Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 193

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

CO2 Hydrogenation to Methanol over Catalysts Derived from Single Cationic Layer CuZnGa LDH Precursors

Li, Molly M.-J., Chen, Chunping, Ayvalı, Tuğçe, Suo, Hongri, Zheng, Jianwei, Teixeira, Ivo F., Ye, Lin, Zou, Hanbo, O’Hare, Dermot, Tsang, Shik Chi Edman·2018·ACS Catalysis

Abstract

Ultrathin (1–3 cationic-layers) (CuZn) 1– x Ga x -CO 3 layered double hydroxide (LDH) nanosheets were synthesized following the aqueous miscible organic solvent treatment (AMOST) method and applied as catalyst precursors for methanol production from CO 2 hydrogenation. It is found that, upon reduction, the aqueous miscible organic solvent treated LDH (AMO-LDH) samples above a critical Ga 3+ composition give consistently and significantly higher Cu surface areas and dispersions than the catalysts prepared from conventional hydroxyl-carbonate phases. Owing to the distinctive local steric and electrostatic stabilization of the ultrathin LDH structure, the newly formed active Cu(Zn) metal atoms can be stably embedded in the cationic layers, exerting an enhancement to the catalytic reaction. The best catalyst in this study displayed methanol productivity with a space-time yield of 0.6 g MeOH· g cat –1 h –1 under typical reaction conditions, which, as far as we are aware, is higher than most reported Cu-based catalysts in the literature.

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/acscatal.8b00474

Cites

1 of 1 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 1 references

Cited by

Metadata

Authors
Li, M. M.-J., Chen, C., Ayvalı, T., Suo, H., Zheng, J., Teixeira, I. F., Ye, L., Zou, H., O’Hare, D., & Tsang, S. C. E.
Venue
ACS Catalysis
Published
2018-04-06
Volume
8
Issue
5
Pages
4390-4401
ISSN
2155-5435 (ISSN-L)
OpenAlex
W2796107058
Cited by
193
References
1
FWCI
3.74
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.