Microphase and Macrophase Separations in Binary Blends of Diblock Copolymers
Bibliographic record
Abstract
Phase behavior of blends of two AB diblock copolymers, with the long one at relatively strong segregation, is studied using the self-consistent field theory, focusing on the effect of compositions of the two block copolymers and their length ratio. In order to carry out extensive calculations on the large parameter space, a unit-cell approximation is employed, in which the mean-field equations are solved using a Bessel function expansion. Phase diagrams are constructed for four typical series of blends by comparing the free energies of the different ordered phases including lamellae, cylinders, and spheres. The results reveal that the competition between macro- and microphase separation leads to complex phase behavior. When the length ratio of the two block copolymers is small, the short copolymers tend to segregate to the A/B interfaces, inducing multiple order−order phase transitions including reentrant phase transitions in some blends. When the length ratio of the two diblock copolymers is sufficiently large, macrophase separation may take place. The predicted phase diagrams are compared with available experiments. Density profiles of typical ordered structures are presented to understand the self-organization of the polymer chains. The energetics of the blends is introduced to account for the appearance of the macro- and microphase separations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".