Aggregation Behavior of Nano-Silica in Polyvinyl Alcohol/Polyacrylamide Hydrogels Based on Dissipative Particle Dynamics
Bibliographic record
Abstract
Due to the aggregation behavior of nano-silica in aqueous solution, the use of nano-silica without surface modification for synthesizing hydrogels is still a challenging task. This paper presents our study on the use of dissipative particle dynamics simulations to discover the aggregation behavior of nano-silica in polyvinyl alcohol (PVA)/polyacrylamide (PAM) blended hydrogels. By simulations, we aimed at investigating the effects of such factors as nano-silica content, polymer component ratio, temperature and shear rate on the aggregation behavior of nano-silica in terms of the mesoscopic morphologies and the relative concentration distribution functions. Our results reveal that the dispersion of nano-silica is seen if the nano-silica content is increased to 1.5%, and the aggregation of nano-silica becomes noticeable in blended hydrogels with an increase in the nano-silica content. This finding agrees well with the experimental results obtained by means of scanning electron microscopy. Furthermore, it is also found that the dispersion of nano-silica becomes more uniform with an increase in PAM content, temperature and shear rate. These findings greatly enrich our understanding of the aggregation behavior of nano-silica in PVA/PAM blended hydrogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".