Catalytic Hydrogenolysis of Glycerol to Propylene Glycol over Mixed Oxides Derived from a Hydrotalcite-Type Precursor
Bibliographic record
Abstract
Selective hydrogenolysis of glycerol to propylene glycol was performed using an environmentally friendly hydrotalcite-derived mixed-metal oxide catalyst. The Mg/Al, Zn/Al, Ni/Mg/Al, Ni/Co/Mg/Al, and Cu/Zn/Al mixed-metal oxide catalysts were prepared from their corresponding hydrotalcite precursors having M 2+ /M 3+ compositions over the range of 0.5−3.0. The physicochemical properties of the catalysts were studied by X-ray diffraction (XRD), inductively coupled plasma mass spectrometry (ICP-MS), NH 3 and CO 2 temperature-programmed desorption (TPD), and nitrogen adsorption studies. The XRD patterns of pure hydrotalcites exhibited characteristics of hydrotalcite phases, while those of calcined hydrotalcites showed the formation of corresponding metal oxides. The ICP-MS analysis showed agreement between the calculated and actual metal compositions. The prepared catalysts were evaluated for the hydrogenolysis of glycerol to propylene glycol in a Parr reactor. The activity studies indicated a maximum glycerol conversion and selectivity toward propylene glycol in the case of Cu/Zn/Al mixed-metal oxide catalysts. Further, the reaction parameters were optimized with the most active Cu/Zn/Al catalyst, and it was found that at a catalyst concentration of 5% (w/w) of aqueous glycerol, a hydrogen pressure of 200 psig, and 80% glycerol dilution, a maximum glycerol conversion of 52% with 93−94% selectivity toward propylene glycol were obtained.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".