Morphology of ionic microemulsions: comparison of SANS studies and the net-average curvature (NAC) model
Bibliographic record
Abstract
Microemulsion (µE) literature presents numerous scattering studies (neutron, X-ray, light) of ionic and nonionic surfactants that have elucidated the morphological transitions that µEs experience upon changes in formulation conditions such as electrolyte concentration and temperature. Unfortunately, up to now, there is no way to predict the morphology of these µEs, and only after the µE is prepared can the morphology be determined using scattering techniques. In this work we compare the average curvature and drop size predicted by the net-average curvature (NAC) model to the Porod radii and characteristic length obtained from neutron scattering of toluene µEs prepared with sodium dihexyl sulfosuccinate (SDHS) and electrolyte. While the drop sizes predicted by the NAC model do not exactly correspond to the aggregate size obtained after applying a Porod analysis of the SANS profiles, the inverse of the average curvature does match the characteristic size obtained from SANS. This observation is consistent with previous comparisons made for nonionic µEs. The difference between the drop size predicted by the NAC model (that matches the solubilization curves) and SANS morphology suggests that the area per molecule of the surfactant (in contact with the internal phase) changes with the curvature of the system. The area per molecule obtained from Porod plots of Type I and II µEs and from the analysis of the scattering profiles of film-contrast Type III µEs show that as the system approaches net zero curvature the area per molecule increases to a maximum value. The data presented in this work suggests that the NAC model can be used to predict essential elements of the morphology of µEs, which may help in the design of µE-based templated structures (nanoparticles, nanoporous materials, etc.).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".