Immobilizing palladium on melamine‐functionalized magnetic nanoparticles: An efficient and reusable phosphine‐free catalyst for Mizoroki–Heck reaction
Bibliographic record
Abstract
A highly efficient and stable heterogeneous catalyst was successfully prepared by anchoring palladium(0) onto melamine‐functionalized Fe3O4 magnetic nanoparticles (MNPs‐Mel‐Pd). With the aid of amine functional groups, melamine was covalently bonded on epoxy functionalized magnetic nanoparticles and then Pd(0) was immobilized on this support with high dispersion. The prepared nanocatalyst exhibits excellent catalytic activity for CC cross coupling (Mizoroki–Heck) of various aryl halides (iodide, bromides, and chrlorides) with olefins under mild reaction condition in relatively short reaction times. The synthesized nanocatalyst was characterized using Fourier transform infrared (FT‐IR) spectroscopy, X‐ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), vibrating sample magnetometer (VSM), and X‐ray photoelectron spectroscopy (XPS) techniques. The loading level of Pd in MNPs‐Mel‐Pd catalyst was measured to be 1.26 × 10−3 mol g−1 by atomic absorption spectroscopy (AAS). In addition, the catalyst can be easily separated and recovered from the reaction mixture by using an external magnet. The heterogeneity of the catalyst was confirmed by the hot filtration test, which was reused for at least six times under the optimized conditions without any significant loss of its activity.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".