Poly(vinyl alcohol) (PVA) in hydrogels, a molecular perspective
Bibliographic record
Abstract
One of the main goals in the field of regenerative medicine is the construction of complex 3D scaffolds that can repair damaged tissue. The polymer chosen for this study is poly(vinyl) alcohol (PVA), widely used in tissue engineering [1]. PVA can be tailored for mechanical properties and has high-performance degradation kinetics. Its morphology and shape can be easily manipulated to improve vascular conduction and tissue induction. PVA is an excellent candidate for the manufacture of hydrogels with high tissue repair capacity and low cytotoxicity. We studied the water-polymer interaction of the material using a molecular dynamics (MD) approach for the calculation of solvation free energy and other parameters. For the modeling and data analysis, we used the high-performance computational software, Gromacs. Hydrogels were prepared at 10, 20, and 35 WT PVA. The hydrogel morphology and pore size were analyzed by SEM. Pore diameter tended to be larger when swelling in water than in PBS. The end-to-end distance of the MD simulated 30-mer was found to be larger in pure water than in the presence of ions. The trend can partially explain the experimental results of pore size. We also found an important change in solvation free energy when the polymer changes from an open to a closed conformation in water.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".