Highly Exposed Subnanometric Palladium Ensembles on Yttrium Oxide Enable Boosted Catalytic Performance for Heck Cross-Coupling Reactions
Bibliographic record
Abstract
Developing high-performance supported metal catalysts for the Heck cross-coupling reaction of aryl halides is appealing but challenging. Single atom catalysts (SACs) have shown potential for various reactions, but insufficient metallic properties and electronic quantum states of the mononuclear metal sites can not efficiently catalyze complicated molecular transformations. Herein, we report an efficient heterogeneous catalyst with highly exposed subnanometric palladium ensembles decorated over oxygen vacancy-containing yttrium oxide (Pd C /Y 2 O 3- x ) designed to overcome these limitations. Using this palladium catalyst in cross-coupling iodobenzene and methyl acrylate to methyl cinnamate achieves high catalytic performance with 99% selectivity and conversion within 3 h under mild conditions. Such performance is close to that of state-of-the-art catalysts in the field. Furthermore, the exposed palladium atoms enable the desired recyclability and reaction scalability, along with easy coupling of aryl halides, even those containing bromides. A combination of spectroscopic characterizations and theoretical calculations underscores the importance of geometric and electronic structures of the atomically precise and isolated palladium clusters and metal cluster-support interactions, which account for the high activity and stability. This work advances the development of heterogeneous catalysts for C–C coupling reactions and provides a universal single-cluster catalyst design principle for complex organic transformations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".