Single Ru(II) Ions on Ceria as a Highly Active Catalyst for Abatement of NO
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it