Reusable, Highly Active Heterogeneous Palladium Catalyst by Convenient Self‐Encapsulation Cross‐Linking Polymerization for Multiple CarbonCarbon Cross‐Coupling Reactions at ppm to ppb Palladium Loadings
Bibliographic record
Abstract
Abstract Designing reusable high‐performance heterogeneous palladium (Pd) catalysts via convenient, economic synthesis is of great importance to the industrial applications of various carbon‐carbon cross‐coupling reactions. We demonstrate herein a convenient one‐pot self‐encapsulation synthesis of a heterogeneous Pd catalyst [Pd@PDEB, PDEB=poly(1,3‐diethynylbenzene)] directly from commercially available, economic precursors. In the synthesis, the formation of the cross‐linked polymer networks and Pd encapsulation are accomplished simultaneously, turning a homogeneous Pd polymerization catalyst into the heterogeneous cross‐coupling catalyst. As a unique, practical heterogeneous catalyst, Pd@PDEB shows remarkably high activity, high reusability, and high versatility towards at least four types of cross‐coupling reactions (Suzuki–Miyaura, Stille, allylic arylation, and Mizoroki–Heck reactions) with even difficult reactants (aryl chlorides and heteroaryl halides) under aerobic conditions with Pd loadings down to ppm or even ppb levels. Evidences from hot filtration and 3‐phase tests demonstrate the heterogeneous nature of the catalyst with very low Pd leaching and negligible contributions of leached homogeneous Pd species towards the coupling reactions. magnified image
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".