Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 298

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

Binary Au–Cu Reaction Sites Decorated ZnO for Selective Methane Oxidation to C1 Oxygenates with Nearly 100% Selectivity at Room Temperature

Open access
Luo, Lei, Gong, Zhuyu, Xu, Youxun, Ma, Jiani, Liu, Huifen, Xing, Jialiang, Tang, Junwang·2022·Journal of the American Chemical Society

Abstract

Direct and efficient oxidation of methane to methanol and the related liquid oxygenates provides a promising pathway for sustainable chemical industry, while still remaining an ongoing challenge owing to the dilemma between methane activation and overoxidation. Here, ZnO with highly dispersed dual Au and Cu species as cocatalysts enables efficient and selective photocatalytic conversion of methane to methanol and one-carbon (C1) oxygenates using O 2 as the oxidant operated at ambient temperature. The optimized AuCu–ZnO photocatalyst achieves up to 11225 μmol·g –1 ·h –1 of primary products (CH 3 OH and CH 3 OOH) and HCHO with a nearly 100% selectivity, resulting in a 14.1% apparent quantum yield at 365 nm, much higher than the previous best photocatalysts reported for methane conversion to oxygenates. In situ EPR and XPS disclose that Cu species serve as photoinduced electron mediators to promote O 2 activation to • OOH, and simultaneously that Au is an efficient hole acceptor to enhance H 2 O oxidation to • OH, thus synergistically promoting charge separation and methane transformation. This work highlights the significances of co-modification with suitable dual cocatalysts on simultaneous regulation of activity and selectivity.

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/jacs.1c09141

Cites

42 of 42 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 298

Metadata

Authors
Luo, L., Gong, Z., Xu, Y., Ma, J., Liu, H., Xing, J., & Tang, J.
Venue
Journal of the American Chemical Society
Published
2021-12-20
Volume
144
Issue
2
Pages
740-750
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W4200383387
Cited by
298
References
42
FWCI
7.03
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.