Oscillatory structural interactions in thin emulsion films containing micelles of ionic surfactant
Bibliographic record
Abstract
We study thin aqueous films sandwiched between two oil phases. The system is stabilized by anionic surfactant. These films exhibit stepwise thinning (stratification), due to the presence of micellar aggregates. By means of dynamic and static light scattering we determine the hydrodynamic diameter, and the aggregation number of the micelles. Using effective micellar volume fraction, we calculate the contribution of the oscillatory stuctural forces to the energy of interaction between the two film surfaces. Adding also the van der Waals and the electrostatic interactions, we are able to predict the contact angles of films which contain one layer of micelles inside. These angles are measured by interferometry, and the obtained experimental values agree very well with the theoretical estimates. It is proved that the oscillatory stuctural energy dominates in the total energy of interaction. The addition of electrolyte leads to smaller contact angles. This is explained by the fact that at higher salt content the effective micellar diameter (and volume fraction) decreases due to shrinkage of the electric double layer around each micelle. Therefore, the magnitude of the oscillatory stuctural energy diminishes, thus reducing the overall attraction and the contact angle. Experiments with batch emulsions confirm that the micelles play a stabilizing role: at higher surfactant concentration the rate of creaming is lower. The repulsive maxima in the oscillatory energy of interaction can serve as potential barriers which impede the process of flocculation and lead to more loosely packed clusters of droplets.
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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 | 0.000 |
| 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) | 0.000 | 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".