scholarly journals Boundary effects on classical liquid density fluctuations

2021 ◽  
Vol 104 (4) ◽  
Author(s):  
K. E. L. de Farias ◽  
Azadeh Mohammadi ◽  
Herondy F. Santana Mota
Keyword(s):  
Density Fluctuations ◽  
Boundary Effects ◽  
Liquid Density

Hexatic Order and Liquid Density Fluctuations

1984 ◽  
Vol 53 (22) ◽  
pp. 2133-2136 ◽  
Author(s):  
G. Aeppli ◽  
R. Bruinsma
Keyword(s):  
Density Fluctuations ◽  
Liquid Density

scholarly journals On Clustering Impurities by Liquid Density Fluctuations

2010 ◽  
Vol 2010 ◽  
pp. 1-5
Author(s):  
Alexander L. Shimkevich
Keyword(s):  
Thermal Conductivity ◽  
Colloidal Particles ◽  
Fractal Structure ◽  
Density Fluctuations ◽  
Liquid Density ◽  
Particle Loading ◽  
Two Phase ◽  
New Class ◽  
Recent Developments ◽  
Natural Mechanism

Recent developments in liquid technology have created a new class of fluids called “nanofluids” which are two-phase mixtures of a non-metal-liquid matrix and addon particles usually less than 100 nm in size. It is reputed that such liquids have a great potential for application. Indeed, many tests have shown that their thermal conductivity can be increased by almost 20% compared to that of the base fluids for a relatively low particle loading (of 1 up to 5% in volume). It is confirmed by experimental data and simulation results. In this study, the author considers an effect of impurity clustering by liquid density fluctuations as a natural mechanism for stabilizing microstructure of the colloidal solution and estimates the effect of fractal structure of colloidal particles on thermal conductivity of water. The results of this study may be useful for motivating choosing the composition of heat-transfer suspension and developing technology for making the appropriate nanofluid.

Can liquid density-fluctuations near solid surface drive the motion of nanoscale droplets?

2020 ◽  
Vol 761 ◽  
pp. 138066
Author(s):  
Kaustubh S. Rane
Keyword(s):  
Solid Surface ◽  
Density Fluctuations ◽  
Liquid Density

Boundary Effects in Risky Chance

1993 ◽  
Author(s):  
Sandra L. Schneider ◽  
John F. Van Steenburgh ◽  
Morey Wong
Keyword(s):  
Boundary Effects

INVESTIGATING ROCK-FACE BOUNDARY EFFECTS ON CAPILLARY PRESSURE AND RELATIVE PERMEABILITY MEASUREMENTS

2011 ◽  
Vol 14 (5) ◽  
pp. 395-409 ◽  
Author(s):  
O. A. Al-Omair ◽  
S. M. Al-Mudhhi ◽  
M. M. Al-Dousari
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Boundary Effects ◽  
Rock Face

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

2019 ◽  
Author(s):  
Joshua Horton ◽  
Alice Allen ◽  
Leela Dodda ◽  
Daniel Cole
Keyword(s):  
Quantum Mechanics ◽  
Force Field ◽  
Organic Molecules ◽  
Force Fields ◽  
Liquid Density ◽  
Small Organic Molecules ◽  
Automated Generation ◽  
Energy Of Hydration ◽  
Novel Method ◽  
Force Field Parameters

<div><div><div><p>Modern molecular mechanics force fields are widely used for modelling the dynamics and interactions of small organic molecules using libraries of transferable force field parameters. For molecules outside the training set, parameters may be missing or inaccurate, and in these cases, it may be preferable to derive molecule-specific parameters. Here we present an intuitive parameter derivation toolkit, QUBEKit (QUantum mechanical BEspoke Kit), which enables the automated generation of system-specific small molecule force field parameters directly from quantum mechanics. QUBEKit is written in python and combines the latest QM parameter derivation methodologies with a novel method for deriving the positions and charges of off-center virtual sites. As a proof of concept, we have re-derived a complete set of parameters for 109 small organic molecules, and assessed the accuracy by comparing computed liquid properties with experiment. QUBEKit gives highly competitive results when compared to standard transferable force fields, with mean unsigned errors of 0.024 g/cm3, 0.79 kcal/mol and 1.17 kcal/mol for the liquid density, heat of vaporization and free energy of hydration respectively. This indicates that the derived parameters are suitable for molecular modelling applications, including computer-aided drug design.</p></div></div></div>

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