Hydrogenation of citral over nitrogen‐doped carbon nanofibre‐supported nickel catalyst
Bibliographic record
Abstract
Abstract Herringbone carbon nanofibres (hCNFs) and nitrogen‐doped carbon nanofibres with 0.115 g/g (11.5 %) of N (N‐hCNFs) were used as the supports of nickel catalysts in the liquid phase hydrogenation of citral using toluene as a solvent. Over 96 % selectivity to citronellal was obtained at 150 °C for both catalysts. At the reaction temperature of 200 °C, reasonable amounts of isopulegol were formed additionally as a result of citronellal cyclization. The catalytic activity of Ni nanoparticles was significantly improved when supported on CNFs doped with nitrogen. The hydrogenation of cis‐citral was remarkably favoured over the trans‐citral isomer regardless of the reaction temperature. The hydrogenation of citronellal under the same process conditions was also studied. The sequential‐parallel reactions network for the hydrogenation of citral under the applied conditions was proposed and the kinetic constants were evaluated, assuming pseudo‐first order kinetics of the hydrogenation process.
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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".