Click Mechanochemistry: Quantitative Synthesis of “Ready to Use” Chiral Organocatalysts by Efficient Two‐Fold Thiourea Coupling to Vicinal Diamines
Bibliographic record
Abstract
Mechanochemical methods of neat grinding and liquid-assisted grinding have been applied to the synthesis of mono- and bis(thiourea)s by using the click coupling of aromatic and aliphatic diamines with aromatic isothiocyanates. The ability to modify the reaction conditions allowed the optimization of each reaction, leading to the quantitative formation of chiral bis(thiourea)s with known uses as organocatalysts or anion sensors. Quantitative reaction yields, combined with the fact that mechanochemical reaction conditions avoid the use of bulk solvents, enabled solution-based purification methods (such as chromatography or recrystallization) to be completely avoided. Importantly, by using selected model reactions, we also show that the described mechanochemical reaction procedures can be readily scaled up to at least the one-gram scale. In that way, mechanochemical synthesis provides a facile method to fully transform valuable enantiomerically pure reagents into useful products that can immediately be applied in their designed purpose. This was demonstrated by using some of the mechanochemically prepared reagents as organocatalysts in a model Morita-Baylis-Hillman reaction and as cyanide ion sensors in organic solvents. The use of electronically and sterically hindered ortho-phenylenediamine revealed that mechanochemical reaction conditions can be readily optimized to form either the 1:1 or the 1:2 click-coupling product, demonstrating that reaction stoichiometry can be more efficiently controlled under these conditions than in solution-based syntheses. In this way, it was shown that excellent stoichiometric control by mechanochemistry, previously established for mechanochemical syntheses of cocrystals and coordination polymers, can also be achieved in the context of covalent-bond formation.
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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) | 0.001 | 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 | 0.001 |
| 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".