Entropic Surface Segregation from Athermal Polymer Blends of Slim and Bulky Polymers
Bibliographic record
Abstract
Experiments, theory, and simulations have revealed an entropic preference at the surface of athermal blends for the component with the shorter statistical segment length. Here, we examined the resulting surface segregation for blends of slim and bulky polymers of equal molecular volume, using Monte Carlo simulations where the polymers are modeled as chains of impenetrable spheres. Comparing to analogous simulations for blends of stiff and flexible polymers [Spencer and Matsen, Macromolecules 2022, 55, 1120], we find that bulkiness has a considerably stronger effect on the surface segregation than stiffness, consistent with self-consistent field theory (SCFT) predictions for worm-like chains [Matsen, J. Chem. Phys . 2022, 156, 184901]. Furthermore, we demonstrate that, under special conditions, this mismatch in strength can lead to anomalous surface segregation of the component with the longer statistical segment length, as predicted by the SCFT.
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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 | 0.000 |
| 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) | 0.001 | 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".