The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Graphene-Based Non-Noble-Metal Catalysts for Oxygen Reduction Reaction in Acid
Abstract
Non-noble-metal catalysts based on Fe–N–C moieties have shown promising oxygen reduction reaction (ORR) activity in proton exchange membrane fuel cells (PEMFCs). In this study, we report a facile method to prepare a Fe–N–C catalyst based on modified graphene (Fe–N– r GO) from heat treatment of a mixture of Fe salt, graphitic carbon nitride ( g -C 3 N 4 ), and chemically reduced graphene ( r GO). The Fe–N– r GO catalyst was found to have pyridinic N-dominant heterocyclic N (40% atomic concentration among all N components) on the surface and have an average Fe coordination of ∼3 N (Fe–N 3,average ) in bulk. Rotating disk electrode measurements revealed that Fe–N– r GO had high mass activity in acid and exhibited high stability at 0.5 V at 80 °C in acid over 70 h, which was correlated to low H 2 O 2 production shown from rotating ring disk electrode measurements.
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.
Integrity signals
Registry
Cites
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.
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
Gasteiger, H. A., Kocha, S. S., Sompalli, B., & Wagner, F. T. (2005) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2004.06.021 · Cited by 4,876
Cited by
Design of electrocatalysts for oxygen- and hydrogen-involving energy conversion reactions
Jiao, Y., Zheng, Y., Jaroniec, M., & Qiao, S. Z. (2015) · Chemical Society Reviews · doi:10.1039/c4cs00470a · Cited by 5,388
Metal-Free Catalysts for Oxygen Reduction Reaction
Dai, L., Xue, Y., Qu, L., Choi, H.-J., & Baek, J.-B. (2015) · Chemical Reviews · doi:10.1021/cr5003563 · Cited by 2,380
Wu, G., Santandreu, A., Kellogg, W., Gupta, S., Ogoke, O., Zhang, H., Wang, H.-L., & Dai, L. (2016) · Nano Energy · doi:10.1016/j.nanoen.2015.12.032 · Cited by 744
Zhang, C., Sha, J., Fei, H., Liu, M., Yazdi, S., Zhang, J., Zhong, Q., Zou, X., Zhao, N., Yu, H., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b02148 · Cited by 545
Higgins, D., Zamani, P., Yu, A., & Chen, Z. (2016) · Energy & Environmental Science · doi:10.1039/c5ee02474a · Cited by 525
Metadata
- Authors
- Byon, H. R., Suntivich, J., & Shao-Horn, Y.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-07-18
- Volume
- 23
- Issue
- 15
- Pages
- 3421-3428
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2086917209
- Cited by
- 453
- References
- 1
- FWCI
- 18.26
- 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
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.
