Preferential Alignment of Incommensurate Block Copolymer Dot Arrays Forming Moiré Superstructures
Bibliographic record
Abstract
Block copolymer (BCP) self-assembly is of great interest as a cost-effective method for large-scale, high-resolution nanopattern fabrication. Directed self-assembly can induce long-range order and registration, reduce defect density, and enable access to patterns of higher complexity. Here we demonstrate preferential orientation of two incommensurate BCP dot arrays. A bottom layer of hexagonal silica dots is prepared via typical self-assembly from a PS-b-PDMS block copolymer. Self-assembly of a second, or top, layer of a different PS-b-PDMS block copolymer that forms a hexagonal dot pattern with different periodicity results in a predictable moiré superstructure. Four distinct moiré superstructures were demonstrated through a combination of different BCPs and different order of annealing. The registration force of the bottom layer of hexagonal dots is sufficient to direct the self-assembly of the top layer to adopt a preferred relative angle of rotation. Large-area helium ion microscopy imaging enabled quantification of the distributions of relative rotations between the two lattices in the moiré superstructures, yielding statistically meaningful results for each combination. It was also found that if the bottom layer dots were too large, the resulting moiré pattern was lost. A small reduction in the bottom layer dot size, however, resulted in large-area moiré superstructures, suggesting a specific size regime where interlayer registration forces can induce long-range preferential alignment of incommensurate BCP dot arrays.
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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".