Liquid‐phase hydrogenation of Chinese white wax to advanced mixed alkanols over Ni‐Cu@ <scp>MgAlO</scp> catalyst
Bibliographic record
Abstract
Abstract The Ni‐Cu@MgAlO catalyst was synthesized by a urea‐assisted pyrolysis co‐precipitation method. Structural characterizations, including N₂ adsorption‐desorption measurement, X‐ray photoelectron spectroscopy (XPS), scanning electron microscope mapping (SEM‐mapping), X‐ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT‐IR), confirmed the successful incorporation of Ni and Cu into the MgAlO framework. The catalyst exhibited good catalytic performance, which can be attributed to the synergistic effect of Ni‐Cu bimetallics, their uniform dispersion on the support, and the catalytic cycle between different valence states. This catalyst was employed to optimize the reaction conditions for the hydrogenation reduction of Chinese white wax. The experimental results indicated that the optimal reaction conditions were achieved at 140°C for 4.0 h, with 0.2 g of catalyst and 2.0 MPa of H 2 pressure. The resulting advanced mixed alkanols showed an acid value of 0.13 and a saponification value of 65.18. Additionally, plausible reaction pathways for the reduction process were proposed. The catalyst's inexpensive and good efficiency are of significant industrial importance, as it enables the conversion of Chinese white wax into advanced mixed alkyl alcohols.
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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 | 0.001 | 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 | 0.000 |
| 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".