Selective Hydrogenolysis of Glycerol and Crude Glycerol (a By-Product or Waste Stream from the Biodiesel Industry) to 1,2-Propanediol over B2O3 Promoted Cu/Al2O3 Catalysts
Bibliographic record
Abstract
The performance of boron oxide (B2O3)-promoted Cu/Al2O3 catalyst in the selective hydrogenolysis of glycerol and crude glycerol (a by-product or waste stream from the biodiesel industry) to produce 1,2-propanediol (1,2-PDO) was investigated. The catalysts were characterized using N2-adsorption-desorption isotherm, Inductively coupled plasma atomic emission spectroscopy (ICP-AES), X-ray diffraction (XRD), ammonia temperature programmed desorption (NH3-TPD), thermogravimetric analysis (TGA), temperature programmed reduction (TPR), and transmission electron microscopy (TEM). Incorporation of B2O3 to Cu/Al2O3 was found to enhance the catalytic activity. At the optimum condition (250 °C, 6 MPa H2 pressure, 0.1 h−1 WHSV (weight hourly space velocity), and 5Cu-B/Al2O3 catalyst), 10 wt% aqueous solution of glycerol was converted into 1,2-PDO at 98 ± 2% glycerol conversion and 98 ± 2% selectivity. The effects of temperature, pressure, boron addition amount, and liquid hourly space velocity were studied. Different grades of glycerol (pharmaceutical, technical, or crude glycerol) were used in the process to investigate the stability and resistance to deactivation of the selected 5Cu-B/Al2O3 catalyst.
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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".