Homopolymers as Structure-Driving Agents in Semicrystalline Block Copolymer Micelles
Bibliographic record
Abstract
A simplified hierarchical self-assembly strategy is presented in which homopolymer additives are used to manipulate the crystallization-driven self-assembly of block copolymer micelles in selective media. By first incorporating the appropriate homopolymer chains within the micelle core, the system then evolves passively to yield crystalline platelets. These lamellae may be considered as self-assembled analogues of the traditional polymeric single crystal, which can be challenging or laborious to obtain otherwise. Used here as the test systems are micelles bearing polycaprolactone as the crystalline subphase in water and a mixed hydrophilic corona of poly(ethylene oxide) and poly(acrylic acid) the composition of which was varied methodically. Comicellization with homo-PCL has no influence at first; instead, the assemblies undergo morphological changes hierarchically, which were probed by electron microscopy and light scattering measurements. For all materials, the final product is consistently lamellar and micrometer-sized; however, lamellar shape variations are encountered as the stabilizing corona is altered. Such lamellae are unexpected based on the composition of most copolymers used here. The phenomenon also depends highly on the nature of the homo-PCL additive. A possible source for the activity of the homo-PCL is suggested, which also provides a strong basis to adapt the strategy for other crystalline materials.
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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) | 0.000 | 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".