The DOI matches its Crossref record.
Microporous Organic Materials for Membrane‐Based Gas Separation
Abstract
Membrane materials with excellent selectivity and high permeability are crucial to efficient membrane gas separation. Microporous organic materials have evolved as an alternative candidate for fabricating membranes due to their inherent attributes, such as permanent porosity, high surface area, and good processability. Herein, a unique pore‐chemistry concept for the designed synthesis of microporous organic membranes, with an emphasis on the relationship between pore structures and membrane performances, is introduced. The latest advances in microporous organic materials for potential membrane application in gas separation of H 2, CO 2, O 2, and other industrially relevant gases are summarized. Representative examples of the recent progress in highly selective and permeable membranes are highlighted with some fundamental analyses from pore characteristics, followed by a brief perspective on future research directions.
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
Fan, H., Mundstock, A., Feldhoff, A., Knebel, A., Gu, J., Meng, H., & Caro, J. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b05136 · Cited by 758
Microporous framework membranes for precise molecule/ion separations
Dou, H., Xu, M., Wang, B., Zhang, Z., Wen, G., Zheng, Y., Luo, D., Zhao, L., Yu, A., Zhang, L., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs00552e · Cited by 371
CTF-based soft touch actuator for playing electronic piano
Mahato, M., Tabassian, R., Nguyen, V. H., Oh, S., Nam, S., Hwang, W.-J., & Oh, I.-K. (2020) · Nature Communications · doi:10.1038/s41467-020-19180-3 · Cited by 94
Metadata
- Authors
- Zou, X., & Zhu, G.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2017-10-24
- Volume
- 30
- Issue
- 3
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W2767202177
- Cited by
- 248
- References
- 0
- FWCI
- 20.68
- 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: Microporous Organic Materials for Membrane‐Based Gas Separation. https://app.cochat.ai/papers/10.1002/adma.201700750
- The paper
- Zou, X., & Zhu, G. (2018). Microporous Organic Materials for Membrane‐Based Gas Separation. Advanced Materials. https://doi.org/10.1002/adma.201700750
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.
