Organocatalysis with carbon nitrides
Bibliographic record
Abstract
Carbon nitrides, a distinguished class of metal-free catalytic materials, have presented a good potential for chemical transformations and are expected to become prominent materials for organocatalysis. This is largely possible due to their low cost, exceptional thermal and chemical stability, non-toxicity, ease of functionalization, porosity development, etc. Especially, the carbon nitrides with increased porosity and nitrogen contents are more versatile than their bulk counterparts for catalysis. These N-rich carbon nitrides are discussed in the earlier parts of the review. Later, the review highlights the role of such carbon nitride materials for the various organic catalytic reactions including Knoevenagel condensation, oxidation, hydrogenation, esterification, transesterification, cycloaddition, and hydrolysis. The recently emerging concepts in carbon nitride-based organocatalysis have been given special attention. In each of the sections, the structure-property relationship of the materials was discussed and related to their catalysis action. Relevant comparisons with other catalytic materials are also discussed to realize their real potential value. The perspective, challenges, and future directions are also discussed. The overall objective of this review is to provide up-to-date information on new developments in carbon nitride-based organic catalysis reactions that could see them rising as prominent catalytic materials in the future.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".