Calculating molecular weight distributions in emulsion polymerization under conditions of diffusion limited chain transfer
Bibliographic record
Abstract
When highly reactive chain transfer agents with low water solubilities (e.g., long chain thiols) are used in emulsion polymerizations, transport of the chain transfer agent (CTA) from the monomer droplets to the polymer particles can become diffusion limited. Consequently, the concentration of CTA in the particles is lower than expected, resulting in apparent transfer constants that can be much lower than the actual transfer constants obtained from studies with homogeneous systems such as bulk or solution. Furthermore, molecular weights will be greater than those obtained in homogeneous systems with the same overall concentration of CTA. There are currently no techniques or methodologies available for predicting molecular weight distributions when the transport of CTA is diffusion limited. Apparent transfer constants may be used but they are typically restricted to a given system and operating conditions. In this work, we describe how the actual CTA concentration in the polymer particles can be estimated through analysis of instantaneous molecular weight distributions. This information is then used to calculate the cumulative molecular weight distribution during the polymerization. Comparisons with experimental molecular weight distributions validate the essential correctness of the approach, but also highlight potential problems. The extension of the approach to online applications is discussed. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 78: 217–227, 2000
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".