Coalescence and film formation of a low molecular weight polyurethane dispersions designed for radiation cure
Bibliographic record
Abstract
The goal of this research was to investigate coalescence and film formation in a polyurethane dispersion (PUD) designed for radiation cure. The recipe for PUD synthesis was taken from a recent patent [US 20220112371A1, April 14, 2022], leading to uniform nanoparticles dispersed in water with N-(2-aminoethyl)-β-alaninate as the stabilizer and with a mean hydrodynamic diameter of 80 nm. The high content of 2-hydroxyethyl acrylate (HEA, 20 wt%) led to a low M n (∼ 2.5 kDa) and a broad molecular weight distribution. Replacing a small fraction of the HEA with donor or acceptor dyes for fluorescence resonance energy transfer (FRET) experiments showed that the HEA was concentrated in a low molecular weight fraction of the polymer. This is an unexpected result. Introducing the dyes by pre-reacting them with the hexamethylene diisocyanate (HDI)-trimer component of the formulation led to a more uniform distribution of the dyes in the sample. We prepared components labeled with phenanthrene (Phen) as the fluorescent donor dye or with 1-(4-nitrophenyl)pyrrolidine (NPP) as the non-fluorescent acceptor dye. FRET experiments conducted on samples in the dispersed state showed that a significant extent of nanoparticle-nanoparticle polymer transfer took place through the water phase. Films formed at room temperature showed a significant amount of energy transfer only 1 h after drying, Φ ET = 69 % for HDI-trimer-labeled samples and Φ ET = 75 % for samples in which the dyes replaced HEA. These experiments indicate the high mobility of the polyurethane chains and substantial polymer diffusion occurs very quickly 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 | 0.000 |
| 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".