Synthesis of Ru‐silica aerogel as efficient catalyst for hydrogenation of p‐phenylenediamine to 1,4‐cyclohexanediamine
Bibliographic record
Abstract
Tetraethylorthosilicate (TEOS) and methyl trimethoxysilane (MTMS) were used as raw materials to prepare an Ru‐silica aerogel catalyst by sol‐gel and the supercritical drying method. The catalyst was characterized by various techniques including BET, TEM, FTIR, SEM, XPS, XRD, TG, and EDS. It was used for the hydrogenation of p‐phenylenediamine (PDA) to 1,4‐cyclohexanediamine (CHDA). The effect of various parameters, such as loading methods, catalyst components, catalyst amount, and the reaction time were systematically investigated. It was found by XPS that supercritical ethanol can convert Ru3+ to Ru0 by its strong reducing property. The catalyst prepared by sol‐gel and the supercritical drying method is homogeneous, with high loading rates and a high specific surface area above 700 m2/g. The reactivity of the catalyst prepared by the sol‐gel method is much higher than that prepared by the impregnation method. The PDA conversion of 99 % alone with the CHDA selectivity of 64 % were achieved under the reaction temperature of 150 °C and the pressure of 5 Mpa for 1 h. The PDA conversion decreased as the MTMS ratio increased in the catalyst. The anti‐cis ratio of CHDA reached the maximum value (2.25) when the ratio of MTMS/ TEOS was 2/3.
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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".