Scaling-Up Simulations of Diffusion in Microporous Materials

Pireddu, Giovanni; Pazzona, Federico G.; Demontis, Pierfranco; Załuska‐Kotur, Magdalena A.

doi:10.1021/acs.jctc.9b00801

Cited by 3 publications

(2 citation statements)

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“…Since temporal channel fluctuations increase transport coefficients, and since inter-particle interactions, or crowding effects, generally decrease diffusion, it is natural to ask if one can predict which effect dominates. Even though recent numerical work showed that diffusion enhancement could be obtained with increased particle density in microporous matrices (Obliger et al 2019), sometimes even exhibiting a maximum (Pireddu et al 2019) at a certain density, it was also noticed that the effect can depend strongly on the precise details of the system understudy (Obliger et al 2023). These results thus call for a general theoretical investigation.…”

Section: Introductionmentioning

confidence: 99%

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Interactions enhance dispersion in fluctuating channels via emergent flows

Wang,

Dean,

Marbach

et al. 2023

J. Fluid Mech.

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Understanding particle motion in narrow channels can guide progress in numerous applications, from filtration to vascular transport. Thermal or active fluctuations of fluid-filled channel walls can slow down or increase the dispersion of tracer particles via entropic trapping in the wall bulges or hydrodynamic flows induced by wall fluctuations, respectively. Previous studies concentrated primarily on the case of a single Brownian tracer. Here, we address what happens when there is a large ensemble of interacting Brownian tracers – a common situation in applications. Introducing repulsive interactions between tracer particles, while ignoring the presence of a background fluid, leads to an effective flow field. This flow field enhances tracer dispersion, a phenomenon reminiscent of that seen for single tracers in incompressible background fluid. We characterise the dispersion by the long-time diffusion coefficient of tracers numerically and analytically with a mean-field density functional analysis. We find a surprising effect where an increased particle density enhances the diffusion coefficient, challenging the notion that crowding effects tend to reduce diffusion. Here, inter-particle interactions push particles closer to the fluctuating channel walls. Interactions between the fluctuating wall and the now-nearby particles then drive particle mixing. Our mechanism is sufficiently general that we expect it to apply to various systems. In addition, our perturbation theory quantifies dispersion in generic advection–diffusion systems.

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Section: Introductionmentioning

confidence: 99%

“…2019), sometimes even exhibiting a maximum (Pireddu et al. 2019) at a certain density, it was also noticed that the effect can depend strongly on the precise details of the system understudy (Obliger et al. 2023).…”

Section: Introductionmentioning

confidence: 99%

Interactions enhance dispersion in fluctuating channels via emergent flows

Wang,

Dean,

Marbach

et al. 2023

J. Fluid Mech.

View full text Add to dashboard Cite

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From molecular sieving to gas effusion through nanoporous 2D graphenes: Comparison between analytical predictions and molecular simulations

Guo,

Galliero,

Vermorel

2023

The Journal of Chemical Physics

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In this paper, we study the permeation of polyatomic gas molecules through 2D graphene membranes. Using equilibrium molecular dynamics simulations, we investigate the permeation of pure gas compounds (CH4, CO2, O2, N2, and H2) through nanoporous graphene membranes with varying pore sizes and geometries. Our simulations consider the recrossing mechanism, often neglected in previous studies, which has a significant effect on permeation for intermediate pore size to molecular diameter ratios. We find that the permeation process can be decoupled into two steps: the crossing process of gas molecules through the pore plane and the escaping process from the pore region to a neighboring adsorption site, which prevents recrossing. To account for these mechanisms, we use a permeance model expressed as the product of the permeance for the crossing process and the probability of molecule escape. This phenomenological model is extended to account for small polyatomic gas molecules and to describe permeation regimes ranging from molecular sieving to effusion. The proposed model captures the temperature dependence and provides insights into the key parameters of the gas/membrane interaction controlling the permeance of the system. This work lays the foundation for predicting gas permeance and exploring membrane separation factors in 2D materials such as graphene.

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