Monte Carlo and numerical self-consistent field study of systems with end-grafted and free polymers in good solvent
Bibliographic record
Abstract
We present a systematic Monte Carlo and numerical self-consistent field (NSCF) study of thin films consisting of grafted and free polymers in good solvent, for the range of densities found in most experiments. Above the overlap threshold for the grafted polymer, the two approaches agree well. Even at low densities, the agreement is surprisingly good. The NSCF results are also directly compared with experiments. The systematic results are interpreted in the context of the regimes and behavior predicted by scaling and analytic SCF theories. We find that the grafted layer is generally thinner, and the penetration of the free polymer into the grafted layer is generally greater than predicted, and that the overall behavior is not in accord with the earlier theories. We find it useful to introduce and distinguish between two measures of the penetration, and we find that one of them can increase with the concentration of grafted polymer.
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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".