Study on liquid‐phase hydrogenation of paranitrotoluene over Ru‐based catalysts
Bibliographic record
Abstract
Abstract This paper presented a study on the role of yttrium addition to Ru‐based catalysts for liquid phase paranitrotoluene hydrogenation reaction. An impregnation‐precipitation method was used for preparation of a series of yttrium doped Ru/NaY catalysts with yttrium content in the range of 0.0026–0.0052 g/g. Properties of the obtained samples were characterized and analyzed by X‐ray diffraction (XRD), H 2 ‐TPR, Transmission electron microscopy (TEM), ICP atomic emission spectroscopy, and Nitrogen adsorption‐desorption. The results revealed that catalytic activity of NaY supported Ru catalysts increased with the yttrium content at first, then decreased with the further increase of yttrium content. When yttrium content was 0.0033 g/g, a Ru‐Y/NaY 2 catalyst showed the most excellent performance of paranitrotoluene hydrogenation reaction (paranitrotoluene conversion and the selectivity toward P‐methyl‐cyclohexylamine reached 99.9 % and 82.5 %, respectively). In addition, to compare with the performance of Ru‐Y/NaY catalysts, the active carbon supported Ru catalysts were prepared using the same method in view of its higher surface area and adsorption capacity. Finally, the effect of solvent on the reaction over Ru‐Y/NaY 2 catalyst has been investigated, it was found that the best performance of paranitrotoluene hydrogenation reaction took place in protic solvents (isopropanol and ethanol). This was mainly ascribed to their polarity and hydrogen‐bond accepting capability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".