The DOI matches its Crossref record.
An Efficient Nanoscale Heterogeneous Catalyst for the Capture and Conversion of Carbon Dioxide at Ambient Pressure
Abstract
Silver nanoparticles were successfully supported on the zeolite‐type metal–organic framework MIL‐101 to yield Ag@MIL‐101 by a simple liquid impregnation method. For the first time, the conversion of terminal alkynes into propiolic acids with CO 2 was achieved by the use of the Ag@MIL‐101 catalysts. Owing to the excellent catalytic activity, the reaction proceeded at atmospheric pressure and low temperature (50 °C). The Ag@MIL‐101 porous material is of outstanding bifunctional character as it is capable of simultaneously capturing and converting CO 2 with low energy consumption and can be recovered easily by centrifugation.
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
Cited by
Carbon capture and conversion using metal–organic frameworks and MOF-based materials
Ding, M., Flaig, R. W., Jiang, H.-L., & Yaghi, O. M. (2019) · Chemical Society Reviews · doi:10.1039/c8cs00829a · Cited by 2,616
Metal–organic frameworks meet metal nanoparticles: synergistic effect for enhanced catalysis
Yang, Q., Xu, Q., & Jiang, H.-L. (2017) · Chemical Society Reviews · doi:10.1039/c6cs00724d · Cited by 1,892
Highly Selective Conversion of Carbon Dioxide to Lower Olefins
Li, Z., Wang, J., Qu, Y., Liu, H., Tang, C., Miao, S., Feng, Z., An, H., & Li, C. (2017) · ACS Catalysis · doi:10.1021/acscatal.7b03251 · Cited by 593
Liang, J., Huang, Y.-B., & Cao, R. (2019) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2017.11.013 · Cited by 444
Cui, W.-G., Zhang, G.-Y., Hu, T.-L., & Bu, X.-H. (2019) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2019.02.001 · Cited by 406
Metadata
- Authors
- Liu, X., Ma, J., Niu, Z., Yang, G., & Cheng, P.
- Venue
- Angewandte Chemie
- Publisher
- Wiley
- Published
- 2014-11-10
- Volume
- 127
- Issue
- 3
- Pages
- 1002-1005
- ISSN
- 0044-8249 (ISSN-L)
- 1521-3757
- OpenAlex
- W4234402712
- Cited by
- 250
- References
- 0
- FWCI
- 10.56
- Type
- journal article
- Language
- de
- 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: An Efficient Nanoscale Heterogeneous Catalyst for the Capture and Conversion of Carbon Dioxide at Ambient Pressure. https://app.cochat.ai/papers/10.1002/ange.201409103
- The paper
- Liu, X., Ma, J., Niu, Z., Yang, G., & Cheng, P. (2015). An Efficient Nanoscale Heterogeneous Catalyst for the Capture and Conversion of Carbon Dioxide at Ambient Pressure. Angewandte Chemie. https://doi.org/10.1002/ange.201409103
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.
