A new and accurate expression for the radial distribution function of confined Lennard-Jones fluid in carbon nanotubes

Abbaspour, Mohsen; Akbarzadeh, Hamed; Abroodi, Mousarreza

doi:10.1039/c5ra16151g

Cited by 6 publications

(3 citation statements)

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“…Using the same force cutoff distance as in the simulation, the RDF is computed. Two atomic layers can be distinguished inside the nanopore from the two peaks in the radial distribution function plot [ 46 ]. The first and second distinctive peaks occur around 4.25 Å and 7.75 Å, respectively, for circular pores.…”

Section: Resultsmentioning

confidence: 99%

Effect of Layer Orientation and Pore Morphology on Water Transport in Multilayered Porous Graphene

2022

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In the present work, the effects on water transport due to the orientation of the layer in the multilayered porous graphene and the different patterns formed when the layer is oriented to some degrees are studied for both circular and non-circular pore configurations. Interestingly, the five-layered graphene membrane with a layer separation of 3.5 Å used in this study shows that the water transport through multilayered porous graphene can be augmented by introducing an angle to certain layers of the multilayered membrane system.

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Section: Resultsmentioning

confidence: 99%

Effect of Layer Orientation and Pore Morphology on Water Transport in Multilayered Porous Graphene

2022

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“…It should be noted that, as the local environment of the RM components is anisotropic, RDFs converge to zero and not unity at long distances . This is commonly encountered in systems in confinement or at interfaces. − Typically, corrections are applied to normalize the density; however, as the RDF peak and minima positions are not affected, such a correction was not applied.…”

Section: Resultsmentioning

confidence: 99%

Reverse Micelles As Antioxidant Carriers: An Experimental and Molecular Dynamics Study

et al. 2017

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Water-in-oil microemulsions with biocompatible components were formulated to be used as carriers of natural antioxidants, such as hydroxytyrosol (HT) and gallic acid (GA). The system was composed of a mixture of natural surfactants, lecithin and monoglycerides, medium chain triglycerides, and aqueous phase. A dual approach was undertaken to study the structure and dynamics of these complicated systems. First, experimental data were collected by using adequate techniques, such as dynamic light scattering (DLS) and electron paramagnetic resonance (EPR) spectroscopy. Following this, a coarse-grained molecular dynamics (CGMD) study based on the experimental composition using the MARTINI force field was conducted. The simulations revealed the spontaneous formation of reverse micelles (RMs) starting from completely random initial conformations, underlying their enhanced thermodynamic stability. The location of the bioactive molecules, as well as the structure of the RM, were in accordance with the experimental findings. Furthermore, GA molecules were found to be located inside the water core, in contrast to the HT ones, which seem to lie at the surfactant interfacial layer. The difference in the antioxidants' molecular location was only revealed in detail from the computational analysis and explains the RM's swelling observed by GA in DLS measurements.

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“…Atomistic structural correlations in condensed-phase systems are often determined by complex multibody interactions that give rise to rich collective behaviors at the macroscale. As such, predicting how a system’s microscopic structure leads to its macroscopic properties is one of the primary challenges in equilibrium statistical mechanics. − Solving this problem is paramount in multiple fields and disciplines, as knowledge of the mapping between the microscale and macroscale can lead to enhanced system designs and to the implementation of new system functionalities. − Because of its importance, theoretical determination of structural correlation functions has been an ongoing research focus for nearly a century. ,− There are three primary approaches for predicting structural correlations: (a) applying integral equation methods, − (b) fitting data to empirically motivated functional forms, − and (c) estimating the correlation functions directly in molecular simulations. − Integral equation methods often give accurate predictions for the properties of fluids. Currently, however, there is not a significantly robust theoretical framework for these methods that is void of both numerical complexity and the need for ad hoc manipulation for applications to complex molecular systems .…”

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confidence: 99%

Ex Machina Determination of Structural Correlation Functions

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Barros

et al. 2020

J. Phys. Chem. Lett.

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Determining the structural properties of condensed-phase systems is a fundamental problem in theoretical statistical mechanics. Here we present a machine learning method that is able to predict structural correlation functions with significantly improved accuracy in comparison with traditional approaches. The usefulness of this ex machina (from the machine) approach is illustrated by predicting the radial distribution functions of two paradigmatic condensed-phase systems, a Lennard-Jones fluid and a hard-sphere fluid, and then comparing those results to the results obtained using both integral equation methods and empirically motivated analytical functions. We find that application of the developed ex machina method typically decreases the predictive error by more than an order of magnitude in comparison with traditional theoretical methods.

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A new and accurate expression for the radial distribution function of confined Lennard-Jones fluid in carbon nanotubes

Abstract: A new and accurate expression for the RDF of confined LJ fluid into carbon nanotubes has been obtained.

Cited by 6 publications

References 29 publications

Effect of Layer Orientation and Pore Morphology on Water Transport in Multilayered Porous Graphene

Effect of Layer Orientation and Pore Morphology on Water Transport in Multilayered Porous Graphene

Reverse Micelles As Antioxidant Carriers: An Experimental and Molecular Dynamics Study

Ex Machina Determination of Structural Correlation Functions

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