Glycerol hydrogenolysis using a Ni/Ce‐Mg catalyst for improved ethanol and 1,2‐propanediol selectivities
Bibliographic record
Abstract
Abstract Glycerol, a byproduct from biodiesel production, is an inexpensive and renewable alternative feedstock for producing valuable chemicals. Hydrogenolysis of glycerol produces ethanol and 1,2‐propanediol (1,2‐PDO) with the help of appropriate catalysts. Commercially available non‐specific Ni‐based catalysts have been reported for use in producing ethanol along with 1,2‐PDO. In this work, a new Ni catalyst on various support materials was developed for the selective production of ethanol and 1,2‐PDO using glycerol hydrogenolysis. The Ni catalyst on CeO2 showed the highest potential for producing both ethanol and 1,2‐PDO. The catalyst with 0.15–0.50 g/g (15–50 wt%) Ni on CeO2 improved glycerol conversion at reaction temperatures of 215–245 °C, however 1,2‐PDO selectivity was unsatisfactory. An improvement in 1,2‐PDO selectivity was attained when Al, Si, Zn, and/or Mg were added as promoters to the Ni catalyst on CeO2, while improvement in the selectivity for ethanol occurred only with the addition of Si or Mg. A variation in Mg content showed that 1,2‐PDO selectivity was favoured at higher Mg content, while ethanol selectivity peaked at 10 % Mg. Systematic investigations found that the catalyst with 0.25 g/g (25 wt%) Ni on a Ce:Mg (4:1 mol:mol) support provided good selectivities for 1,2‐PDO at 68.10 % and ethanol at 9.0 %, respectively.
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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".