Selective Hydrogenation on a Highly Active Single-Atom Catalyst of Palladium Dispersed on Ceria Nanorods by Defect Engineering
Bibliographic record
Abstract
Single-atom catalysis represents a new frontier that integrates the merits of homogeneous and heterogeneous catalysis to afford exceptional atom efficiency, activity, and selectivity for a range of catalytic systems. Herein we describe a simple defect engineering strategy to construct an atomically dispersed palladium catalyst (Pd δ+, 0 < δ < 2) by anchoring the palladium atoms on oxygen vacancies created in CeO 2 nanorods. This was confirmed by spherical aberration correction electron microscopy and extended X-ray absorption fine structure measurement. The as-prepared catalyst showed exceptional catalytic performance in the hydrogenation of styrene (99% conversion, TOF of 2410 h –1 ), cinnamaldehyde (99% conversion, 99% selectivity, TOF of 968 h –1 ), as well as oxidation of triethoxysilane (99% conversion, 79 selectivity, TOF of 10 000 h –1 ). This single-atom palladium catalyst can be reused at least five times with negligible activity decay. The palladium atoms retained their dispersion on the support at the atomic level after thermal stability testing in Ar at 773 K. Most importantly, this synthetic method can be scaled up while maintaining catalytic performance. We anticipate that this method will expedite access to single-atom catalysts with high activity and excellent resistance to sintering, significantly impacting the performance of this class of catalysts.
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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) | 0.000 | 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".