Reducibility Studies of Ceria, Ce0.85Zr0.15O2 (CZ) and Au/CZ Catalysts after Alkali Ion Doping: Impact on Activity in Oxidation of NO and CO
Bibliographic record
Abstract
The aim of these studies was to perform thorough research on the influence of alkali metal ions (Li, Na, K and Cs) on the properties of nanogold catalysts supported on ceria–zirconia. The addition of alkali metal ions onto CeO2 further affected the reducibility, which was not noted for the Zr-doped support (Ce0.85Zr0.15O2). Despite the substantial impact of alkali metal ions on the reducibility of ceria, the activity in CO oxidation did not change much. In contrast, they do not have a large effect on the reducibility of Au/CZ but suppressed the activity of this system in CO oxidation. The results show that for CO oxidation, the negative effect of potassium ions is greater than that of sodium, which corresponds to the shift in the Tmax of the reduction peak towards higher temperatures. The negative effect of Li+ and Cs+ spans 50% CO conversion. The negative effect was visible for CO oxidation in both the model stream and the complex stream, which also contained hydrocarbons and NO. In the case of NO oxidation to NO2, two temperature regimes were observed for Au + 0.3 at% K/CZ, namely in the temperature range below 350 °C; the effect of potassium ions was beneficial for NO oxidation, whereas at higher temperatures, the undoped gold catalyst produced more NO2.
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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 | 0.000 |
| 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) | 0.001 | 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".