A systematic Monte Carlo study of self-assembling amphiphiles in solution
Bibliographic record
Abstract
In this paper, we present a systematic Monte Carlo study of the self-assembly of nonionic, amphiphilic, chainlike molecules in dilute solution. The focus is on the regime in which the molecules form relatively weakly segregated micelles, which are in equilibrium with small submicellar aggregates. We study the size and shape distributions of the aggregates, and the structure of the aggregates’ cores and surfaces. In some cases, spherical micelles, relatively large nonspherical micelles, and submicellar aggregates, all coexist. The size distributions of the spherical micelles are approximately Gaussian, while the nonspherical micelles contribute non-Gaussian tails at relatively large aggregation numbers. The simulation results are interpreted in terms of a simple theory of spherical micelles, and the size distributions are compared with its predictions. For the cases where the agreement is good, we combine the simulations and the theory to calculate the critical micelle concentration as functions of the chain lengths and solvent quality. In cases where there are nonspherical aggregates, the asphericity is quantified using the principal radii of gyration of the micelles, and the size distributions are compared with mean field predictions that account for both spherical and nonspherical aggregates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".