Predicting NO<sub><i>x</i></sub> Catalysis by Quantifying Ce<sup>3+</sup> from Surface and Lattice Oxygen
Bibliographic record
Abstract
Our work introduces a novel technique based on the magnetic response of Ce 3+ and molecular oxygen adsorbed on the surface of nanoceria and ceria-based catalysts that quantifies the number and type of defects and demonstrates that this information is the missing link that finally enables predictive design of NO x catalysis in ceria-based systems. The new insights into ceria catalysis are enabled by quantifying the above for different ceria nanoparticle shapes (i.e., surface terminations) and O 2 partial pressure. We used ceria nanorods, cubes, and spheres and evaluated them for catalytic reduction of NO by CO. We then demonstrated the quantitative prediction of the reactivity of nanomaterials via their magnetism in different atmospheric environments. We find that the observed enhancement of reactivity for ceria nanocubes and nanorods is not directly due to improved reactivity on those surface terminations but rather due to the increased ease of generating lattice defects in these materials. Finally, we demonstrate that the method is equally applicable to highly topical and industrially relevant ceria mixed oxides, using nanoscale alumina-supported ceria as a representative case–a most ill-defined catalyst. Because the total oxide surface is a mixture of active ceria and inactive support and ceria is not likely present as crystallographically well-defined phases, reactivity does not easily scale with surface area or a surface termination. The key parameter to design efficient NO reduction in ceria-based catalysts is knowing and controlling the surface localized excess Ce 3+ ion areal density.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 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".