Mathematical Modelling of 1,6-Hexanediol Diacrylate Photopolymerization with Spatial Gradients and Film Shrinkage
Bibliographic record
Abstract
Two dynamic models are proposed for the photopolymerization of 1,6-hexanediol diacrylate (HDDA) with bifunctional initiator bis-acylphosphine oxide (BAPO) in the presence of oxygen. The first accounts for spatial variations due to monomer and oxygen diffusion, while the second addresses spatial variations and film shrinkage. These partial-differential-equation (PDE) models predict overall vinyl-group conversion as well as time- and spatially-varying concentrations of monomer, initiator, oxygen, pendant vinyl groups and seven types of radicals. Measured diffusion coefficients for monomer, oxygen and initiator, provided by Canon Production Printing, are used in the models. Parameter estimation is performed using overall vinyl-group conversion data from Canon Production Printing, which were obtained using Fourier Transform Infrared (FTIR) spectroscopy for a range of operating conditions of film thicknesses (8-17 μm), BAPO levels (1-4 wt%) and light intensities (200-6000 W/m^2). The first model, which accounts for spatial variations but ignores shrinkage, gives reliable predictions for runs with high BAPO levels (4 wt%) and light intensities (≥5000 W/m^2). Model predictions are not accurate for runs conducted using low BAPO levels (1 wt%), indicating that some model parameters may be inaccurate. As predicted by the second model, shrinkage has a noticeable influence on the model predictions, where a ~9% discrepancy is observed between predictions of overall vinyl-group conversions obtained from the models with and without shrinkage. Prediction discrepancies are larger for simulated experiments involving thin films (8 μm) or low light intensities (1200 W/m^2). In future, it will be important to re-estimate the kinetic parameters, using the shrinkage model, so that accurate model predictions can be obtained over a wide range of operating conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".