Effect of acrylic acid neutralization on ‘livingness’ of poly[styrene‐<i>ran</i>‐(acrylic acid)] macro‐initiators for nitroxide‐mediated polymerization of styrene
Bibliographic record
Abstract
Abstract BACKGROUND: The effect of acrylic acid neutralization on the degradation of alkoxyamine initiators for nitroxide‐mediated polymerization (NMP) was studied using styrene/acrylic acid and styrene/sodium acrylate random copolymers (20 mol% initial acrylate feed concentration) as macro‐initiators. The random copolymers were re‐initiated with fresh styrene in 1,4‐dioxane at 110 °C at SG1 mediator/BlocBuilder® unimolecular initiator ratios of 5 and 10 mol%. RESULTS: The value of kpK (kp = propagation rate constant, K = equilibrium constant) was not significantly different for styrene/acrylic acid and styrene/sodium acrylate compositions at 110 °C (kpK = 2.4 × 10−6–4.6 × 10−6 s−1) and agreed closely with that for styrene homopolymerization at the same conditions (kpK = 2.7 × 10−6–3.0 × 10−6 s−1). All random copolymers had monomodal, narrow molecular weight distributions (polydispersity index M̄w/M̄n = 1.10–1.22) with similar number‐average molecular weights M̄n = 19.3–22.1 kg mol−1. Re‐initiation of styrene/acrylic acid random copolymers with styrene resulted in block copolymers with broader molecular weight distributions (M̄w/M̄n = 1.37–2.04) compared to chains re‐initiated by styrene/sodium acrylate random copolymers (M̄w/M̄n = 1.33). CONCLUSIONS: Acrylic acid degradation of the alkoxyamines was prevented by neutralization of acrylic acid and allowed more SG1‐terminated chains to re‐initiate the polymerization of a second styrenic block by NMP. Copyright © 2008 Society of Chemical Industry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".