Exploring the dynamic evolution of lattice oxygen on exsolved-Mn2O3@SmMn2O5 interfaces for NO Oxidation
Bibliographic record
Abstract
Lattice oxygen in metal oxides plays an important role in the reaction of diesel oxidation catalysts, but the atomic-level understanding of structural evolution during the catalytic process remains elusive. Here, we develop a Mn2O3/SmMn2O5 catalyst using a non-stoichiometric exsolution method to explore the roles of lattice oxygen in NO oxidation. The enhanced covalency of Mn–O bond and increased electron density at Mn3+ sites, induced by the interface between exsolved Mn2O3 and mullite, lead to the formation of highly active lattice oxygen adjacent to Mn3+ sites. Near-ambient pressure X-ray photoelectron and absorption spectroscopies show that the activated lattice oxygen enables reversible changes in Mn valence states and Mn-O bond covalency during redox cycles, reducing energy barriers for NO oxidation and promoting NO2 desorption via the cooperative Mars-van Krevelen mechanism. Therefore, the Mn2O3/SmMn2O5 exhibits higher NO oxidation activity and better resistance to hydrothermal aging compared to a commercial Pt/Al2O3 catalyst. This work reports on an exsolved Mn2O3/SmMn2O5 diesel oxidation catalyst competitive with current commercial materials in reactivity and hydrothermal aging resistance and further clarifies the catalytic mechanism for lattice oxygen as the reactive center
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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".