Optimization of Mixed Ag Catalysts for Catalytic Conversions of NO and C <sub>3</sub> H <sub>6</sub>
Bibliographic record
Abstract
In order to adjust the redox properties of perovskite-based silver catalysts to satisfy the requirements for their application under overstoichiometric oxygen conditions (lean burn), three mixtures were prepared [Mixture (I): 10% La0.88Ag0.12FeO3 + 90% Al2O3; Mixture (II): 3% Ag/(10% La0.88Ag0.12FeO3 + 90% Al2O3); Mixture (III): 10% La0.88Ag0.12FeO3 + 90% (3%Ag/Al2O3)]. Mixture (I) with only a 10% La0.88Ag0.12FeO3 blend exhibited a NO reduction behavior similar to that of La0.88Ag0.12FeO3 alone at stoichiometric oxygen (1% O2). NO conversion of La0.88Ag0.12FeO3 under an excess of oxygen (10% O2) was however significantly improved by mixing with Al2O3 resulting in a value of ~ 30% at >400 °C in the case of Mixture (I). Ag impregnation facilitated the NO catalytic reduction, leading to a better deNOx performance for Mixture (II) and Mixture (III) than that of Mixture (I). It was also found that those silver atoms associated with Al2O3 are more effective for NO transformation. Therefore, the optimal mixed catalyst was Mixture (III), which shows satisfactory NO reduction and C3H6 oxidation at 500 °C under the whole range of O2 content from 1% to 10%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".