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 × 10−4to 8.0 × 10−4 g/cm3), initial concentration of 3,4‐dimethoxybenzophenone (5.0 × 10−6to 1.5 × 10−4 mol/cm3) 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 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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".