Covalent Organic Frameworks via In Situ Monomer Release for Humid CO <sub>2</sub> Uptake
Bibliographic record
Abstract
Achieving high crystallinity and porosity in covalent organic frameworks (COFs) remains a major challenge, particularly for robust azine-linked systems, where limited bond reversibility hinders framework ordering. While several strategies have improved the crystallinity in imine-linked COFs, analogous advances in azine-linked frameworks are still lacking. Here, we present a kinetic modulation strategy based on the in situ slow release of hydrazine via the trifluoroacetic acid (TFA)-mediated hydrolysis of a tetrazine precursor during the COF synthesis. Using this approach, we successfully synthesized two crystalline azine-linked COFs incorporating either a nonplanar bicarbazole or a planar pyrene core. This method enhances structural order, as evidenced by narrowed full width at half-maximum (FWHM) values in PXRD and higher surface areas compared to the COFs prepared with direct hydrazine hydrate addition. Time-resolved UV–vis and PXRD analyses elucidate the role of reaction kinetics in framework evolution. Notably, the resulting COFs exhibit S-shaped water vapor adsorption isotherms with minimal uptake below 40% relative humidity (RH), reflecting their hydrophobic backbones. More importantly, CO 2 uptake remains largely unaffected at 40% RH, and dynamic breakthrough experiments confirm that H 2 O does not interfere with CO 2 adsorption under these conditions. This work establishes a generalizable route for crystallinity enhancement in kinetically trapped COFs and offers promising materials for gas separation under humid environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".