Molecular modelling techniques for predicting liquid–liquid interfacial properties of methanol plus alkane (n-hexane, n-heptane, n-octane) mixtures

2020 ◽  
Vol 22 (46) ◽  
pp. 27121-27133
Author(s):  
Esteban Cea-Klapp ◽  
José Manuel Míguez ◽  
Paula Gómez-Álvarez ◽  
Felipe J. Blas ◽  
Héctor Quinteros-Lama ◽  
...  

Liquid–liquid interfacial properties of methanol plus n-alkane mixtures are investigated by two complementary molecular modelling techniques: molecular dynamic simulations (MD) and density gradient theory (DGT) coupled with the PC-SAFT equation of state.

2016 ◽  
Vol 114 (16-17) ◽  
pp. 2492-2499 ◽  
Author(s):  
Gulou Shen ◽  
Christoph Held ◽  
Xiaohua Lu ◽  
Xiaoyan Ji

2013 ◽  
Vol 135 (5) ◽  
Author(s):  
R. Ansari ◽  
R. Gholami ◽  
S. Ajori

In the current study, the torsional vibration of carbon nanotubes is examined using the strain gradient theory and molecular dynamic simulations. The model developed based on this gradient theory enables us to interpret size effect through introducing material length scale parameters. The model accommodates the modified couple stress and classical models when two or all material length scale parameters are set to zero, respectively. Using Hamilton's principle, the governing equation and higher-order boundary conditions of carbon nanotubes are obtained. The generalized differential quadrature method is utilized to discretize the governing differential equation of the present model along with two boundary conditions. Then, molecular dynamic simulations are performed for a series of carbon nanotubes with different aspect ratios and boundary conditions, the results of which are matched with those of the present strain gradient model to extract the appropriate value of the length scale parameter. It is found that the present model with properly calibrated value of length scale parameter has a good capability to predict the torsional vibration behavior of carbon nanotubes.


2019 ◽  
Vol 150 (11) ◽  
pp. 114703 ◽  
Author(s):  
Parisa Naeiji ◽  
Tom K. Woo ◽  
Saman Alavi ◽  
Farshad Varaminian ◽  
Ryo Ohmura

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