Dispersion State of Nickel Ions on γ-Al_2O_3 and Catalytic Activity of Derived Nickel Catalysts for Hydrogenation of α-Pinene
Bibliographic record
Abstract
The dispersion state of nickel ions on γ-Al2O3 and the catalytic hydrogenation activity of supported Ni/γ-Al2O3 catalysts have been studied by means of X-ray diffraction (XRD), UV-Vis diffuse reflectance spectroscopy (DRS), H2 temperature-programmed reduction (TPR), CO chemisorption and microreactor tests. It has been shown that the supported nickel ions preferentially incorporate into the tetrahedral vacancies of γ-Al2O3 when Ni2+ loading is far below its dispersion capacity on γ-Al2O3. Increasing Ni2+ loading, the ratio of Ni2+ ions incorporated into the octahedral vacancies of γ-Al2O3 increases. Since the octahedral Ni2+ ions are easier to be reduced to the metallic state, the reduction degree of supported NiO/γ-Al2O3 sample increases greatly with Ni2+ loading, thus resulting in a great increase in the CO uptake and catalytic activity of Ni/γ-Al2O3 catalyst for hydrogenation of α-pinene. The promotional effect of La2O3 on the catalytic activity of the supported Ni/γ-Al2O3 catalyst has been studied as well. It has been suggested that the dispersed La3+ species on γ-Al2O3 may inhibit incorporation of Ni2+ ions into the tetrahedral vacancies of γ-Al2O3 and increases the ratio of octahedral Ni2+ ions to tetrahedral Ni2+ ions, and thus increases the reduction degree of the catalyst precursor. As a result, the Ni-La2O3/γ-Al2O3 catalyst shows higher catalytic activity than the Ni/γ-Al2O3 catalyst with the same nickel loading.
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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 | 0.000 |
| 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 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".