Platinum Nanoparticles Supported on Ionic Liquid‐Modified Magnetic Nanoparticles: Selective Hydrogenation Catalysts
Bibliographic record
Abstract
Abstract A method for supporting platinum nanoparticles on magnetite nanoparticles is described. The method requires modification of the surface of the magnetic nanoparticles with ionic liquid groups. Before modification, the magnetic nanoparticles are not stable and easily aggregate and, after modification, the magnetite nanoparticles become highly stable and soluble in polar or non‐polar organic solvents depending on the alkyl group of the linked ionic liquids. The supporting of platinum nanoparticles on the modified magnetic nanoparticles was achieved by adsorbing platinum salts (K2PtCl4) on the surface of the magnetite nanoparticles via ion exchange with the linked ionic liquid groups and then reducing them by hydrazine. The supported platinum nanoparticles were applied in the catalytic hydrogenation of alkynes in which cis‐alkenes were selectively produced, and in the hydrogenation of α,β‐unsaturated aldehydes where the allyl alcohols were obtained as the exclusive products. The new catalyst can be easily separated from the reaction mixtures by applying an external magnetic field and recycled.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".