Self-assembled morphologies of diblock copolymers confined in nanochannels: Effects of confinement geometry
Bibliographic record
Abstract
The self-assembly of diblock copolymers confined in channels of various shaped cross sections is studied using a simulated annealing technique with the "single-site bond fluctuation" model. In the bulk, the asymmetric diblock copolymers used in this study form hexagonally packed cylinders with period L0. The cross sections of the confining channels are of different shapes including regular triangles, rectangles, squares, regular hexagons, regular octagons, and ellipses. For a given geometry, the channel size (characterized by one or two lengths) is varied from very small to several times of L0. It is found that the geometry and size of the confining channels have a large effect on the structure and symmetry of the self-assembled morphologies. Multiple packed cylinders with the symmetry of the confining channels are the major morphologies for low-symmetry cross sections such as triangle, rectangle, and square. More complex structures such as helices or stacked toroids spontaneously form when the confining channels are shaped such as a regular hexagon, a regular octagon, or an ellipse. The domain spacing of the self-assembled structures can be altered by the shape and size of the confining channels. Our results are consistent with available experiments. These results indicate that the self-assembled structures of block copolymers can be manipulated by the shape of the confining channels.
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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) | 0.000 | 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 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".