Application of Potassium Ion Deposition in Determining the Impact of Support Reducibility on Catalytic Activity of Au/Ceria-Zirconia Catalysts in CO Oxidation, NO Oxidation, and C3H8 Combustion
Bibliographic record
Abstract
The purpose of the study was to show how a controlled, subtle change of the reducibility of the support by deposition of potassium ions impacts the activity of gold catalysts. Since the activity of supported gold catalysts in carbon monoxide oxidation is known to strongly depend on the reducibility of the support, this reaction was chosen as the model reaction. The results of tests conducted in a simple system in which the only reagents were CO and O2 showed good agreement with the CO activity trend in tests performed in a complex stream of reagents, which also contained CH4, C2H6, C3H8, NO, and water vapor. The results of the X-ray Diffraction (XRD) studies revealed that the support has the composition Ce0.85Zr0.15O2, that its lattice constant is the same for all samples, and that gold is mostly present in the metallic phase. The reducibility of the systems was established based on Temperature Programmed Reduction (TPR) and in situ XRD measurements in H2 atmosphere. The results show that the low temperature reduction peak, which is due to the presence of gold, is shifted to a higher value by the presence of 0.3 at% potassium ions on the surface. Moreover, the increase of the potassium loading leads to a more pronounced shift. The T50 of CO oxidation in the simple model stream was found to exhibit an excellent linear correlation with the maximum temperature of the low temperature reduction peak of Au catalysts. This means that stabilizing oxygen with a known amount of potassium ions can be numerically used to estimate the T50 in CO oxidation. The results in the complex stream also showed a similar dependence of CO conversion on reducibility, though there was no substantial difference in the activity of the catalysts in other reactions regardless of the potassium loading. These studies have shown that the influence of potassium varies depending on the reaction, which highlights differences in the impact of reducibility and importance of other factors in these reactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".