TEMPO-Functionalized Aromatic Polymer as a Highly Active, pH-Responsive Polymeric Interfacial Catalyst for Alcohol Oxidation
Bibliographic record
Abstract
We report in this paper the design and synthesis of a pH-responsive polymeric interfacial catalyst (PIC) via one-step grafting of 2,2,6,6-tetramethylpiperidine- N -oxyl (TEMPO) onto a polyaryletherketone having pendant benzimidazole groups (SCBI-PAEK-6F). The TEMPO-functionalized polymer has been systematically characterized with 1 H nuclear magnetic resonance, Fourier-transform infrared spectroscopy, elemental analysis, contact angle, and transmission electron microscopy. Moreover, this well-designed polymer has been applied in the oxidation of alcohols under Montanari conditions. It is found that the functionalized polymer can aggregate at the water–oil interface and act as an efficient stabilizer to facilitate the formation of a stable Pickering emulsion, which shows outstanding catalytic activity for alcohol oxidation through the microreactor mechanism. Moreover, the PIC is featured with desirable high pH responsiveness due to the valuable benzimidazole groups. De-emulsification of the Pickering emulsion reaction system can be conveniently triggered by simply tuning the system pH value to 3, thus facilitating the facile recovery of the PIC. In addition, the sustainable catalyst can be reused for subsequent cycles of alcohol oxidation without appreciable loss in catalytic activity or selectivity.
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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".