Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 601

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

High-Density Ultra-small Clusters and Single-Atom Fe Sites Embedded in Graphitic Carbon Nitride (g-C3N4) for Highly Efficient Catalytic Advanced Oxidation Processes

Open access
An, Sufeng, Zhang, Guanghui, Wang, Tingwen, Zhang, Wenna, Li, Keyan, Song, Chunshan, Miller, Jeffrey T., Miao, Shu, Wang, Junhu, Guo, Xinwen·2018·ACS Nano

Abstract

Ultra-small metal clusters have attracted great attention owing to their superior catalytic performance and extensive application in heterogeneous catalysis. However, the synthesis of high-density metal clusters is very challenging due to their facile aggregation. Herein, one-step pyrolysis was used to synthesize ultra-small clusters and single-atom Fe sites embedded in graphitic carbon nitride with high density (iron loading up to 18.2 wt %), evidenced by high-angle annular dark field-scanning transmission electron microscopy, X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and 57 Fe Mössbauer spectroscopy. The catalysts exhibit enhanced activity and stability in degrading various organic samples in advanced oxidation processes. The drastically increased metal site density and stability provide useful insights into the design and synthesis of cluster catalysts for practical application in catalytic oxidation reactions.

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/acsnano.8b04693

Cites

57 of 57 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

Cited by

See all 601

Metadata

Authors
An, S., Zhang, G., Wang, T., Zhang, W., Li, K., Song, C., Miller, J. T., Miao, S., Wang, J., & Guo, X.
Venue
ACS Nano
Published
2018-09-05
Volume
12
Issue
9
Pages
9441-9450
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2890606519
Cited by
601
References
57
FWCI
21.43
Type
journal article
Language
en
Open access
green

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.