The HLD‐NAC Model for Mixtures of Ionic and Nonionic Surfactants
Bibliographic record
Abstract
Abstract The HLD‐NAC model has been used as an “equation of state” to predict the properties of microemulsion (μE) systems formulated with either anionic or nonionic surfactants. The model uses the concept of the hydrophilic‐lipophilic difference (HLD) to calculate the chemical potential difference of transferring a surfactant from the oil to the aqueous phase; as a function of formulation variables such as type of surfactant, oil, temperature, electrolyte concentration. The value of HLD is used as a scaling parameter to calculate the net and average curvatures (NAC) of the surfactant at the water/oil interface. These curvatures determine the phase volumes, phase transitions, and solubilization capacity of μEs. In this work, the HLD‐NAC model is extended to nonideal surfactant mixtures of anionic and nonionic surfactants. The phase behavior of limonene μEs formulated with binary mixtures of sodium dihexyl sulfosuccinate with nonionic nonylphenol ethoxylates and alcohol ethoxylates was used to determine the deviations of the HLD from the ideal mixing behavior. The deviations were fitted using a 2‐parameters Margules equation. The results suggests that the deviations in anionic‐rich systems are due to the charge shielding effect of nonionic surfactants, and in nonionic‐rich systems, the deviations seem to be explained by the increase in hydration of the surfactant headgroups due to the presence of anionic surfactants. When these corrections were used to predict the curvature of dioctyl sulfosuccinate‐dodecyl pentaethylene glycol‐heptane μEs, the HLD‐NAC model corrected for the nonidealities reproduced not only the trends but also the actual range of values reported in the literature.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".