Single Ru(II) Ions on Ceria as a Highly Active Catalyst for Abatement of NO
Bibliographic record
Abstract
Atom trapping leads to catalysts with atomically dispersed Ru 1 O 5 sites on (100) facets of ceria, as identified by spectroscopy and DFT calculations. This is a new class of ceria-based materials with Ru properties drastically different from the known M/ceria materials. They show excellent activity in catalytic NO oxidation, a critical step that requires use of large loadings of expensive noble metals in diesel aftertreatment systems. Ru 1 /CeO 2 is stable during continuous cycling, ramping, and cooling as well as the presence of moisture. Furthermore, Ru 1 /CeO 2 shows very high NO x storage properties due to formation of stable Ru–NO complexes as well as a high spill-over rate of NO x onto CeO 2 . Only ∼0.05 wt % of Ru is required for excellent NO x storage. Ru 1 O 5 sites exhibit much higher stability during calcination in air/steam up to 750 °C in contrast to RuO 2 nanoparticles. We clarify the location of Ru(II) ions on the ceria surface and experimentally identify the mechanism of NO storage and oxidation using DFT calculations and in situ DRIFTS/mass spectroscopy. Moreover, we show excellent reactivity of Ru 1 /CeO 2 for NO reduction by CO at low temperatures: only 0.1–0.5 wt % of Ru is sufficient to achieve high activity. Modulation-excitation in situ infrared and XPS measurements reveal the individual elementary steps of NO reduction by CO on an atomically dispersed Ru ceria catalyst, highlighting unique properties of Ru 1 /CeO 2 and its propensity to form oxygen vacancies/Ce +3 sites that are critical for NO reduction, even at low Ru loadings. Our study highlights the applicability of novel ceria-based single-atom catalysts to NO and CO abatement.
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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".