Microemulsion phase behavior of anionic‐cationic surfactant mixtures: Effect of tail branching
Bibliographic record
Abstract
Abstract This research evaluated middle‐phase microemulsion formation by varying the mole ratio of anionic and cationic surfactants in mixtures with four different oils (trichloroethylene, n‐hexane, limonene, and n‐hexadecane). Mixtures of a double‐tailed anionic surfactant (sodium dihexyl sulfosuccinate, SDHS) and an unbalanced‐tail (i.e., doubletailed with tails of different length) cationic surfactant (benzethonium chloride, BCl) were able to form microemulsions without alcohol addition. The amount of NaCl required to form the middle‐phase microemulsion decreased dramatically as an equimolar anionic‐cationic surfactant mixture was approached. Although the mixture of anionic and cationic surfactants demonstrated a higher critical microemulsion concentration (cμc) compared to the anionic surfactant alone, the Winsor Type IV single‐phase microemulsion started at lower surfactant concentrations for the anionic‐cationic mixture than for the anionic surfactant alone. Under optimum middlephase microemulsion conditions, mixed anionic‐cationic surfactant systems solubilized more oil than the anionic surfactant alone. Pretreatment detergency studies were conducted to test the capacity of these mixed surfactant systems to remove oil form fabrics. It was found that anionic‐rich mixed surfactant formulations yielded the largest oil removal, followed by cationic‐rich systems.
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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 | 0.000 |
| 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".