Investigation of microwave‐assisted synthesis of palladium nanoparticles supported on Fe <sub>3</sub> O <sub>4</sub> as efficient recyclable magnetic catalysts for Suzuki‐Miyaura cross‐coupling
Bibliographic record
Abstract
In this research, a facile and reproducible approach was implemented for the synthesis of palladium nanoparticles supported on Fe 3 O 4 with remarkable activity as an ideal catalyst for Suzuki‐Miyaura cross‐coupling. Magnetite supported Pd nanoparticles reveal high activity in Suzuki‐Miyaura coupling reactions since they could be recycled up to seven times with the same high catalytic activity. This adopted method of catalyst synthesis has many advantages, including reproducibility and the reliability of the adopted synthetic method. The produced catalyst has unique magnetic properties to facilitate catalyst recovery from the reaction mixture by using a strong magnet as an external magnetic field. The synthetic approach adopted in this research is based on the microwave‐assisted irradiation (MWI). The distinctive advantage of adopting this approach rather than conventional heating is the simplicity of adding reactants at mild reaction conditions. Moreover, this method offers a high rate of recyclability as the catalyst itself has a high recyclability rate up to seven times under mild reaction conditions, in addition to reproducibility with an excellent turnover number (8500) and a turnover frequency of 95 000 h −1 . The magnetic properties of the prepared catalyst increase the possibility to separate and purify the products from the catalyst and other byproducts, leading to an increase in the economic value of the catalyst. The prepared catalyst was characterized by various spectroscopic techniques including x‐ray photoelectron spectroscopy (XPS), x‐ray diffraction (XRD), vibrating sample magnetometer (VSM), and transmission electron microscopy (TEM).
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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".