Conversion of Bilayers of PS-<i>b</i>-PDMS Block Copolymer into Closely Packed, Aligned Silica Nanopatterns
Bibliographic record
Abstract
Block copolymer (BCP) self-assembly is an effective and versatile approach for the production of complex nanopatterned interfaces. Monolayers of BCP films can be harnessed to produce a variety of different patterns, including lines, with specific spacings and order. In this work, bilayers of cylinder-forming polystyrene-block-polydimethylsiloxane block copolymer (PS-b-PDMS) were transformed into arrays of silica lines with half the pitch normally attained for conventional monolayers, with the PDMS acting as the source for the SiOx. The primary hurdle was ensuring the bilayer silica lines were distinctly separate; to attain the control necessary to prevent overlap, a number of variables related to the materials and self-assembly process were investigated in detail. Developing a detailed understanding of BCP film swelling during solvent annealing, blending of the PS-b-PDMS with PS homopolymer, utilization of a surface brush layer, and adjustment of the plasma exposure conditions, distinct and separate silica lines were prepared. On the microscale, the sample coverage of PS-b-PDMS bilayers was investigated and maximized to attain >95% bilayers under defined conditions. The bilayer BCP structures were also amenable to graphoepitaxy, and thus, dense and highly ordered arrays of silica line patterns with tightly controlled width and pitch were fabricated and distributed uniformly across a Si surface.
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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".