New estimations of vapor density and surface tension of water at low temperatures using scaled model

Obeidat, Abdalla; Badarneh, Mohammad

doi:10.1016/j.molliq.2019.110952

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…In our previous work, we estimated the critical temperature using four different models of water and the critical temperatures we found were overestimated in all models [36]. On the other hand, the estimation of surface tension and vapor density were in good agreement with experimental data at low temperatures.…”

Section: Introductionsupporting

confidence: 64%

Critical temperature, surface tension and vapor density estimations of methanol using the scaled model

Obeidat

Badarneh

2019

Heliyon

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In the present work, we applied scaled model to calculate surface tension, vapor densities and the critical temperatures of four different models of methanol: namely, H1, J1, J2 and L1 models. The scaled model is based on calculating the free energy of the system. Free energy calculations were performed by applying the Bennet acceptance ratio (BAR) using Monte-Carlo simulations at low temperature range of 220K–280K. The BAR is based on calculating the free energy difference of n-molecules and (n-1)-molecules plus a free probe on methanol. Estimations of vapor densities are based on extrapolating the intercept of the scaled free energy linear line as number of molecules approaches infinity, which requires a pre-known values for liquid densities. To accomplish this, a series of molecular dynamic simulations were performed at low temperature range of 200K–300K with steps of 10K. All the estimated properties were in excellent agreement with experimental published data.

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Section: Introductionsupporting

confidence: 64%

Critical temperature, surface tension and vapor density estimations of methanol using the scaled model

Obeidat

Badarneh

2019

Heliyon

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“…41 A crucial test of the force field performance at interfaces is to compare its prediction for the surface tension with experimental results. 30,39,40,[45][46][47][48][49][50][51][52] With that regard, previous work has shown that the standard truncation of the Lennard-Jones (LJ) interaction potential can lead to large quantitative differences in the surface tension, 53 or even to qualitatively different behaviors of liquids at interfaces. 47,[54][55][56][57][58] Analytical tail corrections are commonly used for the surface tension, 50 but their implementation can be complexespecially for electrolyte solutions, and there is no guarantee that the structure and dynamics of the interface are correctly predicted by truncated potentials.…”

Section: Introductionmentioning

confidence: 99%

Molecular modeling of aqueous electrolytes at interfaces: Effects of long-range dispersion forces and of ionic charge rescaling

Breton

Joly

2020

The Journal of Chemical Physics

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Molecular dynamics simulations of aqueous electrolytes generally rely on empirical force fields, combining dispersion interactions-described by a truncated Lennard-Jones (LJ) potential-and electrostatic interactions-described by a Coulomb potential computed with a long-range solver. Recently, force fields using rescaled ionic charges [electronic continuum correction (ECC)], possibly complemented with rescaling of LJ parameters [ECC rescaled (ECCR)], have shown promising results in bulk, but their performance at interfaces has been less explored. Here, we started by exploring the impact of the LJ potential truncation on the surface tension of a sodium chloride aqueous solution. We show a discrepancy between the numerical predictions for truncated LJ interactions with a large cutoff and for untruncated LJ interactions computed with a long-range solver, which can bias comparison of force field predictions with experiments. Using a long-range solver for LJ interactions, we then show that an ionic charge rescaling factor chosen to correct long-range electrostatic interactions in bulk accurately describes image charge repulsion at the liquid-vapor interface, and the rescaling of LJ parameters in ECCR models-aimed at capturing local ion-ion and ion-water interactions in bulk-describes well the formation of an ionic double layer at the liquid-vapor interface. Overall, these results suggest that the molecular modeling of aqueous electrolytes at interfaces would benefit from using long-range solvers for dispersion forces and from using ECCR models, where the charge rescaling factor should be chosen to correct long-range electrostatic interactions.

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“…The free-energy differences for the SPC/HW-heavy-water were calculated for clusters consisting of

2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 18, 20, 50 and 75 molecules at different temperatures: T = 260K, 280K, and 300K by using Metropolis Monte-Carlo algorithm. Regarding the simulation volume, number of equilibration and number of Monte-Carlo steps, as well as the step move in three dimensions are the same as in the work of one of the co-authors, ref [ 14 ].…”

Section: Molecular Model and Simulation Detailsmentioning

confidence: 99%

“…The standard method is to calculate the vapor-liquid phase diagram as a function of temperature, then make a fit to the points using Wegner expansion with constants depending on the substance [ 12 , 13 ]. Other methods are briefly mentioned in ref [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Free-energy differences of OPC-water and SPC/HW-heavy-water models using the Bennett acceptance ratio

Khasawinah

Alzoubi

Obeidat

2022

Heliyon

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New estimations of vapor density and surface tension of water at low temperatures using scaled model

Cited by 6 publications

References 55 publications

Critical temperature, surface tension and vapor density estimations of methanol using the scaled model

Critical temperature, surface tension and vapor density estimations of methanol using the scaled model

Molecular modeling of aqueous electrolytes at interfaces: Effects of long-range dispersion forces and of ionic charge rescaling

Free-energy differences of OPC-water and SPC/HW-heavy-water models using the Bennett acceptance ratio

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