Monte Carlo and Mean Field Study of Diblock Copolymer Micelles
Bibliographic record
Abstract
When small concentrations of A- b -B diblock copolymer are mixed with selective solvent or A homopolymer, they often form spherical micelles with cores comprised of the B blocks. Mean field theory predicts that the core radii scale primarily with the degree of polymerization of the B block as with α ≃ 2/3 or larger, and this is consistent with many experimental data. However, recent experiments on very strongly segregated “crew-cut” micelles indicate a much weaker dependence. In this paper, we study crew-cut micelles using a simple mean field theory and Monte Carlo simulations. The Monte Carlo simulations include the calculation of the system relaxation times, which are used both to determine the simulation times required to reach equilibrium and to examine, at least qualitatively, the variable solvent qualities at which the micelle structures become “frozen in” for different polymer molecular weights. The mean field results for these crew-cut, strongly segregated micelles are consistent with previous mean field calculations, giving α = 0.77 at fixed solvent quality. The Monte Carlo results indicate that nonequilibrium effects result in a weaker power law, as observed experimentally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".