Insights into selective hydrogenation of levulinic acid using copper on manganese oxide octahedral molecular sieves
Bibliographic record
Abstract
Selective hydrogenation of levulinic acid (LA) to γ-valerolactone (GVL) was studied using copper on manganese oxide octahedral molecular sieve (OMS-2) as catalysts. A range of copper supported on OMS-2 catalysts was prepared using the modified wet-impregnation technique and characterized thoroughly using powder X-ray diffraction, inductively coupled plasma optical emission spectroscopy metal analysis, Fourier transform infrared, high-resolution transmission electron microscopy and N 2 sorption analyses. Process parameters for selective hydrogenation of LA to GVL were optimized using the design of experiment (DoE) approach with response surface methodology comprising a central composite design. Using the optimized conditions (190°C reaction temperature, 20 bar H 2 pressure and 20 wt% Cu loading on OMS-2), up to 98% yield of GVL could be achieved in water as a solvent. Based on DoE, H 2 pressure had the most influence on GVL selectivity followed by catalyst loading used for the hydrogenation of LA. The response surface methodology model also showed synergistic effect of reaction temperature and H 2 pressure on the yield of GVL. 20 wt% Cu/OMS-2 catalysts were re-used up to four cycles and showed noticeable loss of activity after the first cycle due to observed leaching of loose Cu species, thereafter the activity loss diminished during subsequent recycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".