Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 391

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

In situ polymerization of graphene nanosheets and polyurethane with enhanced mechanical and thermal properties

Wang, Xin, Hu, Yuan, Song, Lei, Yang, Hongyu, Xing, Weiyi, Lu, Hongdian·2011·Journal of Materials Chemistry

Abstract

Polyurethane (PU) composites reinforced with graphene nanosheets (GNSs) were prepared by in situpolymerization. Graphene nanosheets, which were derived from chemically reduced graphite oxide (GO) nanosheets, were characterized by solid-state 13C MAS NMR, XRD and FTIR. A morphological study showed that, due to the formation of chemical bonds, the GNS was dispersed well in the PU matrix. With the incorporation of 2.0 wt% of GNSs, the tensile strength and storage modulus of the PU increased by 239% and 202%, respectively. The nanocomposites displayed high electrical conductivity, and good thermal stability of PU was also achieved. The facile and rapid technique presented here will provide an effective and promising method of preparing graphene-based polymer composites.

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 · 10.1039/c0jm03710a

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 391

Metadata

Authors
Wang, X., Hu, Y., Song, L., Yang, H., Xing, W., & Lu, H.
Venue
Journal of Materials Chemistry
Published
2011
Volume
21
Issue
12
Pages
4222
ISSN
  • 0959-9428 (ISSN-L)
  • 1364-5501
OpenAlex
W1974170022
Cited by
391
References
4
FWCI
13.32
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: In situ polymerization of graphene nanosheets and polyurethane with enhanced mechanical and thermal properties. https://app.cochat.ai/papers/10.1039/c0jm03710a
The paper
Wang, X., Hu, Y., Song, L., Yang, H., Xing, W., & Lu, H. (2011). In situ polymerization of graphene nanosheets and polyurethane with enhanced mechanical and thermal properties. Journal of Materials Chemistry. https://doi.org/10.1039/c0jm03710a

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.