Functionalized Ionic Liquids for the Synthesis of Metal Nanoparticles and their Application in Catalysis
Bibliographic record
Abstract
Abstract Colloidal suspensions of metal nanoparticles (NPs) in non‐functionalized ionic liquids (ILs) are active catalysts for a wide variety of organic transformations. The weak ionic interaction between the IL and the metal surface provides a bare particle with a high concentration of active metal sites. However, the long‐term stabilities can be affected as the IL cannot provide sufficient stabilization and aggregation of the NPs under catalytic conditions limits the reuse of NP:IL catalysts. ILs functionalized with a metal‐binding moiety (FILs) can alleviate this agglomeration problem and provide catalysts with improved activities and recyclabilities. The enhanced stability is provided by the covalent attachment of the stabilizer to the metal surface, which holds the electrostatic stabilizing IL headgroup at the outer ligand sphere of the NP. FILs have been used in the synthesis of NPs as soluble ligands in aqueous, organic or IL solution, as neat solvents, as anchors to immobilize NPs onto a solid support and incorporated into polymeric stabilizers. This Minireview discusses the synthesis and application of NP:FIL systems, with an emphasis on catalysis.
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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 | 0.001 | 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".