Activity of Ag/CeZrO2, Ag+K/CeZrO2, and Ag-Au+K/CeZrO2 Systems for Lean Burn Exhaust Clean-Up
Bibliographic record
Abstract
Herein, the activity of Ag and bimetallic Au-Ag catalysts, supported over Ce0.85Zr0.15O2 (CZ), was investigated in a complex stream, whose components included CO, C3H8, NO, O2, and, optionally, an injection of water vapor. In such a stream, three of the possible reactions that can occur are CO oxidation, propane combustion, and NO oxidation. The aim of these studies was to explore whether silver, due to its strong affinity to oxygen, will counteract the stabilization of oxygen by potassium. The effect of the presence of potassium ions on the activity of the monometallic silver catalysts is beneficial in the complex stream without water vapor in all three studied reactions, although it is negligible in the model CO stream. It has been shown that water vapor strongly suppresses the activity of the Ag+K/CZ catalyst, much more so than that of the Ag/CZ catalyst. The second purpose of the work was to determine whether the negative effect of potassium ions on the activity of nanogold catalyst can be countered by the addition of silver. Studies in a model stream for CO oxidation have shown that, for a catalyst preloaded with gold, the effect of potassium is nulled by silver, and the activity of AuAg + 0.15 at%K/CZ and AuAg + 0.30 at%K/CZ is the same as that of the monometallic Au catalyst. Conversely, when the reaction is carried out in a complex stream, containing CO, C3H8, NO, O2, and water vapor, the presence of water vapor leads to higher CO conversion as well as increased NO2 formation and slightly suppresses the C3H8 combustion.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".