The DOI matches its Crossref record.
Molecular simulation of separation of CO2 from flue gases in CU‐BTC metal‐organic framework
Abstract
In this work, a computational study was performed on the adsorption separation of CO 2 from flue gases (mixtures of CO 2/N 2/O 2) in Cu‐BTC metal‐organic framework (MOF) to investigate the applicability of MOFs to this important industrial system. The computational results showed that Cu‐BTC is a promising material for separation of CO 2 from flue gases, and the macroscopic separation behaviors of the MOF were elucidated at a molecular level to give insight into the underlying mechanisms. The present work not only provided useful information for understanding the separation characteristics of MOFs, but also showed their potential applications in chemical industry. © 2007 American Institute of Chemical Engineers AIChE J, 2007
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
Metal–Organic Frameworks for Separations
Li, J.-R., Sculley, J., & Zhou, H.-C. (2012) · Chemical Reviews · doi:10.1021/cr200190s · Cited by 6,323
Carbon Dioxide Capture: Prospects for New Materials
D'Alessandro, D. M., Smit, B., & Long, J. R. (2010) · Angewandte Chemie International Edition · doi:10.1002/anie.201000431 · Cited by 4,046
Al-Janabi, N., Hill, P., Torrente-Murciano, L., Garforth, A., Gorgojo, P., Siperstein, F., & Fan, X. (2015) · Chemical Engineering Journal · doi:10.1016/j.cej.2015.07.020 · Cited by 386
Sorbents for CO2 capture from flue gas—aspects from materials and theoretical chemistry
Hedin, N., Chen, L., & Laaksonen, A. (2010) · Nanoscale · doi:10.1039/c0nr00042f · Cited by 243
Metadata
- Authors
- Yang, Q., Xue, C., Zhong, C., & Chen, J.
- Venue
- AIChE Journal
- Publisher
- Wiley
- Published
- 2007-09-28
- Volume
- 53
- Issue
- 11
- Pages
- 2832-2840
- ISSN
- 0001-1541 (ISSN-L)
- 1547-5905
- OpenAlex
- W2171608545
- Cited by
- 259
- References
- 0
- FWCI
- 14.42
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Molecular simulation of separation of CO2 from flue gases in CU‐BTC metal‐organic framework. https://app.cochat.ai/papers/10.1002/aic.11298
- The paper
- Yang, Q., Xue, C., Zhong, C., & Chen, J. (2007). Molecular simulation of separation of CO2 from flue gases in CU‐BTC metal‐organic framework. AIChE Journal. https://doi.org/10.1002/aic.11298
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.
