Effect of Silica as Fillers on Polymer Interdiffusion in Poly(butyl methacrylate) Latex Films
Bibliographic record
Abstract
In this paper, we examine the influence of silica as fillers on polymer interdiffusion in poly(butyl methacrylate) latex films. We carry out fluorescence resonance energy transfer (FRET) measurements on latex films that allow us to follow the extent of polymer diffusion as a function of time after the latex/pigment dispersion dries. In this study, we compare four different types of colloidal silica and discuss how fillers affect the rate of polymer interdiffusion in latex films. The efficiency of energy transfer data for newly formed films indicate that 54 and 73 nm diameter SiO 2 particles have little or no effect on the interfacial area between donor- and acceptor-labeled latex cells, whereas the 16 and 27 nm SiO 2 particles significantly reduce the interfacial area with increasing amounts of filler. The maximum efficiency of energy transfer data indicate that 16 and 27 nm SiO 2 also affect the extent of mixing that can be achieved in 200 h annealing at 60 °C. The rate of polymer interdiffusion in latex films is retarded as one increases the amount of filler, and this rate decreases as the filler size decreases. Our results can be explained with a free volume model that assumes that the surface of the silica particles raises the effective glass transition temperature of the matrix.
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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 | 0.001 |
| 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".