Polymer Diffusion in PBMA Latex Films Using a Polymerizable Benzophenone Derivative as an Energy Transfer Acceptor
Bibliographic record
Abstract
Fluorescence resonance energy transfer (FRET) measurements were used to monitor polymer diffusion in poly(butyl methacrylate) latex films with a polymer molar mass of M w ≈ 125 000 ( M w / M n = 2.5). These experiments employed the nonfluorescent acceptor chromophore NBen, which allowed faster data acquisition at lower acceptor dye concentration (0.3, 0.5 mol %) than previous experiments with anthracene (1 mol %) as the acceptor. The data were analyzed in two distinct ways. Our traditional simplified approach involved calculating f m values for the quantum efficiencies of FRET (Φ ET ). Apparent diffusion coefficients D app were calculated by making rather severe assumptions about f m . In addition, we carried out mathematical simulations of diffusion which satisfied Fick's laws in a spherical geometry. The concentration profiles of donor and acceptor were introduced into equations that describe the rate for of energy transfer, and donor decay profiles were simulated ( t ). By comparing simulated and experimental decay profiles as a function of sample annealing time, optimum values of the mean diffusion coefficient 〈 D 〉 were obtained. A comparison of the two different methods of data analysis indicates that D app values are larger than 〈 D 〉 values by a factor of 2−4 but track the “true” diffusion coefficients rather well. From the temperature dependence of the diffusion coefficients, we found effective activation energies for diffusion of E a = 33.5 ± 2.5 kcal/mol from D app and 38 ± 5 kcal/mol from 〈 D 〉. These values are very similar to the value of E a = 39 kcal/mol from D app obtained in earlier experiments in which anthracene served as the acceptor chromophore.
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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 | 0.001 |
| 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".