Uncovering Structure–Activity Relationships in Pt/CeO<sub>2</sub> Catalysts for Hydrogen-Borrowing Amination
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The hydrogen-borrowing amination of alcohols is a promising route to produce amines. In this study, experimental parameters involved in the preparation of Pt/CeO 2 catalysts were varied to assess how physicochemical properties influence their performance in such reactions. An amination reaction between cyclopentanol and cyclopentylamine was used as the model reaction for this study. The Pt precursor used in the catalyst synthesis and the properties of the CeO 2 support were both found to strongly influence catalytic performance. Aberration corrected scanning transmission electron microscopy revealed that the most active catalyst comprised linearly structured Pt species. The formation of these features, a function result of epitaxial Pt deposition along the CeO 2 [100] plane, appeared to be dependent on the properties of the CeO 2 support and the Pt precursor used. Density functional theory calculations subsequently confirmed that these sites were more effective for cyclopentanol dehydrogenation─considered to be the rate-determining step of the process─than Pt clusters and nanoparticles. This study provides insights into the desirable catalytic properties required for hydrogen-borrowing amination but has relevance to other related fields. We consider that this study will provide a foundation for further study in this atom-efficient area of chemistry.
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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".