ReviewSmall (Weinheim an der Bergstrasse, Germany)2026
Gas Separation With COF Membranes: Crystalline Design Meets Selective Transport.
Review in Small (Weinheim an der Bergstrasse, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
Covalent organic frameworks (COFs) are emerging crystalline porous materials with precisely defined architectures and tunable chemistry, offering strong potential for next-generation gas separation membranes. This review provides a material-centric perspective on COF-based membranes, emphasizing how framework design governs molecular transport behavior. We discuss key structure-property relationships in COF synthesis, highlighting the roles of crystallinity and chemical functionality in regulating selective transport. Recent advances in synthetic methods are compared in terms of framework order, pore alignment, and scalability. The evolution of COF membrane fabrication is then reviewed, from mixed-matrix systems to in situ grown and freestanding membranes, with attention to interfacial engineering, mechanical stability, and orientation control. Fundamental transport mechanisms are summarized to connect pore chemistry with separation performance. Application-focused progress in CO
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.