Model study for large deformation of physical polymeric gels
Bibliographic record
Abstract
A model for large deformation of polymer gels with physical cross-linking is developed and shown to be in good agreement with experimental stress-strain curves which show strain hardening in intermediate strains followed by strain softening in large deformations near the yield strain. The model takes into account the coil-helix transition equilibrium and allows for the distribution of the end-to-end distance. The gel is considered to be formed by long flexible chains and crystalline zones acting as junctions of the chains. The number of segments contained in a flexible chain is variable due to the equilibrium between the two regions. As the end-to-end distance increases due to the deformation, more and more segments are reeled out from the junction zone. Finally, one end of the chain is librated from the junction and the chain becomes dangling. The appearance of dangling chains causes the strain softening because they cease to contribute to the elasticity. From the parameter dependence of the stress-strain relations, it was found that the yield behavior depends strongly on the distribution of end-to-end distance. The yield strain is approximately given by the ratio of the upper limit of the number of segments and the average end-to-end distance. The standard deviation of the end-to-end distance affects significantly the width of the peak in the stress-strain curve, thus determining the degree of strain softening.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".