Synthesis and catalytic application of palladium nanoparticles supported on kaolinite-based nanohybrid materials
Bibliographic record
Abstract
Palladium nanoparticles (PdNPs) were deposited on the surface of the modified clay mineral, kaolinite. To improve compatibility, abundance and control of the size of the nanoparticles, kaolinite was modified by the grafting of an amino alcohol (triethanolamine (TEA)) and an ionic liquid (1-(2-hydroxyethyl)-3-methylimidazolium (ImIL)). Characterization techniques (XRD, TGA, solid state (13)C NMR and FTIR spectroscopy) confirmed the effective grafting of these compounds on the internal surface of kaolinite. After the synthesis of PdNPs onto clay particles, TEM allowed the visualization of abundant PdNPs with sizes ranging from 4 to 6 nm, uniformly distributed onto the platelets of modified kaolinite. Unmodified clay showed low abundance and random distribution of the nanoparticles. The catalysts obtained were effective for the catalytic reduction of 4-nitrophenol (4-NP), the material with TEA being the most effective. These materials have exhibited excellent performance during the Heck and particularly the Suzuki-Miyaura coupling reactions, with reaction yields up to 100%. These catalysts showed a very slight loss in activity for three consecutive catalytic cycles (less than 10% decrease of the activity compared to the first cycle). This was an evidence that the prior grafting modification of kaolinite helps in significantly improving the quality of the synthesized NPs and also promotes their strong attachment onto the clay mineral surface.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".