Achieving Regioselective Control for Mechanochemical Reactions: A Planetary Ball‐Milling, Ru‐Catalyzed Synthesis of 3,4‐ and 3,4,5‐Isoxazoles
Bibliographic record
Abstract
Abstract A mechanochemical‐enabled Ru‐catalyzed regioselective synthesis of 3,4‐isoxazoles and 3,4,5‐isoxazoles from terminal and internal alkynes and hydroxyimidoyl chlorides is reported. This solid‐state and solvent‐free approach carefully examines the impact of the milling conditions on regiocontrol in 1,3‐dipolar cycloadditions using mechanochemical means. The study reveals that milling frequency, jar material, and the choice of liquid additive for liquid‐assisted grinding (LAG) significantly influence the catalytic activity of the Ru catalyst. Transmission electron microscope (TEM) analysis confirms the crucial role of coordinating liquid additives such as acetone and cyclopentyl methyl ether (CPME) in stabilizing and reducing the size of the in‐situ formed Ru nanoparticles, which is essential for catalytic activity. The applicability of this protocol is further demonstrated through the synthesis of a library of 3,4‐ and 3,4,5‐isoxazoles from a wide range of terminal and internal alkynes with varying physical states and electronic properties that highlights the potential of this method for the synthesis of more complex target molecules.
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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".