Effects of Diffusion‐Controlled Radical Reactions on RAFT Polymerization
Bibliographic record
Abstract
Abstract The ‘livingness’ of a controlled radical polymerization process such as reversible addition–fragmentation transfer polymerization (RAFT) depends on the rapid deactivation of propagating radicals (the radical addition reaction in RAFT) that suppresses radical termination reactions. However, at high monomer conversions when the polymerization system becomes viscous, polymer chains may experience diffusion limitations and the radical reactions (radical addition and termination) readily become diffusion controlled. The effects of the diffusion‐controlled reactions on the RAFT kinetics and molecular‐weight development are investigated in this work using a modeling approach. It is demonstrated that the diffusion‐controlled radical termination accelerates the polymerization rate and improves the control of polymer molecular weight, while the diffusion‐controlled radical addition also accelerates the rate but broadens the molecular‐weight distribution. This model elucidates the magnitudes and changes for various types of chains involved in the RAFT, i.e., propagating radical chain, adduct radical chain, dormant chain, and dead chain. Polydispersity vs conversion of diffusion‐controlled radical termination (left) and diffusion‐controlled radical addition (right). magnified image Polydispersity vs conversion of diffusion‐controlled radical termination (left) and diffusion‐controlled radical addition (right).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".