A first-principles study on water flow through single-walled carbon nanotubes using artificial neural network method

Samad Ahadian, Hiroshi Mizuseki, Yoshiyuki Kawazoe

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Ab initio molecular dynamics simulations are done to elucidate the electronic structure and properties of water/single-walled carbon nanotubes (SWCNTs) systems. The artificial neural network (ANN) approach and statistical methods are then used to model and analyze these properties. The ANN method substantially speeds up the ab initio electronic structure calculations and has superior accuracy in mimicking the results of such calculations. We aim to understand the effects of CNT chirality, temperature, and CNT flexibility on the water diffusion inside SWCNTs. In this regard, the CNT is fixed implies that the position of CNT is kept constant during the diffusion process. Statistical analysis of results shows that there is a nonmonotonic variation of diffusion length of water with respect to the CNT chirality. However, an increase in temperature and rigid CNTs accelerate the water diffusion.

Original languageEnglish
Pages (from-to)10227-10233
Number of pages7
JournalJournal of Nanoscience and Nanotechnology
Volume11
Issue number11
DOIs
Publication statusPublished - 2011

Keywords

  • Ab Initio Calculations
  • Artificial Neural Network
  • Carbon Nanotubes
  • Statistical Methods
  • Water Diffusion

ASJC Scopus subject areas

  • Bioengineering
  • Chemistry(all)
  • Biomedical Engineering
  • Materials Science(all)
  • Condensed Matter Physics

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