Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 729

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

MnO2-Based Nanostructures as Catalysts for Electrochemical Oxygen Reduction in Alkaline Media

Cheng, Fangyi, Su, Yi, Liang, Jing, Tao, Zhanliang, Chen, Jun·2010·Chemistry of Materials

Abstract

This paper reports a systematical study on the electrochemical properties of MnO 2 -based nanostructures as low-cost catalysts for oxygen reduction reaction (ORR) in alkaline media. The results show that the catalytic activities of MnO 2 depend strongly on the crystallographic structures, following an order of α- > β- > γ-MnO 2 . Meanwhile, morphology is another important influential factor to the electrochemical properties. Among various micro and nanostructures, α-MnO 2 nanospheres and nanowires outperform the counterpart microparticles. Furthermore, a new nanocomposite catalyst by depositing Ni nanoparticles on α-MnO 2 nanowires (denoted as MnO 2 -NWs@Ni-NPs) was prepared and characterized. The as-prepared MnO 2 -NWs@Ni-NPs nanocomposite exhibits an onset potential of 0.08 V, a specific current of 33.5 mA/mg, and an overall quasi 4-electron transfer involved in oxygen reduction reaction, indicating its potential application as the electrocatalyst of oxygen reduction reaction.

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/cm901698s

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 729

Metadata

Authors
Cheng, F., Su, Y., Liang, J., Tao, Z., & Chen, J.
Venue
Chemistry of Materials
Published
2009-11-17
Volume
22
Issue
3
Pages
898-905
ISSN
  • 0897-4756 (ISSN-L)
  • 1520-5002
OpenAlex
W2018359639
Cited by
729
References
4
FWCI
10.91
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: MnO2-Based Nanostructures as Catalysts for Electrochemical Oxygen Reduction in Alkaline Media. https://app.cochat.ai/papers/10.1021/cm901698s
The paper
Cheng, F., Su, Y., Liang, J., Tao, Z., & Chen, J. (2010). MnO2-Based Nanostructures as Catalysts for Electrochemical Oxygen Reduction in Alkaline Media. Chemistry of Materials. https://doi.org/10.1021/cm901698s

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.