Preparation of polyvinyl alcohol–chitosan– <scp> NH <sub>4</sub> Br </scp> catalyst and its application to cycloaddition of carbon dioxide to propylene oxide
Bibliographic record
Abstract
Abstract An eco‐friendly polyvinyl alcohol (PVA)–chitosan(CS)–NH 4 Br catalyst was prepared using a modified solution method. Although the amounts of PVA and CS were fixed, the amount of NH 4 Br was varied in the range 15–45 wt.%. The catalytic performance of PVA–CS–NH 4 Br was compared based on the carbonation performance in the presence of single‐component catalysts (PVA, CS, and NH 4 Br) and dual component catalyst (PVA–CS). The catalytic activity of PVA–CS was inadequate and considerably less than that observed when either PVA or CS was exclusively loaded. The number of functional groups within the PVA–CS structure was presumed to have been reduced due to the interactions of the OH groups in PVA and CS. However, the PVA–CS–NH 4 Br catalyst distinctively exhibited its catalytic performance. It has been suggested that NH 4 Br aided in sustaining the innate functional groups that both PVA and CS have within the PVA–CS–NH 4 Br structure. These results indicate that the functional groups and nucleophiles within the PVA–CS–NH 4 Br structure more effectively participated in the carbonation of propylene oxide. Quantitatively, the addition of 30 wt.% NH 4 Br yielded the best performance. Characteristic analyses of the catalysts were performed using X‐ray diffraction, thermogravimetric analysis, Fourier transform infrared spectroscopy, and X‐ray photoelectron spectroscopy.
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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".