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 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) | 0.000 | 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 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".