PdNP Decoration of Halloysite Lumen via Selective Grafting of Ionic Liquid onto the Aluminol Surfaces and Catalytic Application
Bibliographic record
Abstract
The synthesis of selectively deposited palladium nanoparticles (PdNPs) inside tubular halloysite lumens is reported. This specific localization was directed by the selective modification of the aluminol surfaces of the clay mineral through stable Al-O-C bonds. An ionic liquid (1-(2-hydroxyethyl)-3-methylimidazolium) was grafted onto halloysite following the guest displacement method (generally used for kaolinite) using halloysite-DMSO preintercalate. The characterization of this clay nanohybrid material (XRD, NMR, TGA) showed characteristics reminiscent of similar materials synthesized from kaolinite. The grafting on halloysite lumens was also effective without using the DMSO preintercalate. The presence of these new functionalities in halloysite directs the synthesis of uniform PdNPs with size ranging between 3 and 6 nm located exclusively in the lumens. This results from the selective adsorption of PdNPs precursors in functionalized lumens through an anion exchange mechanism followed by in situ reduction. In contrast, the unmodified clay mineral displayed nanoparticles both inside and outside the tubes. These catalysts showed significant catalytic activity for the reduction of 4-nitrophenol (4-NP). The most efficient catalysts were recycled up to three times without reducing significantly the catalytic activities.
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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".