Influence of Ethylene Glycol and Propylene Glycol on Polymer Diffusion in Poly(butyl acrylate-<i>co</i>-methyl methacrylate) Latex Films
Bibliographic record
Abstract
We describe fluorescence resonance energy transfer (FRET) experiments carried out to examine the effect of ethylene glycol and propylene glycol on the early stages of polymer diffusion in poly(butyl acrylate-co-methyl methacrylate) latex films. In our approach, we temporarily arrest the drying process of a wet latex film by sealing the film in a previously cooled airtight sample chamber. This arrests propagation of the drying front and suppresses polymer diffusion during the measurements. We then measure donor fluorescence decays from 0.5 mm diameter spots at various locations on the film. From our analysis, we obtain information about the earliest stages of polymer diffusion as the film is still drying. We also investigate the effect of these glycols on polymer diffusion at longer aging times on predried latex films. Ethylene glycol and propylene glycol retard polymer diffusion at early times immediately after the passing of the drying front but enhance the rate of polymer diffusion at later aging times. This behavior is described quantitatively in terms of free-volume theory and the partitioning of the glycols between the aqueous and polymer phases in the film.
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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".