Compartmentalization of Reverse Atom Transfer Radical Polymerization in Miniemulsion
Bibliographic record
Abstract
Compartmentalization of an atom transfer radical polymerization (ATRP) miniemulsion has been experimentally observed to reduce the overall polymerization rate, primarily by increasing the rate of deactivation due to the confined space effect, and compartmentalization was also found to improve the control over the polymerization and to reduce the final polydispersity index of the polymer. Compartmentalization of an ATRP system requires that the probability of having two active chains in one particle becomes sufficiently low that the particle size influences the number of active radicals (through segregation and confined space effects). This probability is determined by both the particle volume and the number of polymer chains within each particle. For a given number of chains, compartmentalization effects become evident only when the number of reactants (active polymeric radicals and the deactivating species CuBr 2 −tris[2-di(2-ethylhexyl acrylate)aminoethyl]amine (EHA 6 TREN)) within each particle becomes limited by decreasing particle volume. Alternatively, for a given particle volume compartmentalization effects become evident when reactants (active polymeric radicals and the deactivating species CuBr 2 −EHA 6 TREN) become limited by decreasing chain number. The difference between a conventional free radical polymerization and ATRP is highlighted by the opposing impact that compartmentalization has on the kinetics of the polymerizations. In a conventional system, segregation effects cause an increase in the polymerization rate, while the confined space effect dominates the kinetics in ATRP and results in a decrease in rate.
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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 | 0.001 |
| 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".