Molecular Dynamics Study on the Temperature Response of a Chitosan-Based Graft Polymer with Different Grafting Densities of Oligo(ethylene glycol) Methacrylate Side Chains
Bibliographic record
Abstract
Chitosan-based thermoresponsive graft polymers serve as promising functional materials for sustainable and intelligent applications, while their temperature-induced phase transition mechanism associated with the two components of the backbone and side chain remains elusive. Here, we perform molecular dynamics simulations from 300 to 390 K to explore the lower critical solution temperature (LCST) behavior of a graft polymer (CMCS- g -OEGMA) with carboxymethyl chitosan (CMCS) as the backbone and oligo(ethylene glycol) methyl ether methacrylate (OEGMA) as the side chain. The simulations show that a high grafting density (>0.5, especially when all structural units are fully occupied) is essential for LCST transition, at which a structural change of single-chain extended-to-collapsed and multichain dispersed-to-aggregated, a conformational transition from syn to anti, and a solvation structure disruption in cage-like first and second shells happen above LCST. The driving effect for this transition comes from the hydrophobic interaction of OEGMA brushes, energetically diminishing the original stabilizing interactions of the exo -anomeric effect, intramolecular hydrogen-bond networks, and steric solvation shells along the backbone, consequently enabling the phase transition stabilized by reformed intramolecular interactions in the rearranged collapsed chain structure.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".