Control of Corona Composition and Morphology in Aggregates of Mixtures of PS-<i>b</i>-PAA and PS-<i>b</i>-P4VP Diblock Copolymers: Effects of pH and Block Length
Bibliographic record
Abstract
The corona compositions and morphologies in aggregates of mixtures of amphiphilic polystyrene-block-poly(acrylic acid) (PS-b-PAA) and polystyrene-block-poly(4-vinylpyridine) (PS-b-P4VP) diblock copolymers are influenced by controllable assembly parameters such as the hydrophilic block length and solution pH. The morphologies and corona compositions of the aggregates were investigated by transmission electron microscopy and electrophoretic mobility, respectively. When mineral acids or bases are present during aggregate formation, they can exert a strong influence on the corona composition. Morphology changes were also seen with changing pH, as well as changes in corona composition, specifically for vesicles. Because of complications introduced by the presence of ions, the general hypothesis that the external corona of the vesicles is composed of the longer chains, while the shorter chains form the inner corona, which is valid only in mixtures containing only nonionic chains without any additives (no acids or bases) or within a well-defined narrow pH range. In addition to the numerical block lengths and the pH, the solubility of the hydrophilic blocks can also influence the morphology and as well as the interfacial composition of vesicles; as the numerically longer chains become less soluble, they can contract and move to the interior, while the numerically shorter but more soluble chains go to the external corona. A remarkable morphological feature of the pH continuum is that for some compositions vesicles are observed in four distinct pH regions, separated by pH ranges in which other morphologies dominate. The effect of pH and microion content on coil dimensions of the PVP and PAA chains in the block copolymers is most likely responsible for the observed behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".