Measurement of palladium crust thickness on catalyst by EPMA
Bibliographic record
Abstract
Selective hydrogenation is a key process in petrochemistry to obtain good feedstock for polymers synthesis. Common catalysts for this process consist in metallic palladium deposited with an eggshell distribution on porous alumina. For this system, the catalytic activity is known to be in strong relation with the thickness of the palladium crust. Typical catalyst consists of 2 - 4 mm diameter spherical beads having a 200 - 400 μm thick palladium crust and a total palladium amount of about 0.3 to 0.5 wt%. The palladium distribution in the catalyst bead can be easily characterized by electron probe microanalysis (EPMA) using polished cross-sections of the beads trough their diameter. By measuring the local concentration of palladium on several points along the bead diameter we obtain the distribution profile of palladium in the bead. Two strategies are proposed to measure this palladium crust thickness by EPMA. First the crust thickness is defined by the distance to the catalyst bead surface containing a fixed amount of total palladium (for example 95 % or 98 %). Second, the palladium profile is modelled by a parameterized analytical function from which a crust thickness can be extracted. Catalytic tests on four samples having different palladium crust thicknesses confirm the strong relation between activity and crust thickness. However the crust thickness containing 98 % of the palladium content shows the best correlation with activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".