Adsorption of Anionic–Cationic Surfactant Mixtures on Metal oxide Surfaces
Bibliographic record
Abstract
Abstract This research evaluates the adsorption of anionic and cationic surfactant mixtures on charged metal oxide surfaces (i.e., alumina and silica). For an anionic‐rich surfactant mixture below the CMC, the adsorption of anionic surfactant was found to substantially increase with the addition of low mole fractions of cationic surfactant. Two anionic surfactants (sodium dodecyl sulfate and sodium dihexyl sulfosuccinate) and two cationic surfactants (dodecyl pyridinium chloride and benzethonium chloride) were studied to evaluate the effect of surfactant tail branching. While cationic surfactants were observed to co‐adsorb with anionic surfactants onto positively charged surfaces, the plateau level of anionic surfactant adsorption (i.e., at or above the CMC) did not change significantly for anionic–cationic surfactant mixtures. At the same time, the adsorption of anionic surfactants onto alumina was dramatically reduced when present in cationic‐rich micelles and the adsorption of cationic surfactants on silica was substantially reduced in the presence of anionic‐rich micelles. This demonstrates that mixed micelle formation can effectively reduce the activity of the highly adsorbing surfactant and thus inhibit the adsorption of the surfactant, especially when the highly adsorbing surfactant is present at a low mole fraction in the mixed surfactant system. Thus surfactant adsorption can be either enhanced or inhibited using mixed anionic–cationic surfactant systems by varying the concentration and composition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".