Ion–ion interactions of LiPF6 and LiBF4 in propylene carbonate solutions

Takeuchi, Munetaka; Kameda, Yasuo; Umebayashi, Yasuhiro; Ogawa, Sari; Sonoda, Takaaki; Ishiguro, Shin‐ichi; Fujita, Miho; Sano, Mitsuru

doi:10.1016/j.molliq.2009.07.003

Cited by 115 publications

(141 citation statements)

References 47 publications

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“…The NernstEinstein equation is a commonly used tool to determine the level of ionic association (sometimes expressed as the ionic dissociation) for liquid electrolytes [45,60,61]. Figure 11 shows the predicted conductivity was significantly higher than the corresponding measured conductivity.…”

Section: Ionic Associationmentioning

confidence: 98%

Pulsed-Field Gradient NMR Self Diffusion and Ionic Conductivity Measurements for Liquid Electrolytes Containing LiBF4 and Propylene Carbonate

Richardson

Voice

Ward

2014

Electrochimica Acta

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Page 1 of 42A c c e p t e d M a n u s c r i p t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 AbstractLiquid electrolytes have been prepared using lithium tetrafluoroborate (LiBF 4 ) and propylene carbonate (PC). Pulsed-field gradient nuclear magnetic resonance (PFG-NMR) measurements were taken for the cation, anion and solvent molecules using lithium ( 7 Li), fluorine ( 19 F) and hydrogen ( 1 H) nuclei, respectively. It was found that lithium diffusion was slow compared to the much larger fluorinated BF 4 anion likely resulting from a large solvation shell of the lithium. Ionic conductivity and viscosity have also been measured for a range of salt concentrations and temperatures. By comparing the measured conductivity with a ideal predicted conductivity derived from the Nernst-Einstein equation and self diffusion coefficients the degree of ionic association of the anion and cation was determined and was observed to increase with salt concentration and temperature. Using the measured viscosity and self diffusion coefficients the effective radius of each of the species was determined for various salt concentrations.

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Section: Ionic Associationmentioning

confidence: 98%

Pulsed-Field Gradient NMR Self Diffusion and Ionic Conductivity Measurements for Liquid Electrolytes Containing LiBF4 and Propylene Carbonate

Richardson

Voice

Ward

2014

Electrochimica Acta

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“…Takeuchi et al measured the self-diffusivities of LiPF 6 in PC by the pulse gradient spin echo (PGSE) NMR technique in the concentration range of 0.1 M to 3 M at room temperature (T = 298 K). 23 Figure 1 shows the self-diffusivities of cation (D + ), anion (D − ), and solvent (D 0 ), respectively. The self-diffusivities decrease as its concentration increases because the ion-ion interaction becomes stronger at higher concentrations.…”

Section: Experimental Values and Evaluation Of Transport Properties Omentioning

confidence: 99%

A Method for Estimating Transport Properties of Concentrated Electrolytes from Self-Diffusion Data

Kim

Srinivasan

2016

J. Electrochem. Soc.

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A theoretical method is introduced that calculates the electrolyte-transport properties using Onsager-Stefan-Maxwell (OSM) theory (concentrated-solution theory) and the generalized-Darken relation. OSM theory is used to relate binary diffusivities to the transport properties of the electrolyte, including transference number, ionic conductivity, and Fickian diffusivity. The generalized-Darken relation is proposed to calculate the binary diffusivities of multicomponent systems from self-diffusivities. Finally, an example is demonstrated to show the details of how this method can be used to estimate transport properties. The calculated properties were reasonably close to the previously reported experimental values. © The Author To develop a physics-based lithium-ion battery model or battery management systems, accurate estimation of electrolyte transport properties are essential. This is especially true as new high-energy cells are designed with thick electrodes wherein electrolyte transport dominates the end of discharge at high current densities.However, the experimental measurements of the electrolyte properties are not trivial because it requires well-controlled experimental apparatus to obtain accurate values. Depending on the technique used, the estimation of the transport properties can be very sensitive to both natural and induced convection when estimating the diffusion coefficient. In addition, solvent reduction; inevitable when using lithium metal electrodes in electrochemical techniques, results in side reactions, and gas formation, leading to convolution of the obtained data. To properly describe mass transport in a solution, n(n − 1)/2 transport properties are necessary for n chemically distinct species because the system has n(n − 1)/2 binary diffusivities (D ij ).2 In a general lithium-ion battery, the electrolyte includes a cation, an anion, and a solvent; therefore, three independent transport properties are required. The cation transference number relative to the solvent t 0 + , ionic conductivity κ, and Fickian diffusivity D serve to describe the mass transport of the electrolyte. 3,4 In this work, we introduced a novel method of evaluating the electrolyte-transport properties using the Onsager-Stefan-Maxwell (OSM) theory (concentrated-solution theory) 2-10 combined with the generalized-Darken relation. 11,12 This method provides a way to estimate transport properties with minimized experimental measurements. The key to this method is converting self-diffusivities to binary diffusivities of multicomponent systems using the generalized-Darken relation. After the conversion, OSM theory can be used to calculate the electrolyte transport properties from the binary diffusivities. This paper is intended to provide the details of this conversion and evaluate its accuracy. As an example, we demonstrate the applicability by calculating the three transport properties of LiPF 6 in PC, and show that the estimated properties compare favorably with published literature data. Onsager-Stefan-Maxwell (OSM) Theor...

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“…9. The experimental data in the graph is derived from a nuclear magnetic resonance (NMR) technique to measure diffusion: 51 Tsunekawa et al 52 employed NMR to measure the diffusion of Li + and BF Comparison of the estimated Li + diffusivity in pure PC from Takeuchi et al, 11 Tsunekawa et al, 52 Postupna et al, 12 Soetens et al, 21 and the present work.…”

Section: Discussionmentioning

confidence: 76%

Assessing Electrolyte Transport Properties with Molecular Dynamics

Jones

Ward

Gittleson

et al. 2017

J. Electrochem. Soc.

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In this work we use estimates of ionic transport properties obtained from molecular dynamics to rank lithium electrolytes of different compositions. We develop linear response methods to obtain the Onsager diffusivity matrix for all chemical species, its Fickian counterpart, and the mobilities of the ionic species. We apply these methods to the well-studied propylene carbonate/ethylene carbonate solvent with dissolved LiBF 4 and O 2 . The results show that, over a range of lithium concentrations and carbonate mixtures, trends in the transport coefficients can be identified and optimal electrolytes can be selected for experimental focus; however, refinement of these estimation techniques is necessary for a reliable ranking of a large set of electrolytes. There is increasing interest in designing and manufacturing highperformance batteries for a wide variety of applications; 1 however, finding the best electrochemical system and configuration is a challenging task given all the possible combinations of electrodes and electrolyte components. In particular, choosing an electrolyte optimal for a given battery configuration and chemistry is daunting. Herein, we propose a computational approach to electrolyte screening and selection. Through a combination of techniques, e.g. ab initio calculations to provide fundamental parameters to molecular dynamics which, in turn, provides transport coefficients to a reaction-diffusion model of the full-scale battery, 2 we can predict battery performance. These predictions can be compared with limited experiments for validation before selecting the best candidates for intensive development. The concept of computational screening is not new, e.g. the work the Ceder group 3 and the battery-focused "Genome" efforts such as the Electrolyte Genome, 4 but here we focus on the selection of ideal electrolytes for Li batteries based on the transport properties which have been identified as critical to performance. 5,6 In order to effectively down-select from a large population of potential electrolytes, the parameters of a full-scale model of battery performance must come from more fundamental calculations. On a molecular level, the use of molecular dynamics (MD) to model diffusion, 7-15 ionic mobility, [16][17][18][19][20] and estimate the associated transport coefficients is wide-spread and has been shown to be predictive given representative interatomic potentials. In particular, Refs. 11,12,21 apply the techniques to measure the diffusivity of nonaqueous Li ion systems and Refs. 17,18 estimate the diffusivity and mobility of Li + in water.To address this big-data computational challenge, in the Theory section we introduce linear response methods to estimate the mutual diffusion and mobility coefficients of the species fluxes, using notation summarized in Table I. Generally speaking, linear response methods have many advantages over alternative methods that use large gradients/unphysical driving forces and large systems to estimate transport coefficients since they are equilibrium method...

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Ion–ion interactions of LiPF6 and LiBF4 in propylene carbonate solutions

Cited by 115 publications

References 47 publications

Pulsed-Field Gradient NMR Self Diffusion and Ionic Conductivity Measurements for Liquid Electrolytes Containing LiBF4 and Propylene Carbonate

Pulsed-Field Gradient NMR Self Diffusion and Ionic Conductivity Measurements for Liquid Electrolytes Containing LiBF4 and Propylene Carbonate

A Method for Estimating Transport Properties of Concentrated Electrolytes from Self-Diffusion Data

Assessing Electrolyte Transport Properties with Molecular Dynamics

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