Improved Stability and Catalytic Activity of Palladium Nanoparticle Catalysts using Phosphine‐Functionalized Imidazolium Ionic Liquids
Bibliographic record
Abstract
Abstract Palladium nanoparticles (Pd NPs) stabilized by 6 different phosphine‐functionalized ionic liquids (PFILs) were synthesized in imidazolium‐based ionic liquids (ILs) using H2(g) (4 bar) as a reductant. Characterization showed well‐dispersed particles of ∼3 nm (TEM) and confirmed the PFIL stabilization of the NPs (XPS). The PFILs were composed of an imidazolium functionality separated from the phosphine group by a propyl or undecyl chain. The counter anions for both FILs and IL solvents were chosen from N‐bis(trifluoromethanesulfonyl)imide (Tf2N−), trifluoromethanesulfonate (TfO−) or hexafluorophosphate (PF6−). Colloidal suspensions of the Pd NPs were employed as biphasic hydrogenation catalysts for the reduction of the olefinic bond in styrene under mild conditions (50 °C, 4 bar H2(g), 1.5 h). The PFIL‐stabilized Pd NPs were effective hydrogenation catalysts and showed superior activity and recyclability over NPs synthesized in the absence of PFILs. Poisoning tests of the Pd NP catalysts and characterization of the electronic properties of the phosphine were also performed.
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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".