Self-Assembly of Mixtures of Block Copolymers of Poly(styrene-<i>b</i>-acrylic acid) with Random Copolymers of Poly(styrene-<i>c</i><i>o</i>-methacrylic acid)
Bibliographic record
Abstract
The effects of the addition of random copolymers of poly(styrene-co-methacrylic acid) [P(S-co-MAA)] on the self-assembly of block copolymers of poly(styrene-b-acrylic acid) (PS-b-PAA) are described. The effects of variation of five factors, including the MAA content, the weight fraction and molar mass of the P(S-co-MAA), the initial concentration of the mixture, and the length of the PAA segment in the block copolymer, were investigated. With increasing MAA content, the localization of the random copolymer in the aggregate changed from the core to the interface, which led to a morphological transition from spheres to vesicles. Vesicles, mixtures of vesicles and large spheres, and large spheres alone were formed with increasing weight fraction of the random copolymer. When the molar mass of the random copolymer was high, both rods and vesicles were observed at low water contents; otherwise, only vesicles were observed. The vesicle size increased (from 100 to 140 nm) with increasing initial polymer concentration, whereas the vesicle membrane thickness remained constant. The size of the vesicles prepared from the mixtures increased with water content but decreased with the length of PAA in the diblock.
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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 | 0.000 |
| 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".