4.5 Article

Coarse-grained molecular dynamics simulation of water diffusion in the presence of carbon nanotubes

Journal

JOURNAL OF MOLECULAR GRAPHICS & MODELLING
Volume 62, Issue -, Pages 69-73

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jmgm.2015.09.009

Keywords

Coarse-grained; Molecular dynamics; Carbon nanotubes; Water diffusion; Contrast agent

Funding

  1. [CTQ2013-47699-R]
  2. [2013/UEM11]
  3. [2014/UEM14]

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Computational modeling of the translational diffusion of water molecules in anisotropic environments entails vital relevance to understand correctly the information contained in the magnetic resonance images weighted in diffusion (DWI) and of the diffusion tensor images (DTI). In the present work we investigated the validity, strengths and weaknesses of a coarse-grained (CG) model based on the MARTINI force field to simulate water diffusion in a medium containing carbon nanotubes (CNTs) as models of anisotropic water diffusion behavior. We show that water diffusion outside the nanotubes follows Fickis law, while water diffusion inside the nanotubes is not described by a Fickis behavior. We report on the influence on water diffusion of various parameters such as length and concentration of CNTs, comparing the CG results with those obtained from the more accurate classic force field calculation, like the all-atom approach. Calculated water diffusion coefficients decreased in the presence of nanotubes in a concentration dependent manner. We also observed smaller water diffusion coefficients for longer CNTs. Using the CG methodology we were able to demonstrate anisotropic diffusion of water inside the nanotube scaffold, but we could not prove anisotropy in the surrounding medium, suggesting that grouping several water molecules in a single diffusing unit may affect the diffusional anisotropy calculated. The methodologies investigated in this work represent a first step towards the study of more complex models, including anisotropic cohorts of CNTs or even neuronal axons, with reasonable savings in computation time. (C) 2015 Elsevier Inc. All rights reserved.

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