Synthesis of a highly stable <scp>Pt</scp> / <scp> CeO <sub>2</sub> </scp> / <scp> Al <sub>2</sub> O <sub>3</sub> </scp> catalyst for gasoline engine emission control by adjusting <scp>Pt</scp> distribution
Bibliographic record
Abstract
Abstract For a CeO 2 /Al 2 O 3 ‐supported Pt catalyst system, the distribution of Pt is of significant importance to its hydrothermal stability and catalytic performance. Generally speaking, impregnation of a Pt precursor on CeO 2 /Al 2 O 3 composite is a commonly applied approach to synthesize the catalyst. In this work, partially calcined CeO 2 on Al 2 O 3 was used as the support to modify Pt distribution. The characterization results reveal that for conventional Pt/CeO 2 /Al 2 O 3 , Pt is distributed on the outer surface of CeO 2 /Al 2 O 3 ; although high dispersion of Pt is obtained for the fresh sample, severe aggregation of Pt species inevitably takes place upon hydrothermal aging treatment, leading to drastic catalyst deactivation. On the other hand, when Pt is principally embedded into the CeO 2 region, the Pt/CeO 2 /Al 2 O 3 catalyst has better hydrothermal stability; however, it still shows undesirable catalytic performance owing to the comparatively low dispersion of Pt on the surface. Fortunately, when Pt species are partially embedded into the CeO 2 region, and the other portion is located on the outer surface of CeO 2 , a favourable balance between the dispersion and hydrothermal stability is realized, which consequently brings about superior three‐way catalytic performance after hydrothermal aging treatment.
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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".