2022
Fabian L. Thiemann, Christoph Schran, Patrick Rowe, Erich A. Müller, Angelos Michaelides
Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick Journal Article
In: ACS Nano, vol. 16, no. 7, pp. 10775–10782, 2022.
Abstract | Links | BibTeX | Tags: Confinement, Hydrogen bonding, Machine Learning Potentials, Water at Interfaces, Water flow
@article{Thiemann2022/10.1021/acsnano.2c02784,
title = {Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick},
author = {Fabian L. Thiemann and Christoph Schran and Patrick Rowe and Erich A. Müller and Angelos Michaelides},
doi = {10.1021/acsnano.2c02784},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
journal = {ACS Nano},
volume = {16},
number = {7},
pages = {10775–10782},
abstract = {Experimental measurements have reported ultra-fast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behaviour is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials with water experiencing indeed a $backslashapprox 5$ times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of unprecedented accuracy for water flow in single-wall nanotubes, but also provides detailed mechanistic insight into its radius and material dependence for future technological application.},
keywords = {Confinement, Hydrogen bonding, Machine Learning Potentials, Water at Interfaces, Water flow},
pubstate = {published},
tppubtype = {article}
}
Experimental measurements have reported ultra-fast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behaviour is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials with water experiencing indeed a $backslashapprox 5$ times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of unprecedented accuracy for water flow in single-wall nanotubes, but also provides detailed mechanistic insight into its radius and material dependence for future technological application.