Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,291

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Graphene: Corrosion-Inhibiting Coating

Prasai, Dhiraj, Tuberquia, Juan Carlos, Harl, Robert R., Jennings, G. Kane, Bolotin, Kirill I.·2012·ACS Nano

Abstract

We report the use of atomically thin layers of graphene as a protective coating that inhibits corrosion of underlying metals. Here, we employ electrochemical methods to study the corrosion inhibition of copper and nickel by either growing graphene on these metals, or by mechanically transferring multilayer graphene onto them. Cyclic voltammetry measurements reveal that the graphene coating effectively suppresses metal oxidation and oxygen reduction. Electrochemical impedance spectroscopy measurements suggest that while graphene itself is not damaged, the metal under it is corroded at cracks in the graphene film. Finally, we use Tafel analysis to quantify the corrosion rates of samples with and without graphene coatings. These results indicate that copper films coated with graphene grown via chemical vapor deposition are corroded 7 times slower in an aerated Na(2)SO(4) solution as compared to the corrosion rate of bare copper. Tafel analysis reveals that nickel with a multilayer graphene film grown on it corrodes 20 times slower while nickel surfaces coated with four layers of mechanically transferred graphene corrode 4 times slower than bare nickel. These findings establish graphene as the thinnest known corrosion-protecting coating.

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/nn203507y

Cites

1 of 1 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 1 references

Cited by

See all 1,291

Metadata

Authors
Prasai, D., Tuberquia, J. C., Harl, R. R., Jennings, G. K., & Bolotin, K. I.
Venue
ACS Nano
Published
2012-02-10
Volume
6
Issue
2
Pages
1102-1108
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2052746811
Cited by
1,291
References
1
FWCI
34.61
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: Graphene: Corrosion-Inhibiting Coating. https://app.cochat.ai/papers/10.1021/nn203507y
The paper
Prasai, D., Tuberquia, J. C., Harl, R. R., Jennings, G. K., & Bolotin, K. I. (2012). Graphene: Corrosion-Inhibiting Coating. ACS Nano. https://doi.org/10.1021/nn203507y

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.