Sequential Nanopatterned Block Copolymer Self-Assembly on Surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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 it