Effect of method of preparation on activity of Pd/Al<sub>2</sub>O<sub>3</sub> monolith catalysts
Bibliographic record
Abstract
Abstract Pd/Al2O3 monolithic catalyst of different washcoat thicknesses were prepared by two methods and tested for the activity of hydrogenation of α‐methyl styrene. These catalysts were prepared by two methods; either the palladium was impregnated on γ‐alumina and this Pd/Al2O3 powder was used to prepare the slurry for washcoating (Cat 1) or γ‐alumina washcoating was followed by impregnation of palladium (Cat 2). The effect of slurry concentration, pH of the slurry, and addition of binders on the catalyst properties was investigated. The monolithic catalysts were characterised by determination of metal dispersion, surface area, scanning electron microscopy, and weight loss of washcoat during ultrasonication. Well‐adhered washcoats were obtained with slurry prepared using milled γ‐alumina, whereas the adhesion of the washcoat prepared using Pd/Al2O3 powders was very poor. Addition of binders significantly improved the adhesion of the washcoats prepared from Pd/Al2O3. Metal dispersion for Cat 2 decreased with washcoat loading but did not change with loading for Cat 1. The activity tests were conducted at different washcoat loadings and the productivity of the monolithic catalyst prepared in both methods has been compared.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".