ArticleACS materials letters2025
The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning.
Article in ACS materials letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Covalent Organic Frameworks in Smart Food Packaging: Advances, Challenges, and Future Prospects.Comprehensive reviews in food science and food safety · 2026Review
- Bioinspired pore engineering in two-dimensional covalent organic frameworks.Chemical science · 2026Article
- Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research.JACS Au · 2026Review
- ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning.Industrial & engineering chemistry research · 2026Article
- Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions.Chemical reviews · 2025Review
- Unlocking the Comparative Potential of Porous Frameworks: A Review on MOFs and COFs for Gas Sorption.Topics in current chemistry (Cham) · 2025Review
- Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CHThe journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covalent organic frameworks (COFs) are promising materials for gas adsorption; however, only a small number of COFs has been studied for a few types of gas separations to date. To unlock the full potential of the COF space, composed of 69 784 different types of materials, we studied the adsorption of five important gas molecules, CO
Identifiers
What OpenQuestion holds
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.