ReviewACS nano2025
Toward Continuous, Oriented Covalent Organic Framework Membranes for Precise Molecular Separations.
Review in ACS nano, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Supramolecular Allostery Achieves Tunable Luminescence Aromatic Bridged Phenothiazine Noncovalent Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interfacial Entropy Drives Crystallization of Covalent Framework Membranes for Precise Ionic Separation.Angewandte Chemie (International ed. in English) · 2026Article
- Covalent Organic Frameworks for CONanomaterials (Basel, Switzerland) · 2026Review
- Ultrathin Covalent Organic Framework Membranes with Tailorable Porous Channels for High-Permeance Helium Separation.Journal of the American Chemical Society · 2026Article
- Highly efficient light-gated COF membrane for precise multistage molecular separation.Science advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The goal of achieving energy-efficient, precise molecular separations has motivated interest in developing and employing porous crystalline frameworks as membrane materials. Covalent organic frameworks (COFs) are ordered crystalline matrices composed of covalently bonded organic monomers and are synthesized via reversible reticular chemistry. COFs possess high porosity, structural tunability, and chemical and thermal stability, making them ideally suited for emerging, high-value membrane separation processes, such as ion separations, organic solvent nanofiltration, and gas separations. Although a range of COF membranes have been fabricated and tested in the past decade, these membranes are primarily polycrystalline, weakly crystalline, and/or discontinuous, resulting in suboptimal performance. In this review, we identify the properties that make COFs well-suited as membrane materials, while critically outlining the shortcomings of existing disordered COF membranes. We then highlight the recent emergence of highly crystalline, continuous, oriented two-dimensional COF membranes as a promising path forward for highly selective molecular separations. These continuous, oriented COF membranes exhibit tunable one-dimensional nanochannels, allowing for ultrafast molecular transport and precise species selectivity, thereby expanding the set of separations that can be practically achieved with membrane systems. We discuss synthesis and modification techniques that result in continuous, oriented COF membranes and evaluate the performance of such membranes for a variety of molecular separations. We conclude by identifying ongoing challenges in the development of COF membranes and outlining the future of their applications in molecular separations, which will necessarily rely on advancements in the synthesis of continuous, oriented membranes.
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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.