Regulating the self‐assembly of <scp>AB</scp>/<scp>CD</scp> diblock copolymer blends via secondary interactions
Bibliographic record
Abstract
Abstract Designed multiblock copolymers with complex architectures offer unlimited opportunities to obtain novel nanostructured phases, however, their synthesis could be challenging and expensive. An alternative approach to access desired nanostructures is to use blends of block copolymers with simple chain architectures and designed block‐block interactions. We use binary blends composed of AB and CD diblock copolymers as a model system to establish design principles of polymeric blends containing block copolymers. Specifically, we explore the phase behavior of AB/CD blends by using the polymeric self‐consistent field theory to construct phase diagrams of the blends focusing on the sphere‐forming regions in the phase space. We predict the formation of various spherical packing phases composed of either core‐shell‐structured spheres or binary spheres resembling metallic alloys. We demonstrate that the equilibrium morphology can be regulated by adjusting the blend composition and molecular parameters such as block fractions, conformational asymmetry, and segment‐segment interactions. The strategy of using secondary interaction in polymeric blends to control the phase behavior explored in the current study can also be generalized to other soft matter systems.
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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".