Pyrrolidone‐functional smart polymers via nitroxide‐mediated polymerization
Bibliographic record
Abstract
ABSTRACT Nitroxide‐mediated polymerization (NMP) of N‐(2‐methacryloyloxyethyl) pyrrolidone (MAEPYR) with 2‐([tert‐butyl[1‐(diethoxyphosphoryl)‐2,2‐dimethylpropyl]amino]oxy)‐2‐methylpropanoic acid (BlocBuilder) initiator and N‐tert‐butyl‐N‐[1‐diethylphosphono‐(2,2‐dimethylpropyl)] (SG1) nitroxide permitted controlled synthesis of poly(N‐(2‐methacryloyloxyethyl)‐pyrrolidone‐stat‐9‐(4‐vinylbenzyl)‐9H‐carbazole) (poly(MAEPYR‐stat‐VBK)) statistical copolymers. With at least 5 mol % VBK, the dispersity Đ of the copolymers was below 1.4 at conversions less than 50%. At conversions higher than 50%, and at lower VBK feed content, there was a significant amount of termination reactions, which broadened the molecular weight distribution of the final polymers (Đ = 1.4–2.3). The MAEPYR‐rich statistical copolymers were subsequently tested for thermoresponsive behavior in aqueous media. The cloud point temperatures (CPTs) in aqueous solution were tuned by changing the VBK composition, solution concentration, and heating rate, and the transitions were thermally reversible with partial loss of reversibility at higher heating rates. The CPT decreased from 59.0 to 49.7 °C with addition of only 1 mol % of VBK in the copolymer, and at more than 6 mol % VBK, the copolymer was water insoluble. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2014, 52, 2011–2024
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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".