The DOI matches its Crossref record.
Covalent Organic Framework–Covalent Organic Framework Bilayer Membranes for Highly Selective Gas Separation
Abstract
Covalent organic frameworks (COFs) have been proposed as alternative candidates for molecular sieving membranes due to their chemical stability. However, developing COF membranes with narrowed apertures close to the size of common gas molecules is a crucial task for selective gas separation. Herein, we demonstrate a new type of a two-dimensional layered-stacking COF–COF composite membrane in bilayer geometry synthesized on a porous support by successively regulating the growth of imine-based COF-LZU1 and azine-based ACOF-1 layers via a temperature-swing solvothermal approach. The resultant COF-LZU1–ACOF-1 bilayer membrane has much higher separation selectivity for H 2 /CO 2, H 2 /N 2, and H 2 /CH 4 gas mixtures than the individual COF-LZU1 and ACOF-1 membranes due to the formation of interlaced pore networks, and the overall performance surpasses the Robeson upper bounds. The COF-LZU1–ACOF-1 bilayer membrane also shows high thermal and long-time stabilities.
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
Liu, R., Tan, K. T., Gong, Y., Chen, Y., Li, Z., Xie, S., He, T., Lu, Z., Yang, H., & Jiang, D. (2021) · Chemical Society Reviews · doi:10.1039/d0cs00620c · Cited by 1,083
Wang, Y., Peh, S. B., & Zhao, D. (2019) · Small · doi:10.1002/smll.201900058 · Cited by 342
Metadata
- Authors
- Fan, H., Mundstock, A., Feldhoff, A., Knebel, A., Gu, J., Meng, H., & Caro, J.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-07-18
- Volume
- 140
- Issue
- 32
- Pages
- 10094-10098
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2883769015
- Cited by
- 758
- References
- 0
- FWCI
- 20.60
- 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: Covalent Organic Framework–Covalent Organic Framework Bilayer Membranes for Highly Selective Gas Separation. https://app.cochat.ai/papers/10.1021/jacs.8b05136
- The paper
- Fan, H., Mundstock, A., Feldhoff, A., Knebel, A., Gu, J., Meng, H., & Caro, J. (2018). Covalent Organic Framework–Covalent Organic Framework Bilayer Membranes for Highly Selective Gas Separation. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.8b05136
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.