Mutual diffusion in binary Ar-Kr mixtures and empirical diffusion models

Zhou, Yihan; Miller, Gregory H.

doi:10.1103/physreve.53.1587

Cited by 51 publications

(49 citation statements)

References 28 publications

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“…The above relation is known in the Condensed Matter Community as Darken's equation [47,48,46,49]. Similar relation has tacitly been assumed also in the plasma community [42,35,22,23].…”

Section: Relating Self-diffusion With Interdiffusionmentioning

confidence: 86%

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Diffusivity of Mixtures in Warm Dense Matter Regime

Haxhimali

Rudd

2014

Lecture Notes in Computational Science and Engineering

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Modeling of ionic diffusion in warm dense plasma mixtures has been of longstanding interest in astrophysics and in Inertial Confinement Fusion. Here we review traditional approaches to calculating plasma diffusion using kinetic theory. We also review earlier classical molecular dynamics (MD) results. We discuss some new results from MD for self and mutual diffusion in a mixture of deuterium and argon at warm dense matter regime. We make use of Yukawa interionic potentials as an effective potential that accounts for the screening effects of the electrons to the ions. We further provide a general description of the Green-Kubo technique to extract the diffusivity of a multicomponent mixture. The description is very general and it can be extended to plasmas.

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“…The above relation is known in the Condensed Matter Community as Darken's equation [47,48,46,49]. Similar relation has tacitly been assumed also in the plasma community [42,35,22,23].…”

Section: Relating Self-diffusion With Interdiffusionmentioning

confidence: 86%

“…If we group separately the velocity autocorrelation and cross-correlation functions that appear in Maxwell-Stefan diffusion coefficient (31) and use the Fnotation of McCall and Douglass [45,28,46], we have the following:…”

Section: Relating Self-diffusion With Interdiffusionmentioning

confidence: 99%

Diffusivity of Mixtures in Warm Dense Matter Regime

Haxhimali

Rudd

2014

Lecture Notes in Computational Science and Engineering

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“…The common assumption that the cross-correlations are negligible appears to be generally true for the salt components and dissolved oxygen in the modeled system with the noted exception; however, the cross-correlations with the solvents (not shown) are relatively large and negative, most likely due to momentum conservation. 10 (The individual coefficients of the diffusion matrix being negative does not violate the overall requirement for dissipation, J · ∇μ < 0, which constrains L to be positive definite.) We also checked the satisfaction of the constraints: b m a ab = 0 and ab = ba , were both obeyed to within error.…”

Section: Resultsmentioning

confidence: 99%

“…28, Sec. 5.1), the Hamiltonian dynamics perturbed an external field E areẋ [10] where x α are the atomic positions, p α are their momenta, and is the total potential energy, and are such that rate of change of the Hamiltonian, H , is:…”

Section: Theorymentioning

confidence: 99%

“…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][8][9][10][11][12][13][14][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.…”

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confidence: 99%

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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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Separation of hydrocarbon mixture of neopentane and n‐hexane using NaY zeolite: Large distinct diffusivity

Nag

Yashonath

2022

J Comput Chem

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A recently proposed method based on Levitation and Blow torch effects, is employed here to see if it can separate a mixture of neopentane and n-hexane. The results show that the mixture can be separated with a hot zone temperature of just 40 K above the ambient temperature, 300 K. The two components are found to accumulate at the two extreme ends of the zeolite column. The computed separation factor is in the range of 10 15 -10 20 (as compared to 10 4 for existing separation methods).The energy expense for the separation is significantly smaller than for existing separation methods by several orders of magnitude. Transport (D 11 ), self (D s ), and distinct diffusivities (D d ) of the mixture were computed. The contribution of distinct diffusivity to the transport diffusivity is 70% as compared to 10%-30% seen in other separation methods and is larger by 2.3 times as compared to the self-diffusivity.

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Mutual diffusion in binary Ar-Kr mixtures and empirical diffusion models

Cited by 51 publications

References 28 publications

Diffusivity of Mixtures in Warm Dense Matter Regime

Diffusivity of Mixtures in Warm Dense Matter Regime

Assessing Electrolyte Transport Properties with Molecular Dynamics

Separation of hydrocarbon mixture of neopentane and n‐hexane using NaY zeolite: Large distinct diffusivity

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