Hydrogenolysis of Glycerol over Cu/ZnO-Based Catalysts: Influence of Transport Phenomena Using the Madon–Boudart Criterion
Bibliographic record
Abstract
Batch hydrogenolysis of concentrated glycerol has been conducted over different Cu/ZnO-based catalysts prepared by the coprecipitation method. The catalysts were characterized by X-ray diffraction, H 2 temperature-programmed reduction, and the N 2 O titration technique of measurement of the metallic Cu surface area. Results show that the reaction system is affected by hydrogen pressure, temperature, glycerol concentration, and the surface area of metallic copper. Our results tend to show that the reaction scheme is more complicated than the commonly accepted dehydration–hydrogenation mechanism. Tests conducted with varying hydrogen pressure indicate that the mechanism may begin with the dehydrogenation of glycerol to glyceraldehyde. Tests conducted with varying water content tend to show that high water content favors ethylene glycol (EG) formation. The selectivity to 1,2-propanediol (12PG) versus ethylene glycol is a function of the relative reaction rates where the glyceraldehyde can react to either yield 12PG or go through a retro-Claisen route to yield EG. Finally, multiple catalytic tests conducted with a constant amount of copper surface area show that, according to the Madon–Boudart criterion, the catalytic system is heavily hampered by transport phenomena limitations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".