Grafting–through Strategy in Emulsion: An Eco–friendly and Effective Route for the Synthesis of Graft Copolymers
Bibliographic record
Abstract
Abstract Brush‐like and centipede multigraft copolymers of poly( n ‐butyl acrylate)– g –poly(styrene) were synthesized via seeded emulsion polymerization under 1,1‐diphenylethylene (DPE) and miniemulsion polymerization. Single‐tailed and double‐tailed poly(styrene) macromonomers were prepared by DPE‐technique in emulsion and Steglich esterification. Then poly( n ‐butyl acrylate)– g –poly(styrene) multigraft copolymers were obtained by miniemulsion copolymerization. The molecular characteristics of poly(styrene) macromonomers and poly( n ‐butyl acrylate)– g –poly(styrene) have been characterized by gel permeation chromatography. The weight content of poly(styrene) and the number of branch points in brush‐like and centipede multigraft copolymers were calculated by proton nuclear magnetic resonance, and the maximum number of branch points is 47. Differential scanning calorimetry analysis and morphological observations by atomic force microscopy confirmed microphase separation in poly( n ‐butyl acrylate)– g –poly(styrene). Tensile properties showed that brush‐like and centipede multigraft copolymers had characteristics of TPEs when the weight content of poly(styrene) in poly( n ‐butyl acrylate)– g –poly(styrene) was 23 %∼37 %. And centipede multigraft copolymers showed better elastic properties than brush‐like multigraft copolymers, which was in line with our previous work.
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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) | 0.001 | 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 | 0.000 |
| 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".