A Highly Water‐Dispersible/Magnetically Separable Palladium Catalyst: Selective Transfer Hydrogenation or Direct Reductive N‐Formylation of Nitroarenes in Water
Bibliographic record
Abstract
Abstract Simple ion exchange of the chloride anion of an ionic‐liquid‐functionalized magnetic nanoparticle with [PdCl4]2− provided a highly water‐dispersible and magnetically separable palladium catalyst that exhibited excellent activity toward transfer hydrogenation reactions in water as a solvent. The catalyst demonstrated outstanding performance in aqueous‐phase transfer hydrogenation of various nitroarenes in a highly chemo‐ and regioselective manner by using HCOONH4 as a low‐cost, green, and easily available hydrogen donor. Also, by using only 0.25 mol % of the catalyst and formic acid as both a hydrogen donor and formylating agent, the catalyst showed excellent activity in the one‐pot, direct synthesis of N‐arylformamides from nitroarenes in water as a solvent. Notably, owing to the presence of a hydrophilic ionic liquid on the surface of silica‐coated iron oxide nanoparticles, the catalyst showed highly stable dispersion in water, as evidenced by the zeta potential and extremely low affinity to the organic phase. These features make this catalyst system suitable for an efficient double‐separation strategy (successive extraction/final magnetic separation). The recovered aqueous phase containing the catalyst can be simply and efficiently reused in eight runs without a decrease in activity and can be easily separated from the aqueous phase at the end of the process by applying an external magnetic field.
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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) | 0.001 | 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".