Electrolytes play a critical role in performance, safety, cost, and life of battery systems. Based on publications and patents alone, an enormous amount of research funding is spent each year investigating new or existing electrolyte systems. Electrochemical energy storage devices such as Li-ion cells and other emerging cell chemistries (e.g., Na, Mg) comprise a multi-billion dollar market that is projected to have greater than linear growth in coming decades to surpass $20B USD per annum. This collective emphasis within academic and market sectors calls for computational tools that can economically accelerate selection and characterization of advanced electrolyte systems. The focus of this work is to demonstrate value through intelligent combination of chemical physics models with ab initio techniques such as DFT. A logical target is electrolyte systems for Li-ion and Na-ion systems, the latter being an area of increased interest in light of its economic potential. Recent refinement and extension of chemical physics modeling to multi-component electrolyte systems has produced tools of unequaled speed and high accuracy. The Advanced Electrolyte Model (AEM, R&D 100 award, 2014) is an example of such a molecular-based tool that has been successfully applied to predict a wide spectrum of molecular and macro-scale properties for battery electrolytes. AEM employs a “static model” approach under the NPNRAMSA basis1, wherein aspects of key interactions such as ion solvation are defined through time averages that incorporate solvent residence times around ions and configuration-dependent distance-of-approach of solvents (ligands) to ions. Only reference values are needed for such averages within AEM, akin to “standard state” values. DFT and MD are logical starting points for such averaged or optimized quantities, as they yield rich information covering configurational and statistical aspects of molecular interactions. The challenge then is to standardize the computational “hand-shake” between DFT/MD and AEM. Two parameters are of primary interest in this model integration exercise. The first is the relative coordination between solvent molecules and an ion in terms of solvent alignment. This parameter has much influence on the effective size of the solvated ion under faradaic transport conditions. Expectedly, solvent alignment will be sensitive to the true solvation number at the salt concentration of interest, accounting for steric factors and field effects. The second parameter of interest is the average solvent residence time of a solvent in the immediate proximity of an ion (e.g., primary solvation shell), which reflects the relative strength of association between solvent and ion. Both of these parameters can be approximated by looking at energy-optimized configurations with representative populations of members (DFT, e.g. ref [1]) and by tracking pair correlation quantities over the simulation timeline (MD). We will present examples of such information and their use within AEM for Li-ion or Na-ion electrolytes such as organic carbonates with LiPF6 or NaPF6. A computational tool developed for materials will be confirmed as robust for more widespread use when it is amenable to investigating a diversity of material targets. We consider the comparison of Li-ion and Na-ion systems to gain understanding of fundamental differences that would influence electrochemical cell behavior. In general terms, it is anticipated that since sodium is typically less solvated than lithium in solution (due to its lower surface charge density) as seen in Fig. 1 for aqueous systems, then we could exploit this attribute to design electrolytes of lower viscosity, since viscosity is dependent in part to the structure in solution, of which ion solvation is a strong contributor in electrolytes [2] (while this concept is demonstrated for aqueous systems in Figs. 1 and 2, our electrolyte targets in the battery space will be non-aqueous). Having lower viscosity will provide a distinct benefit at lower temperatures, where battery electrolytes exhibit high viscosities that impact battery power and pulse duration [3]. Thus, we seek those instances where Na-ion electrolytes could provide superior conductivity at low temperatures, as compared to Li-ion systems. This work is sponsored by the Laboratory Directed Research and Development Program at the INL, under management of the Battelle Energy Alliance, LLC. 1. M. T. Benson, M. K. Harrup, K. Gering, Computational and Theoretical Chemistry 1005 (2013) 25–34. 2. K. L. Gering, Electrochim. Acta, Vol. 51, 3125 (2006). 3. K. L. Gering, ECS Transactions, Vol. 1 (26), 119 (2006). 1 Non-primitive, non-restricted associative mean spherical approximation. Figure 1
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May 1, 2023
Qiong Wang + 4
Open Access