Interfacial Mass Transfer in Nitroxide-Mediated Miniemulsion Polymerization
Bibliographic record
Abstract
A mathematical model has been developed to describe the interfacial mass transfer of TEMPO in a nitroxide-mediated miniemulsion polymerization (NMMP) system in the absence of chemical reactions. The model is used to examine how the diffusivity of TEMPO in the aqueous and organic droplet phases, the average droplet diameter and the nitroxide partition coefficient influences the time required for the nitroxide to reach phase equilibrium under non-steady state conditions. Our model predicts that phase equilibrium is achieved quickly (< 1 × 10−4 s) in NMMP systems under typical polymerization conditions and even at high monomer conversions when there is significant resistance to molecular diffusion. The characteristic time for reversible radical deactivation by TEMPO was found to be more than ten times greater than the predicted equilibration times, indicating that phase equilibrium will be achieved before TEMPO has an opportunity to react with active polymer radicals. However, significantly longer equilibration times are predicted, when average droplet diameters are as large as those typically found in emulsion and suspension polymerization systems, indicating that the aqueous and organic phase concentrations of nitroxide may not always be at phase equilibrium during polymerization in these systems. Influence of droplet phase TEMPO diffusivity, DTEMPO,drop, on the predicted organic phase concentration of TEMPO.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".