The Promoting Effect of Ni on Glycerol Hydrogenolysis to 1,2-Propanediol with In Situ Hydrogen from Methanol Steam Reforming Using a Cu/ZnO/Al2O3 Catalyst
Bibliographic record
Abstract
Production of green chemicals using a biomass derived feedstock is of current interest. Among the processes, the hydrogenolysis of glycerol to 1,2-propanediol (1,2-PD) using externally supplied molecular hydrogen has been studied quite extensively. The utilization of methanol present in crude glycerol from biodiesel production can avoid the additional cost for molecular hydrogen storage and transportation, as well as reduce the safety risks due to the high hydrogen pressure operation. Recently the hydrogenolysis of glycerol with a Cu/ZnO/Al2O3 catalyst using in situ hydrogen generated from methanol steam reforming in a liquid phase reaction has been reported. This paper focusses on the effect of added Ni on the activity of a Cu/ZnO/Al2O3 catalyst prepared by an oxalate gel-co-precipitation method for the hydrogenolysis of glycerol using methanol as a hydrogen source. It is found that Ni reduces the conversion of glycerol but improves the selectivity to 1,2-PD, while a higher conversion of methanol is observed. The promoting effect of Ni on the selectivity to 1,2-PD is attributed to the slower dehydration of glycerol to acetol coupled with a higher availability of in situ hydrogen produced from methanol steam reforming and the higher hydrogenation activity of Ni towards the intermediate acetol to produce 1,2-PD.
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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) | 0.000 | 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".