Steady Shear Rheology and Surface Activity of Polymer-Surfactant Mixtures
Bibliographic record
Abstract
Understanding the interactions between polymers and surfactants is critical for designing advanced fluid systems used in applications such as enhanced oil recovery, drilling, and chemical processing. This study examines the effects of five surfactants: two anionic (Stepanol WA-100 and Stepwet DF-95), one cationic (HTAB), one zwitterionic (Amphosol CG), and one non-ionic (Alfonic 1412-3 Ethoxylate), on the steady shear rheology and surface activity of two polymers, namely cationic hydroxyethyl cellulose based polymer (LR-400) and anionic polyacrylamide based polymer (Praestol 2540TR). The polymer-surfactant solutions behave as shear-thinning fluids and follow the power-law model. Anionic surfactants exhibit a strong effect on the rheology of cationic polymer LR-400 solution. The consistency index rises sharply with the increase in surfactant concentration. Also, the solutions become highly shear-thinning with the increase in surfactant concentration. The effects of other surfactants on the rheology of cationic polymer solution are small to modest. None of the surfactants investigated exhibit a strong influence on the rheology of anionic polymer Praestol 2540TR. Only weak to modest effects of surfactants are observed on the rheology of anionic polymers. The surface tension of the polymer-surfactant solution decreases with the increase in surfactant concentration. Zwitterionic surfactant Amphosol CG is found to be most effective in reducing the surface tension at a given concentration in ppm. This surfactant also raises the electrical conductivity of the solution to the largest extent. From the changes in slope of surface tension versus surfactant concentration plots, the approximate values of critical aggregation concentration (CAC) and polymer saturation point (PSP) are estimated.
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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 | 0.000 | 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) | 0.001 | 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".