Facile and green synthesis of a series of dihydro-[1,2,4]triazolo[1,5-<i>a</i>]pyrimidine scaffolds
Bibliographic record
Abstract
This work presents a new catalytic application of 4,4′-trimethylenedipiperidine for the efficient synthesis of a series of dihydro-[1,2,4] triazolo[1,5-a]pyrimidines. According to the principles of green chemistry, the reaction was performed (a) in a solvent mixture comprised of water and ethanol (1:1 v/v) at reflux temperature and (b) solvent-free grinding in a mortar by a pestle. The organocatalyst could be reused up to 10 runs, and no reduction of catalytic activity was detected. A variety of substituted dihydro-[1,2,4] triazolo[1,5-a]pyrimidines were obtained in good to excellent yields under eco-friendly conditions. 4,4′-Trimethylenedipiperidine is commercially available and easy to handle, and it also shows high thermal stability and good solubility in water. This work revealed that this organocatalyst, a hydrogen bond donor to active carbonyl groups and simultaneously a Lewis base through the nitrogen atom of second piperidine moiety, could play a vital role in the promotion of the one-pot multi-component reactions. The main merits of the current methodology include short reaction time, wide substrate scope, use of a metal-free catalyst and green solvents, and simple work-up process. Furthermore, this organocatalyst can be an alternative to piperidine for organic transformations.
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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".