Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 487

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Understanding Water Permeation in Graphene Oxide Membranes

Open access
Wei, Ning, Peng, Xinsheng, Xu, Zhiping·2014·ACS Applied Materials & Interfaces

Abstract

Water transport through graphene-derived membranes has gained much interest recently due to its promising potential in filtration and separation applications. In this work, we explore water permeation in graphene oxide membranes using atomistic simulations and theoretical analysis, by considering flow through the interlayer gallery, expanded channels such as wrinkles of interedge spaces, and pores within the sheet. We find that, although flow enhancement can be established by nanoconfinement, fast water transport through pristine graphene channels is prohibited by a prominent side-pinning effect from capillaries formed within oxidized regions. We then discuss several flow enhancement mechanisms through the porous microstructures of graphene oxide membranes. These understandings are integrated into a complete picture to understand water permeation through the layer-by-layer and porous microstructure and can guide rational design of functional membranes for energy and environmental applications.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/am500777b

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 2 references

Cited by

See all 487

Metadata

Authors
Wei, N., Peng, X., & Xu, Z.
Venue
ACS Applied Materials & Interfaces
Published
2014-04-10
Volume
6
Issue
8
Pages
5877-5883
ISSN
  • 1944-8244 (ISSN-L)
  • 1944-8252
OpenAlex
W1987709643
Cited by
487
References
2
FWCI
20.03
Type
journal article
Language
en
Open access
green

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: Understanding Water Permeation in Graphene Oxide Membranes. https://app.cochat.ai/papers/10.1021/am500777b
The paper
Wei, N., Peng, X., & Xu, Z. (2014). Understanding Water Permeation in Graphene Oxide Membranes. ACS Applied Materials & Interfaces. https://doi.org/10.1021/am500777b

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.