Kinetics of lamellar formation on sparsely stripped patterns
Bibliographic record
Abstract
Chemical epitaxy based on the self-assembly of block copolymers is viewed as a promising technique to achieve ordered patterns on a large scale. Herein, we study the kinetics of lamellar formation of block copolymers under the direction of sparsely stripped patterns using cell dynamics simulations of the time-dependent Ginzburg-Landau theory. First, a scaling law is unveiled with the ordering time of lamellae, tp, with respect to the multiples between the periods of lamellae and stripe patterns, which is consistent with the power law evolution of the correlation length existing in the bulk phase of lamellae. Second, the tolerative windows of perfect order, with deviation from integer multiples, are also estimated from the aspect of kinetics. The results of the ordering time and tolerative windows are of great interest for relevant experiments or applications. Finally, a two-stage evolution is explored during the pattern formation of chemical epitaxy by probing into the evolution of defects, which is of fundamental interest for us to understand the coarsening kinetics of block copolymers under the direction of chemical patterns.
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.
How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".