Influence of the surface chemistry on the structural and mechanical properties of silica‐polymer composites
Bibliographic record
Abstract
Abstract Poly(vinyl alcohol) (PVA) composite films filled with nanometric, monodisperse, and spherical silica particles were prepared by the mixing of an aqueous PVA solution and SiO2 colloidal suspension and the evaporation of the solvent. Adjusting the solution pH to 5 and 9 controlled the PVA‐SiO2 interaction. Adsorption isotherms showed a higher PVA/surface affinity at a lower pH. This interaction influenced the composite structure and the particle distribution within the polymer matrix, which was investigated by small‐angle neutron scattering, electron microscopy, and swelling measurements. Most of the mechanical properties could be related to the composite structure, that is, the distribution of clusters within the polymer matrix. The progressive creation of a cluster network within the polymeric matrix as the silica volume fraction increased reduced the extensibility or swelling capacity of the composite. The effect was more acute at a higher pH, at which the surface interaction with PVA was weaker and promoted the interconnection between clusters. © 2003 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 41: 3127–3138, 2003
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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".