Statistical mechanics and phantom network modelling of disordered biopolymer gels
Bibliographic record
Abstract
Disordered biopolymer gels, such as alginate gels, contain disordered amorphous regions as well as ordered regions known as junction zones, the latter playing a paramount role in maintaining the integrity of the gel network. The considerable dimensions of these junction zones and their physical interactions are responsible for distinctive phenomena, such as self-healing, observed in biopolymer gels. Compared to rubber-like materials, where polymer chains are interconnected by chemical cross-links, statistical mechanics modeling of disordered biopolymer gels poses new challenges due to the need to capture the contributions from both the disordered and ordered regions. In the literature, the disordered domains are usually modeled by a collection of random coils, while each junction zone is represented by a rigid rod. Attempt has been made to introduce the two-node coil-rod structure, consisting of a random coil connected to a rigid rod, into classical polymer network models (e.g., the Arruda–Boyce eight-chain model) to predict the mechanical response of the biopolymer gels. Although this approach provides valuable insights and reasonable predictions that agree with experimental data, the interactions between the junction zones and the amorphous regions are significantly simplified. This study aims to extend the two-node coil-rod structure to a four-node coil-rod structure, in which two polymer chains share a junction zone, more explicitly representing chain association induced by physical forces in biopolymer gels. By incorporating statistical mechanics and the phantom network model, the entropy of the system is derived, from which the stress–stretch relationships for the network are obtained. Comparisons are made with the model based on the two-node coil-rod structure, and the conditions for establishing equivalency between the two methods are examined. This work establishes a foundation for developing more advanced network models that incorporates the simultaneous interaction of multiple junction zones, a phenomenon often present in biopolymer gels.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".