Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 545

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Graphene-based electrochemical energy conversion and storage: fuel cells, supercapacitors and lithium ion batteries

Hou, Junbo, Shao, Yuyan, Ellis, Michael W., Moore, Robert B., Yi, Baolian·2011·Physical Chemistry Chemical Physics

Abstract

Graphene has attracted extensive research interest due to its strictly 2-dimensional (2D) structure, which results in its unique electronic, thermal, mechanical, and chemical properties and potential technical applications. These remarkable characteristics of graphene, along with the inherent benefits of a carbon material, make it a promising candidate for application in electrochemical energy devices. This article reviews the methods of graphene preparation, introduces the unique electrochemical behavior of graphene, and summarizes the recent research and development on graphene-based fuel cells, supercapacitors and lithium ion batteries. In addition, promising areas are identified for the future development of graphene-based materials in electrochemical energy conversion and storage systems.

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/c1cp21915d

Cites

7 of 7 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 7 references

Cited by

See all 545

Metadata

Authors
Hou, J., Shao, Y., Ellis, M. W., Moore, R. B., & Yi, B.
Venue
Physical Chemistry Chemical Physics
Published
2011
Volume
13
Issue
34
Pages
15384
ISSN
  • 1463-9076 (ISSN-L)
  • 1463-9084
OpenAlex
W2171587338
Cited by
545
References
7
FWCI
20.91
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-based electrochemical energy conversion and storage: fuel cells, supercapacitors and lithium ion batteries. https://app.cochat.ai/papers/10.1039/c1cp21915d
The paper
Hou, J., Shao, Y., Ellis, M. W., Moore, R. B., & Yi, B. (2011). Graphene-based electrochemical energy conversion and storage: fuel cells, supercapacitors and lithium ion batteries. Physical Chemistry Chemical Physics. https://doi.org/10.1039/c1cp21915d

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.