Poly(divinylbenzene) Microspheres as an Intermediate Morphology between Microgel, Macrogel, and Coagulum in Cross-Linking Precipitation Polymerization
Bibliographic record
Abstract
Precipitation copolymerizations of mixtures of divinylbenzene-55 (DVB55) and 4-methylstyrene at total monomer loadings of 4 vol % were conducted in solvent mixtures comprised of mixtures of methyl ethyl ketone and heptane. The experimental compositions hence formed a two-dimensional matrix where the actual DVB content varied from 0 to 55% and the solvent composition varied from 0 to 100 vol % MEK. Four distinct polymer morphologies including microspheres, microgels (and soluble polymer), macrogel, and coagulum were observed and are reported in form of a morphology map superimposed on the above compositional map. The structures of these four polymer architectures are described, and the effects of both DVB concentration and solvency on the transitions between morphology domains are discussed. The portion of reaction volume occupied by the polymer formed decreases with both decreasing solvency and increasing DVB concentration. These results indicate that the microspheres are formed, in part, by an internal contraction which is caused by both the marginal solvency of the continuous phase and the cross-linking of the polymer network. This contraction is likely progressive, supporting the presence of a lightly cross-linked corona around the microspheres which acts as an in-situ steric stabilizer layer.
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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 | 0.000 |
| 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".