Shear Thinning Behavior of Concentrated Latex Dispersions
Bibliographic record
Abstract
Abstract Summary: Measured viscosity vs. shear rate relationships were analyzed for a wide variety of carboxylated latexes reported in the literature using the theoretical model proposed by one of the authors. The theory contains three main parameters: cm, ko and δ, which are the maximum volume fraction, a particle interaction parameter closely related to the secondary electroviscous effect, and the thickness of the stabilizing surface layer, respectively. It is assumed that sheared dispersions always approach close packing for high volume fractions, i.e. cm = 0.74, and that the shear thinning is entirely due to the energy dissipation associated with hydrodynamic and colloidal particle interactions, which at low shear rates is larger than at high ones (for simple shear flows). The experimental data include those by Laun who measured the viscosity of concentrated latex dispersions over nine order of magnitude of shear rate ranging from 10−3 to 106 s−1, and Chu et al., who prepared several monodisperse latexes and measured the viscosity of individual as well as trimodal blends of these three latexes as a function of shear rate. The viscosity of carboxylated latex is also influenced by surface “hair”, which appears to be closely related to the amount and type of functional monomers, and degree of dissociation of carboxylic acid groups on the latex surface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".