In Situ Monitoring of the Mechanosynthesis of the Archetypal Metal–Organic Framework HKUST-1: Effect of Liquid Additives on the Milling Reactivity
Bibliographic record
Abstract
We have applied in situ monitoring of mechanochemical reactions by high-energy synchrotron powder X-ray diffraction to study the role of liquid additives on the mechanochemical synthesis of the archetypal metal-organic framework (MOF) HKUST-1, which was one of the first and is still among the most widely investigated MOF materials to be synthesized by solvent-free procedures. It is shown here how the kinetics and mechanisms of the mechanochemical synthesis of HKUST-1 can be influenced by milling conditions and additives, yielding on occasion two new and previously undetected intermediate phases containing a mononuclear copper core, and that finally rearrange to form the HKUST-1 architecture. On the basis of in situ data, we were able to tune and direct the milling reactions toward the formation of these intermediates, which were isolated and characterized by spectroscopic and structural means and their magnetic properties compared to those of HKUST-1. The results have shown that despite the relatively large breadth of analysis available for such widely investigated materials as HKUST-1, in situ monitoring of milling reactions can help in the detection and isolation of new materials and to establish efficient reaction conditions for the mechanochemical synthesis of porous MOFs.
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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".