Selective hydrogenation of cinnamaldehyde using Pd catalysts supported on Mg/Al mixed oxides: Influence of the Pd incorporation method
Bibliographic record
Abstract
A series of hydrotalcite‐like compounds was synthesized by varying Mg/Al molar ratio with values of 2, 3, and 4. After thermal treatment at 823 K, the corresponding mixed oxides were obtained and used as catalytic supports. The incorporation of a Pd metallic phase (0.5 g/g loading), was carried out by two methods: 1) in situ vapour phase thermal decomposition, and 2) impregnation by organic method. Fresh and calcined samples were characterized by XRD and N2 sorption experiments. The basic and metal functions were analyzed by CO2‐TPD and H2‐TPR. The Pd‐support interaction was studied by FTIR spectroscopy using CO as a probe molecule while the morphology of Pd nanoparticles on the catalysts was studied by SEM, HRTEM, and theoretical simulation using the Fast Fourier Transform (FFT) method. Finally, the catalytic activity results showed a higher conversion towards hydrocinnamaldehyde in the cinnamaldehyde hydrogenation reaction for the catalysts prepared by vapour phase thermal decomposition, compared with those prepared by organic method, showing the significant dependence on the catalytic activity and the Pd incorporation method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".