Greening up organic reactions with caffeine: applications, recent developments, and future directions
Bibliographic record
Abstract
Numerous studies in the field of alkaloid chemistry have provided researchers with valuable insights into their unique properties as catalysts. Among the diverse natural catalysts, caffeine has emerged as a green, expedient, and biodegradable catalyst with high efficiency and applicability. Interest in using caffeine as a catalyst has burgeoned over the past few years with its role in diverse multicomponent reactions. Preparation of its imidazolium salts and further conversion to Nitrogen Heterocyclic Carbene (NHC) ligands and ionic liquids offers new paradigms. Caffeine has also played a multifaceted role as a support material in influencing the structural properties of nanoparticles. We hope that the chemistry of caffeine and its applications for sustainable organic transformations discussed in this review will stimulate new thinking and open new avenues in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".