Selective hydrogenation of 3,4‐dimethoxybenzophenone in liquid phase over Pd/C catalyst in a slurry reactor
Bibliographic record
Abstract
Abstract Selective hydrogenation of benzophenone to benzhydrol is industrially relevant. In the current work, selective hydrogenation of 3,4‐dimethoxybenzophenone (3,4‐DMBP) to 3,4‐dimethoxybenzhydrol (3,4‐DMBH) was investigated over 5 % w/w Pd/C and 5 % w/w Pt/C as catalysts in a slurry reactor. The effects of hydrogen partial pressure (0.2–1 MPa), catalyst loading (2.0 × 10 −4 to 8.0 × 10 −4 g/cm 3 ), initial concentration of 3,4‐dimethoxybenzophenone (5.0 × 10 −6 to 1.5 × 10 −4 mol/cm 3 ) and temperature (40–70 °C) on rate of reaction and selectivity were studied. Effect of solvent such as methanol, ethanol, 2‐propanol and tetrahydrofuran (THF) was also investigated. 5 % w/w Pd/C was the better catalyst with THF as the best solvent, which gave 100 % conversion and 80 % selectivity in 1 h for 0.5 MPa hydrogen pressure at 60 °C. The Langmuir‐Hinshelwood‐Hougen‐Watson model was fitted. The intrinsic kinetics and mechanism of hydrogenation were established.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".