Field-theoretic simulations of bottlebrush copolymers
Bibliographic record
Abstract
Traditional particle-based simulations struggle with large bottlebrush copolymers, consisting of many side chains grafted to a backbone. Field-theoretical simulations (FTS) allow us to overcome the computational demands in order to calculate their equilibrium behavior. We consider bottlebrushes where all grafts are symmetric diblock copolymers, focusing on the order-disorder transition (ODT) and the size of ordered domains. Increasing the number of grafts and decreasing the spacing between them both raise the transition temperature. The ODT and lamellar period asymptotically approach constants as the number of grafts increases. As the spacing between grafts becomes large, the bottlebrushes behave like diblock copolymers, and as it becomes small, they behave like starblock copolymers. In between, the period increases, reaching a maximum when the spacing is approximately 0.35 times the length of the grafts. A comparison of FTS with mean-field calculations allows us to assess the effect of compositional fluctuations. Fluctuations suppress ordering, while having little effect on the period, as is the case for diblock copolymers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".