Effects of the Hydration States of Water Molecules on the Mechanical Properties of Dual Movable Cross-Linked Polymeric Gels
Bibliographic record
Abstract
Hydrogels are widely studied in basic science and applications under aqueous conditions. The material properties of the hydrogels are significantly influenced by the water content. As the water content increases, the hydrogels become more brittle. However, the precise relationship between the hydration state and the mechanical properties remains unclear. Intermediate water (IMW), which refers to absorbed water, plays a crucial role in influencing the biocompatibility and antifouling properties of these materials. In this study, the correlation between IMW and the mechanical properties of hydrogel was investigated by using dual movable cross-network (DC) gels. The DC gels feature knitted structures combining hydrophilic and hydrophobic polymers to regulate their hydration state. The mechanical properties of the DC gels generally decreased with an increasing amount of hydrophilic polymers due to the higher water content. Interestingly, certain compositions of the DC gels exhibited superior mechanical properties. Spectral, structural, and thermal analysis suggest that IMW contributes to cross-linking between polymer networks through hydrated water molecules, thereby enhancing the mechanical properties of the DC gels. This study highlights the critical role of water molecules in affecting the mechanical properties of the hydrogels. This study offers important insights into the interactions between polymers and water molecules, thereby contributing to the rational design of water-responsive functional materials.
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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 | 0.000 |
| 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".