Rheological Behavior of Acid-Swellable Cationic Copolymer Latexes
Bibliographic record
Abstract
2-Vinylpyridine (2VP) was copolymerized with four different cross-linker densities ranging from 0.05 to 0.31 wt % divinylbenzene (DVB) via aqueous emulsion polymerization to produce a series of submicrometer-sized, lightly cross-linked P2VP latexes. Protonation of the 2VP residues leads to a latex-to-microgel transition due to interchain electrostatic repulsion, as confirmed by dynamic light scattering. The DVB content of these pH-responsive copolymer particles strongly affects their rheological behavior. The particle size and viscosity of the swollen cationic microgels exhibit a maximum at approximately 0.11 wt % DVB. Static light scattering results confirm this density as the minimum amount of DVB required to ensure that all P2VP chains are cross-linked (i.e. that there is no soluble fraction), thus allowing optimal swelling of the microgels. Viscosity studies shows that the solution viscosity of a P2VP microgel at low pH follows two models, depending on its concentration. For volume fractions below 0.30, the P2VP microgels behave as hard spheres, as predicted by the Batchelor equation. For more concentrated P2VP microgels (volume fractions above 0.30), the rheological behavior can be predicted using the Krieger-Dougherty model for strong particle-particle interactions; thus, this semiempirical approach provides a useful description of the aqueous solution behavior of microgel.
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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) | 0.001 | 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".