Synergistic impact of Cu and support materials in Ni-based catalysts for glycerol hydrogenolysis to 1,2-propanediol
Bibliographic record
Abstract
Selective hydrogenolysis of glycerol to 1,2-propanediol (1,2-PD) represents an essential evaluating process for the valorization of glycerol. In this research, a series of Ni- and Ni–Cu-based catalysts supported on SiO2, SiO2–Al2O3, and titanium (IV) oxide (TiO2), were prepared via the sequential wetness impregnation approach and evaluated for their performance in glycerol hydrogenolysis. The results indicated that the 25Ni-10Cu/TiO2 catalyst exhibited strong catalytic efficiency in glycerol hydrogenolysis, showing 72.9% conversion of glycerol and 85.8% selectivity to 1,2-PD (62.5% yield) after 24 h reaction time. This enhanced performance is attributed to the optimal balance and synergy between the acidity of the support and the active metal sites, promoting both dehydration and hydrogenation reactions. The presence of Cu in the catalyst system was found to significantly enhance glycerol hydrogenolysis while inhibiting unwanted side reactions. In contrast, monometallic Ni/SiO2 demonstrated the lowest conversion of glycerol (33.8%) at 24 h, while bimetallic Ni–Cu/SiO2 and Ni–Cu/SiO2–Al2O3 catalysts exhibited high activity, with glycerol conversion rates exceeding 91%. These findings demonstrate that incorporation of Ni with Cu is essential for optimizing both catalytic efficiency and selectivity, particularly when combined with supports like TiO2.
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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) | 0.000 | 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".