Effect of compression on the molecular shape of polymer mushrooms with variable stiffness
Bibliographic record
Abstract
Under confinement, the average shape of a polymer chain is modified in interesting ways. In this work, we discuss how confinement affects the mean geometrical properties of wormlike polymers with variable flexibility and monomer–monomer interaction. Here, we consider a polymer mushroom, i.e., a single chain that is permanently anchored to a flat surface by an end point. Compression is introduced by confining the chains inside an infinite slab with parallel hard walls. Regarding polymer shape, we focus on two large-scale geometrical properties that are not correlated a priori: the chain’s size and its entanglement complexity. Using Monte Carlo simulations, we have analyzed the behavior of these two properties under confinement for a range of potential energy functions. A recurrent pattern of shape transitions emerges, as indicated by changes in the correlation between mean size and entanglements. Our results show that, whereas a flexible polymer with strong self-attraction sustains high compression without deforming, polymers that are either too rigid or too weakly self-attracting are “flattened” by slight compression. Furthermore, we find a general relation between molecular size and entanglements that is valid over a range of polymer models and levels of confinement. We conclude that chain stiffness influences less the compressive behavior of a polymer than chain self-interactions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".