Effect of Polymer Concentration on the Rheology and Surface Activity of Cationic Polymer and Anionic Surfactant Mixtures
Bibliographic record
Abstract
The effects of polymer concentration on rheology, surface tension, and electrical conductivity of polymer–surfactant mixtures are investigated experimentally. The polymer studied is a cationic quaternary ammonium salt of hydroxyethyl cellulose, and the surfactant used is anionic sodium lauryl sulfate. The polymer concentration is varied from 1000 to 4000 ppm, and the surfactant concentration varied from 0 to 500 ppm. Polymer concentration affects the properties of the mixtures substantially. At a given surfactant concentration, the consistency of the polymer–surfactant mixture rises sharply with the increase in polymer concentration. The mixture also becomes more shear-thinning with the increase in polymer concentration. The surface tension decreases substantially, and the electrical conductivity increases with the increase in polymer concentration at a fixed surfactant concentration. At a given polymer concentration, the consistency index generally exhibits a maximum and the surface tension exhibits a minimum at some intermediate surfactant concentration. With the increase in polymer concentration, the maximum in the consistency index and the minimum in surface tension shift to higher surfactant concentrations. Although the exact mechanisms are not clear at present, a possible explanation for the observed initial changes in rheological and surface-active properties of polymer–surfactant mixtures with the addition of surfactant is charge neutralization and entanglement of polymer chains. At high surfactant concentrations, recharging and disentanglement of polymer chains probably take place.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".