scholarly journals Molecular dynamics study of pressure-driven water transport through graphene bilayers

2016 ◽  
Vol 18 (3) ◽  
pp. 1886-1896 ◽  
Author(s):  
Bo Liu ◽  
Renbing Wu ◽  
Julia A. Baimova ◽  
Hong Wu ◽  
Adrian Wing-Keung Law ◽  
...  

Water molecules form layered structures inside graphene bilayers and ultra-high pressure-driven flow rates can be observed.

RSC Advances ◽  
2016 ◽  
Vol 6 (68) ◽  
pp. 63586-63596 ◽  
Author(s):  
Luying Wang ◽  
Randall S. Dumont ◽  
James M. Dickson

The amorphous aromatic polyamide membranes with different membrane densities were modeled to study the porous structure of free-volume pores and the pressure-driven water transport by using molecular dynamics simulations.


2019 ◽  
Vol 21 (38) ◽  
pp. 21389-21406 ◽  
Author(s):  
Pooja Sahu ◽  
Sk. Musharaf Ali

In the quest for identifying a graphene membrane for efficient water desalination, molecular dynamics simulations were performed for the pressure-driven flow of salty water across a multilayer graphene membrane.


2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Hong-Ji Yan ◽  
Zhen-Hua Wan ◽  
Feng-Hua Qin ◽  
De-Jun Sun

Abstract A modified multiscale method without constitutive equation is proposed to investigate the microscopic information and macroscopic flow properties of polymeric fluid with the memory effect between parallel plates. In this method, the domain is entirely described by macromodel with isolated molecular dynamics simulations applied to calculate the necessary local stresses. The present method is first verified by the creep-recovery motion and pressure-driven flow, and all results are in excellent agreement with the available numerical solutions in literature. Then, the method is extended to simulate two typical problems of relatively large spatial scale in general beyond the capability of molecular dynamics simulations. In the planar Couette flow, the relationship between macroscopic properties and the time evolution of local molecular information is investigated in detail without long time averaging. All results that are consistent with nonequilibrium molecular dynamics and literature qualitatively or quantitatively demonstrate the validity of present multiscale method in simulating transient viscoelastic flows and the capacity to obtain the polymer information. In the pressure-driven flow, a general monotonically decreasing relationship between the maximum or average velocities and the polymer concentrations has been found regardless of the polymer chain length. Particularly, the reference concentration that satisfies a power law with chain length is closely related to the overlap concentration, and the reference velocity is exactly the relevant velocity of Newtonian fluid with corresponding zero shear rate viscosity.


2021 ◽  
Vol 7 (31) ◽  
pp. eabf0669
Author(s):  
Yoshiki Ishii ◽  
Nobuyuki Matubayasi ◽  
Go Watanabe ◽  
Takashi Kato ◽  
Hitoshi Washizu

Self-assembled ionic liquid crystals can transport water and ions via the periodic nanochannels, and these materials are promising candidates as water treatment membranes. Molecular insights on the water transport process are, however, less investigated because of computational difficulties of ionic soft matters and the self-assembly. Here we report specific behavior of water molecules in the nanochannels by using the self-consistent modeling combining density functional theory and molecular dynamics and the large-scale molecular dynamics calculation. The simulations clearly provide the one-dimensional (1D) and 3D-interconnected nanochannels of self-assembled columnar and bicontinuous structures, respectively, with the precise mesoscale order observed by x-ray diffraction measurement. Water molecules are then confined inside the nanochannels with the formation of hydrogen bonding network. The quantitative analyses of free energetics and anisotropic diffusivity reveal that, the mesoscale geometry of 1D nanodomain profits the nature of water transport via advantages of dissolution and diffusion mechanisms inside the ionic nanochannels.


2004 ◽  
Vol 76 (17) ◽  
pp. 5063-5068 ◽  
Author(s):  
David S. Reichmuth ◽  
Timothy J. Shepodd ◽  
Brian J. Kirby

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