Control of Morphology and Corona Composition in Aggregates of Mixtures of PS-<i>b</i>-PAA and PS-<i>b</i>-P4VP Diblock Copolymers: Effects of Solvent, Water Content, and Mixture Composition
Bibliographic record
Abstract
The morphologies and corona compositions in aggregates of mixtures of PS-b-PAA and PS-b-P4VP diblock copolymers are influenced by controllable assembly parameters such as water content, block copolymer molar ratios, and solvent effects as well as the hydrophilic block lengths and block length ratios. All these factors can affect the morphology of the aggregates as well as their corona composition, the latter especially in vesicles, where two interfaces are involved. The morphologies and corona compositions of the aggregates were investigated by transmission electron microscopy and electrophoretic mobility, respectively. They depend, to a large extent, on the solubility of P4VP and PAA in the given organic solvent (e.g., DMF, THF, or dioxane), which influences the coil dimensions of the hydrophilic chains. The water content affects both the size and the shape of the block copolymer aggregates as well as the corona composition. Water acts as a precipitant for the hydrophobic block in the common solvent and, therefore, its progressive addition to the solution changes the interaction parameter with the hydrophobic block. The block copolymer molar ratio has an effect on both the morphology and the corona composition of the aggregates. With increasing PS-b-P4VP content in the mixture, the morphology transforms gradually from large compound micelles (LCMs), through coexistence of LCMs and small spherical micelles (SSMs), and eventually to vesicles. As expected, the corona composition of the aggregates is also affected by the block copolymer molar ratio, and changes progressively from pure PAA to a mixture of PAA and P4VP and to pure P4VP with increasing PS-b-P4VP content. It is clear that the use of mixtures of the soluble chains offers the opportunity of fine-tuning the corona composition in block copolymer aggregates under assembly conditions.
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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".