H2-TPR, XPS and TEM Study of the Reduction of Ru and Re promoted Co/γ-Al2O3, Co/TiO2 and Co/SiC Catalysts
Bibliographic record
Abstract
<p class="1Body">Effects of Ru and Re promoters on Co-CoO<sub>x </sub>catalysts supported on γ-Al<sub>2</sub>O<sub>3</sub>, TiO<sub>2</sub> and SiC were investigated to improve the understanding of the role of promoters of the active phase of Co-CoO<sub>x</sub>-Ru and Co-CoO<sub>x</sub>-Re. The influence of promoter addition on the composition and activity of the catalysts was characterized by several methods, such as H<sub>2</sub>-TPR, XPS, chemisorption and TEM. Furthermore, the role of support and metal-support interaction was especially studied and different support materials were compared.</p><p class="1Body">Based on the results, addition of promoter metals (Ru or Re) will most likely improve catalytic activity of Co/γ-Al<sub>2</sub>O<sub>3</sub>, Co/TiO<sub>2</sub> and Co/SiC catalysts by increasing the active metal surface available for chemical reaction and by decreasing the size of the metallic nanoparticles. These changes in the catalytic activity were also associated with the changes in the ratio of metal and metal oxide phases in the surface composition as observed by XPS. Promoter metals also decreased the reduction temperatures needed for the reduction of Co<sub>3</sub>O<sub>4</sub> to CoO and further to metallic cobalt. Significant decrease in reduction temperature was observed especially when ruthenium was used as the promoter.</p>
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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 | 0.021 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| 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".