A Nano‐Fibrillated Mesoporous Carbon as an Effective Support for Palladium Nanoparticles in the Aerobic Oxidation of Alcohols “on Pure Water”
Bibliographic record
Abstract
A novel nano-fibrillated mesoporous carbon (IFMC) was successfully prepared via carbonization of the ionic liquid 1-methyl-3-phenethyl-1H-imidazolium hydrogen sulfate (1) in the presence of SBA-15. The material was shown to be an efficient and unique support for the palladium nanoparticle (PdNP) catalyst Pd@IFMC (2) in aerobic oxidation of heterocyclic, benzylic, and heteroatom containing alcohols on pure water at temperatures as low as 40 °C for the first time and giving almost consistent activities and selectivities within more than six reaction runs. The catalyst has also been employed as an effective catalyst for the selective oxidation of aliphatic and allylic alcohols at 70-80 °C. The materials were characterized by X-ray photoelectron spectroscopy (XPS), N(2) adsorption-desorption analysis, transmission electron microscopy (TEM), and electron tomography (ET). Our compelling XPS and ET studies showed that higher activity of 2 compared to Pd@CMK-3 and Pd/C in the aerobic oxidation of alcohols on water might be due to the presence of nitrogen functionalities inside the carbon structure and also the fibrous nature of our materials. The presence of a nitrogen heteroatom in the carboneous framework might also be responsible for the relatively uniform and nearly atomic-scale distribution of PdNPs throughout the mesoporous structure and the inhibition of Pd agglomeration during the reaction, resulting in high durability, high stability, and recycling characteristics of 2. This effect was clearly confirmed by comparing the TEM images of the recovered 2 and Pd@CMK-3.
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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".