Modelling particle growth under saturated and starved conditions in emulsion polymerization
Bibliographic record
Abstract
Abstract Modelling of particle growth under saturated and starved conditions in emulsion polymerization was investigated in order to allow describing the broadening of the particle size distribution and account for the effects of restricted diffusion inside the monomer‐swollen polymer particles. In emulsion polymerization, if particle nucleation and coagulation are avoided, the reaction proceeds as a consequence of radical capture, desorption, and termination reactions inside the particles. Therefore, first, investigation of radical entry and desorption models was done under saturation of polymer particles with monomer, where diffusion parameters are constant. The discrimination criterion of the different models was based on fitting the reaction and the total particle size distribution. Indeed, the broadening of the particle size distribution was found to reveal a dependency of radical capture on the particle size; such an effect would be misestimated if only the mean particle size is considered. Second, the models for starved conditions that account for the variation of radical diffusion inside the polymer particles, such as the gel and glass effects, were investigated with comparison to a wide variety of experimental conditions spanning from weak to strong gelation. The dominating mechanisms as well as the interests of both intervals are highlighted.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".