Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 314

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Ultrafast Molecule Separation through Layered WS2 Nanosheet Membranes

Sun, Luwei, Ying, Yulong, Huang, Hubiao, Song, Zhigong, Mao, Yiyin, Xu, Zhiping, Peng, Xinsheng·2014·ACS Nano

Abstract

Two-dimensional layered materials have joined in the family of size-selective separation membranes recently. Here, chemically exfoliated tungsten disulfide (WS2) nanosheets are assembled into lamellar thin films and explored as an ultrafast separation membrane for small molecules with size of about 3 nm. Layered WS2 membranes exhibit 5- and 2-fold enhancement in water permeance of graphene oxide membranes and MoS2 laminar membranes with similar rejection, respectively. To further increase the water permeance, ultrathin nanostrands are used as templates to generate more fluidic channel networks in the WS2 membrane. The water permeation behavior and separation performance in the pressure loading-unloading process reveal that the channels created by the ultrathin nanostrands are cracked under high pressure and result in a further 2-fold increase of the flux without significantly degrading the rejection for 3 nm molecules. This is supported by finite-element-based mechanical simulation. These layered WS2 membranes demonstrate up to 2 orders of magnitude higher separation performance than that of commercial membranes with similar rejections and hold the promising potential for water purification.

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/nn501786m

Cites

4 of 4 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 4 references

Cited by

See all 314

Metadata

Authors
Sun, L., Ying, Y., Huang, H., Song, Z., Mao, Y., Xu, Z., & Peng, X.
Venue
ACS Nano
Published
2014-05-28
Volume
8
Issue
6
Pages
6304-6311
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2329044055
Cited by
314
References
4
FWCI
14.12
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: Ultrafast Molecule Separation through Layered WS2 Nanosheet Membranes. https://app.cochat.ai/papers/10.1021/nn501786m
The paper
Sun, L., Ying, Y., Huang, H., Song, Z., Mao, Y., Xu, Z., & Peng, X. (2014). Ultrafast Molecule Separation through Layered WS2 Nanosheet Membranes. ACS Nano. https://doi.org/10.1021/nn501786m

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.