Sequential Nanopatterned Block Copolymer Self-Assembly on Surfaces
Bibliographic record
Abstract
Bottom-up self-assembly of high-density block-copolymer nanopatterns is of significant interest for a range of technologies, including memory storage and low-cost lithography for on-chip applications. The intrinsic or native spacing of a given block copolymer is dependent upon its size (N, degree of polymerization), composition, and the conditions of self-assembly. Polystyrene-block-polydimethylsiloxane (PS-b-PDMS) block copolymers, which are well-established for the production of strongly segregated single-layer hexagonal nanopatterns of silica dots, can be layered sequentially to produce density-doubled and -tripled nanopatterns. The center-to-center spacing and diameter of the resulting silica dots are critical with respect to the resulting double- and triple-layer assemblies because dot overlap reduces the quality of the resulting pattern. The addition of polystyrene (PS) homopolymer to PS-b-PDMS reduces the size of the resulting silica dots but leads to increased disorder at higher concentrations. The quality of these density-multiplied patterns can be calculated and predicted using parameters easily derived from SEM micrographs of corresponding single and multilayer patterns; simple geometric considerations underlie the degree of overlap of dots and layer-to-layer registration, two important factors for regular ordered patterns, and clearly defined dot borders. Because the higher-molecular-weight block copolymers tend to yield more regular patterns than smaller block copolymers, as defined by order and dot circularity, this sequential patterning approach may provide a route toward harnessing these materials, thus surpassing their native feature density.
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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 | 0.001 |
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".