Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 418

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Graphene Supported Co-g-C3N4 as a Novel Metal–Macrocyclic Electrocatalyst for the Oxygen Reduction Reaction in Fuel Cells

Liu, Qiao, Zhang, Junyan·2013·Langmuir

Abstract

Graphitic carbon nitride (g-C3N4) polymer was doped with cobalt species and supported on a similar sp(2) structure graphene, to form a novel nitrogen-metal macrocyclic catalyst for the oxygen reduction reaction (ORR) in alkaline fuel cells. The structural characterizations confirmed the formation of Co-N bonds and the close electron coupling between Co-g-C3N4 and graphene sheets. The electrocatalytic measurements demonstrated Co-g-C3N4-catalyzed reduction of oxygen mainly in a four electron pathway. The improvement of ORR activity is closely related to the abundant accessible Co-Nx active sites and fast charge transfer at the interfaces of Co-g-C3N4/graphene. Also, Co-g-C3N4@graphene exhibited comparable ORR activity, better durability, and methanol tolerance ability in comparison to Pt/C, and bodes well for a promising non-noble cathode catalyst for the application of direct methanol fuel cells. The chemical doping strategy in this work would be helpful to improve other present catalysts for fuel cell applications.

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/la400003h

Cites

2 of 2 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 2 references

Cited by

See all 418

Metadata

Authors
Liu, Q., & Zhang, J.
Venue
Langmuir
Published
2013-03-08
Volume
29
Issue
11
Pages
3821-3828
ISSN
  • 0743-7463 (ISSN-L)
  • 1520-5827
OpenAlex
W2328025663
Cited by
418
References
2
FWCI
15.87
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 Supported Co-g-C3N4 as a Novel Metal–Macrocyclic Electrocatalyst for the Oxygen Reduction Reaction in Fuel Cells. https://app.cochat.ai/papers/10.1021/la400003h
The paper
Liu, Q., & Zhang, J. (2013). Graphene Supported Co-g-C3N4 as a Novel Metal–Macrocyclic Electrocatalyst for the Oxygen Reduction Reaction in Fuel Cells. Langmuir. https://doi.org/10.1021/la400003h

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.