Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 395

The DOI matches its Crossref record; 127 of the 127 references it cites were found in a registry or an index.

Graphene-based nanosheets for stronger and more durable concrete: A review

Open access
Shamsaei, Ezzatollah, de Souza, Felipe Basquiroto, Yao, Xupei, Benhelal, Emad, Akbari, Abozar, Duan, Wenhui·2018·Construction and Building Materials

Abstract

Abstract Graphene and graphene-based nanosheets (GNS) possess extraordinary mechanical, chemical, thermal and electrical properties, enabling attractive applications, ranging from structural strength/durability improvement, anti-corrosion, to self-cleaning surfaces and energy saving. Recent extensive research demonstrates that GNS also have the ability to transform conventional construction materials into smarter, stronger and more durable advanced composites. This review provides a comprehensive overview on developments and novelties in the fabrication, properties and applications of GNS in enhancing the strength and durability of concrete composites, pointing out the main challenges and future perspectives for GNS in the construction field.

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 · 10.1016/j.conbuildmat.2018.06.201

Cites

127 of 127 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 127 references

Metadata

Authors
Shamsaei, E., de Souza, F. B., Yao, X., Benhelal, E., Akbari, A., & Duan, W.
Venue
Construction and Building Materials
Publisher
Elsevier BV
Published
2018-09
Volume
183
Pages
642-660
ISSN
0950-0618 (ISSN-L)
OpenAlex
W2864025239
Cited by
395
References
127
FWCI
25.60
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: Graphene-based nanosheets for stronger and more durable concrete: A review. https://app.cochat.ai/papers/10.1016/j.conbuildmat.2018.06.201
The paper
Shamsaei, E., de Souza, F. B., Yao, X., Benhelal, E., Akbari, A., & Duan, W. (2018). Graphene-based nanosheets for stronger and more durable concrete: A review. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2018.06.201

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.