Influence of Surface Properties of Nanostructured Ceria-Based Catalysts on Their Stability Performance
Bibliographic record
Abstract
As the poor cycling stability of CeO2 catalysts has become the major obstacle for applications of diesel particulate filters (DPF), it is necessary to investigate how to reduce their structural and compositional changes during soot oxidation. In this study, different ratios of Samarium (Sm) were doped into the lattice of CeO2 nanoparticles to improve the catalytic performance as well as surface properties. The stability was investigated by recycling the catalyst, mixing it with soot again, and repeating the thermogravimetric analysis (TGA) tests seven times. Consistent observations were expected for more cycles. It was found that doping 5%, 10%, and 20% samarium into the CeO2 lattice can improve the catalyst stability but at the cost of losing some activity. While the catalyst became more stable with the increasing Sm doping, the 10% Sm-doped catalyst showed the best compromise between stability and activity. Ce3+ and Oα were found to play important roles in controlling catalytic soot oxidation activity. These two species were directly related to oxygen vacancies and oxygen storage capacity of the catalyst. Sm-doped catalysts showed a minimized decrease in the Ce3+ and Oα content when the fresh and spent catalysts were compared.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".