Insights into Water Permeation through hBN Nanocapillaries by Ab Initio Machine Learning Molecular Dynamics Simulations-Reference-Cited by-同舟云学术

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Insights into Water Permeation through hBN Nanocapillaries by Ab Initio Machine Learning Molecular Dynamics Simulations

Published:2020-08-10 Issue:17 Volume:11 Page:7363-7370
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Ghorbanfekr Hossein¹²^ORCID,Behler Jörg³^ORCID,Peeters François M.²^ORCID

Affiliation:

1. Data Science Hub, Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium

2. Departement Fysica, Universiteit Antwerpen,, Groenenborgerlaan 171, Antwerpen B-2020, Belgium

3. Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Tammannstrasse 6, 37077 Göttingen, Germany

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.0c01739

Reference60 articles.

1. Water conduction through the hydrophobic channel of a carbon nanotube

2. Flows in one-dimensional and two-dimensional carbon nanochannels: Fast and curious

3. Friction of Water on Graphene and Hexagonal Boron Nitride from Ab Initio Methods: Very Different Slippage Despite Very Similar Interface Structures

4. Graphene-based membranes

5. Emerging opportunities for nanotechnology to enhance water security

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