The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Why Are Carbon Nanotubes Fast Transporters of Water?
Abstract
Extraordinarily fast transport of water in carbon nanotubes (CNTs) in recent experiments has been generally attributed to the smoothness of the CNT surface. Using molecular dynamics simulations we investigate water flow in (16,16) CNTs and show that the enhanced flow rates over Hagen-Poiseuille flow arise from a velocity "jump" in a depletion region at the water nanotube interface and that the water orientations and hydrogen bonding at the interface significantly affect the flow rates. For nanotube with the same smooth wall structure but with more hydrophilic Lennard-Jones (LJ) parameters of silicon, the enhancement is greatly reduced because it does not have "free" OH bonds pointing to the wall as in CNTs that would reduce the number of hydrogen bonds in the depletion layer. Roughness in the tube walls causes strong hydrogen-bonding network and no significant flow enhancement is attained in rough tubes.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Canonical dynamics: Equilibrium phase-space distributions
Hoover, W. G. (1985) · Physical Review A · doi:10.1103/physreva.31.1695 · Cited by 23,465
Polymorphic transitions in single crystals: A new molecular dynamics method
Parrinello, M., & Rahman, A. (1981) · Journal of Applied Physics · doi:10.1063/1.328693 · Cited by 19,790
GROMACS 3.0: a package for molecular simulation and trajectory analysis
Lindahl, E., Hess, B., & van der Spoel, D. (2001) · Journal of Molecular Modeling · doi:10.1007/s008940100045 · Cited by 6,977
Water conduction through the hydrophobic channel of a carbon nanotube
Hummer, G., Rasaiah, J. C., & Noworyta, J. P. (2001) · Nature · doi:10.1038/35102535 · Cited by 3,557
Enhanced flow in carbon nanotubes
Majumder, M., Chopra, N., Andrews, R., & Hinds, B. J. (2005) · Nature · doi:10.1038/438044a · Cited by 1,679
Water at Hydrophobic Surfaces: Weak Hydrogen Bonding and Strong Orientation Effects
Scatena, L. F., Brown, M. G., & Richmond, G. L. (2001) · Science · doi:10.1126/science.1059514 · Cited by 1,137
Controlled Rotation of Optically Trapped Microscopic Particles
Paterson, L., MacDonald, M. P., Arlt, J., Sibbett, W., Bryant, P. E., & Dholakia, K. (2001) · Science · doi:10.1126/science.1058591 · Cited by 1,105
Osmotic water transport through carbon nanotube membranes
Kalra, A., Garde, S., & Hummer, G. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1633354100 · Cited by 906
Rapid Transport of Gases in Carbon Nanotubes
Skoulidas, A. I., Ackerman, D. M., Johnson, J. K., & Sholl, D. S. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.185901 · Cited by 690
Fluid flow in carbon nanotubes and nanopipes
Whitby, M., & Quirke, N. (2007) · Nature Nanotechnology · doi:10.1038/nnano.2006.175 · Cited by 667
Cited by
Sun, P., Wang, K., & Zhu, H. (2016) · Advanced Materials · doi:10.1002/adma.201502595 · Cited by 668
Rao, R., Pint, C. L., Islam, A. E., Weatherup, R. S., Hofmann, S., Meshot, E. R., Wu, F., Zhou, C., Dee, N., Amama, P. B., et al. (2018) · ACS Nano · doi:10.1021/acsnano.8b06511 · Cited by 588
Wettability effect on nanoconfined water flow
Wu, K., Chen, Z., Li, J., Li, X., Xu, J., & Dong, X. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1612608114 · Cited by 567
Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective
Faucher, S., Aluru, N., Bazant, M. Z., Blankschtein, D., Brozena, A. H., Cumings, J., Pedro de Souza, J., Elimelech, M., Epsztein, R., Fourkas, J. T., et al. (2019) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.9b02178 · Cited by 365
Self-citationHow fast does water flow in carbon nanotubes?
Kannam, S. K., Todd, B. D., Hansen, J. S., & Daivis, P. J. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4793396 · Cited by 329
Metadata
- Authors
- Joseph, S., & Aluru, N. R.
- Venue
- Nano Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-01-12
- Volume
- 8
- Issue
- 2
- Pages
- 452-458
- ISSN
- 1530-6984 (ISSN-L)
- 1530-6992
- OpenAlex
- W2001979196
- Cited by
- 834
- References
- 22
- FWCI
- 28.35
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Why Are Carbon Nanotubes Fast Transporters of Water? https://app.cochat.ai/papers/10.1021/nl072385q
- The paper
- Joseph, S., & Aluru, N. R. (2008). Why Are Carbon Nanotubes Fast Transporters of Water? Nano Letters. https://doi.org/10.1021/nl072385q
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
