Prediction of thermodynamic properties of organic mixtures: Combining molecular simulations with classical thermodynamics
Publication type: Journal Article
Publication date: 2020-11-01
scimago Q2
wos Q2
SJR: 0.578
CiteScore: 5.4
Impact factor: 2.7
ISSN: 03783812, 18790224
Physical and Theoretical Chemistry
General Chemical Engineering
General Physics and Astronomy
Abstract
The binary interaction parameters of the nonrandom two liquid (NRTL) thermodynamic model are predicted for several organic mixtures using molecular simulations. Based on the theoretical framework of the two-fluid theory, the binary interaction parameters are expressed in terms of the interaction energies, size of the molecules, and size of the local molecular domains; these quantities are calculated from molecular simulations. We show that our technique is robust in terms of its predictions involving organic mixtures with compatible chemical characteristics while we propose possible modifications in the case of mixtures involving incompatible chemical components or significant size disparity, where there is a notable difference between the interaction parameters calculated from simulations and those obtained from experimental data regression. We further demonstrate that the binary interaction parameters calculated from data regression are not unique and that molecular simulations can guide the parameter selection process by identifying physically relevant binary interaction parameters. Requiring only the local molecular structure information from molecular simulations, the method offers fast and reliable prediction of phase equilibrium properties, especially in cases where limited experimental data are available.
Found
Nothing found, try to update filter.
Found
Nothing found, try to update filter.
Top-30
Journals
|
1
|
|
|
Construction and Building Materials
1 publication, 11.11%
|
|
|
Chemical Engineering Science
1 publication, 11.11%
|
|
|
Molecular Pharmaceutics
1 publication, 11.11%
|
|
|
Mendeleev Communications
1 publication, 11.11%
|
|
|
Metals
1 publication, 11.11%
|
|
|
Industrial & Engineering Chemistry Research
1 publication, 11.11%
|
|
|
Chinese Journal of Chemical Engineering
1 publication, 11.11%
|
|
|
Optics and Laser Technology
1 publication, 11.11%
|
|
|
Journal of Molecular Liquids
1 publication, 11.11%
|
|
|
1
|
Publishers
|
1
2
3
4
5
|
|
|
Elsevier
5 publications, 55.56%
|
|
|
American Chemical Society (ACS)
2 publications, 22.22%
|
|
|
OOO Zhurnal "Mendeleevskie Soobshcheniya"
1 publication, 11.11%
|
|
|
MDPI
1 publication, 11.11%
|
|
|
1
2
3
4
5
|
- We do not take into account publications without a DOI.
- Statistics recalculated weekly.
Are you a researcher?
Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
9
Total citations:
9
Citations from 2025:
1
(11.11%)
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
K. Ravichandran A. et al. Prediction of thermodynamic properties of organic mixtures: Combining molecular simulations with classical thermodynamics // Fluid Phase Equilibria. 2020. Vol. 523. p. 112759.
GOST all authors (up to 50)
Copy
K. Ravichandran A., Tun H., Khare R., Chen C. Prediction of thermodynamic properties of organic mixtures: Combining molecular simulations with classical thermodynamics // Fluid Phase Equilibria. 2020. Vol. 523. p. 112759.
Cite this
RIS
Copy
TY - JOUR
DO - 10.1016/j.fluid.2020.112759
UR - https://doi.org/10.1016/j.fluid.2020.112759
TI - Prediction of thermodynamic properties of organic mixtures: Combining molecular simulations with classical thermodynamics
T2 - Fluid Phase Equilibria
AU - K. Ravichandran, Ashwin
AU - Tun, Hla
AU - Khare, Rajesh
AU - Chen, Chau-Chyun
PY - 2020
DA - 2020/11/01
PB - Elsevier
SP - 112759
VL - 523
SN - 0378-3812
SN - 1879-0224
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2020_K. Ravichandran,
author = {Ashwin K. Ravichandran and Hla Tun and Rajesh Khare and Chau-Chyun Chen},
title = {Prediction of thermodynamic properties of organic mixtures: Combining molecular simulations with classical thermodynamics},
journal = {Fluid Phase Equilibria},
year = {2020},
volume = {523},
publisher = {Elsevier},
month = {nov},
url = {https://doi.org/10.1016/j.fluid.2020.112759},
pages = {112759},
doi = {10.1016/j.fluid.2020.112759}
}