Intensifying levulinic acid hydrogenation using mechanochemically prepared copper on manganese oxide catalysts
Bibliographic record
Abstract
A series of copper nanoparticles supported on manganese oxide octahedral molecular sieves (OMS-2) were prepared using mechanochemical (Ball-Mill) and conventional wet-impregnation (Wet-Imp) methods. All catalysts prepared were thoroughly characterised using ICP-OES elemental analysis, X-ray diffraction (XRD), N2 sorption, H2 temperature programmed reduction (TPR) and transmission electron microscopy (TEM) techniques. The catalyst preparation methods greatly affected the size of the Cu nanoparticles. TEM images showed that 5 wt% Cu/OMS-2 (Ball-Mill) catalyst had a narrow particle size distribution with an average Cu nanoparticle size of 2.1 nm, while the corresponding 5 wt% Cu/OMS-2 catalyst prepared using wet-impregnation method had an average Cu nanoparticle size of 19.2 nm. The structural features of the catalysts were corelated with the catalytic activity using the liquid phase hydrogenation of levulinic acid (LA) to γ-valerolactone (GVL), as an exemplar process. In LA hydrogenation at 190 °C and 20 bar H2 pressure, the ball milled catalysts achieved higher LA conversion, and greater GVL yield, as compared to the corresponding catalysts prepared by wet-impregnation method, reinforcing that Cu nanoparticle size and metal dispersion are important tool to intensify the catalytic activity. For instance, 5 wt% Cu/OMS-2 (Ball-Mill) catalyst achieved almost twice the turnover frequency (TOF), 24.7 h−1 as compared to the 5 wt% Cu/OMS-2 (Wet-Imp) catalyst, TOF 11.8 h−1, under identical reaction conditions. The results of this study demonstrate that ball milling is a superior method for Cu/OMS-2 catalyst preparation than wet impregnation.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".