The Characteristic Curvature of Ionic Surfactants
Bibliographic record
Abstract
Abstract Characterizing the hydrophilic‐lipophilic nature of a surfactant molecule has been a challenge for colloid scientists and technologists. The hydrophilic‐lipophilic balance (HLB), the packing factor, the phase inversion temperature (PIT) and the natural curvature of the surfactant are all terms that seek to address this issue. In this article we build on the hydrophilic–lipophilic difference concept (HLD) (Salager et al. Langmuir, 16, 5534–5539, 2000) to develop a methodology to determine a characteristic curvature (Cc) for ionic surfactants based on the phase behavior of mixed ionic surfactant microemulsions. In essence, the method consists of evaluating the shift in optimal electrolyte concentration as a function of the mole fraction of the test surfactant in a mixture with a reference surfactant, sodium dihexyl sulfosuccinate (SDHS) and applying the appropriate HLD equation for ionic surfactant mixtures to determine Cc. The values of Cc were determined for a range of surfactants, including sodium dodecyl sulfate (SDS), sodium dodecyl benzene sulfonate (SDBS), sodium naphthenate, and others. The method was also extrapolated to nonionic additives and hydrophilic linkers. It was observed that the calculated values of Cc were similar to those predicted by group contribution models, however the proposed method can be used even for complex surfactant mixtures. Finally, when Cc values were compared to apparent packing factor and HLB values, it was found that Cc is correlated with the apparent packing factor of ionic surfactants, and that Cc correlates with the HLB value for nonionic amphiphiles. The physical interpretation of Cc, and its potential application in the Net‐Average Curvature equation of state for oil‐surfactant‐water systems is discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".