In Situ Monitoring of Mechanochemical Covalent Organic Framework Formation Reveals Templating Effect of Liquid Additive
Bibliographic record
Abstract
Covalent organic frameworks (COFs) have emerged as a new class of molecularly precise, porous functional materials characterized by a broad structural and chemical versatility, leading to a diverse range of applications. Despite the increasing popularity of COFs, fundamental aspects of their formation are poorly understood and profound experimental insights into their formation processes are still lacking. Here we use a combination of in situ X-ray powder diffraction and Raman spectroscopy to elucidate the reaction mechanism of mechanochemically synthesized imine COFs, leading to the observation of key reaction intermediates. Real-time monitoring provides experimental evidence of templating effects by the liquid additive for the subsequent pore formation and layer assembly. Moreover, the solid-state catalyst scandium triflate Sc(OTf) 3 is revealed to be instrumental in directing the reaction kinetics and mechanism, resulting in products with crystallinity and porosity en par with solvothermally synthesized COFs. This work highlights the potential of mechanochemistry as a green synthetic route towards COF synthesis, and emphasizes the subtle interplay between choice of liquid additives, catalysts, and activation procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".