Supported metal catalysts with single-atom promoters via reductive atom trapping
Bibliographic record
Abstract
Nanosized cerium oxide (CeO2) has been extensively used as an oxygen storage component in automotive emission control systems. However, the possible involvement of atomically dispersed cerium (Ce) has not been explored. Here, we demonstrate the controllable transformation of CeO2 nanoparticles into isolated Ce1 cations on the surface of gamma-type alumina (γ-Al2O3) via reductive atom trapping, achieving over half-monolayer coverage. Supported single-atom rhodium (Rh1) surrounded by dispersed Ce1 shows superior performance to Rh1 on bare Al2O3 or crystalline CeO2 in catalyzing NO reduction, exhibiting a striking one-order-of-magnitude increase in turnover frequency. Dispersed Ce1 also exhibits greatly enhanced oxygen transfer capability and introduces a modified reaction mechanism that involves adjacent Rh1-Ce1 dual-sites, resulting in a greatly decreased activation barrier (96 vs. 192 kJ/mol). The understanding of reductive atom trapping of Ce1 as well as its structure-property relationships obtained in this work could be implemented in the rational design of Ce1-promoted catalysts for many other applications. Benefiting from the greatly enhanced OSC, activity enhancements are also seen with Ce1-promoted platinum nanoparticles for the oxidation of CO and hydrocarbons. Additionally, dispersing Ce1 on Al2O3 results in modified surface properties, which could be further utilized to explore the field of acid-base catalysis
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".