Obtaining Curvature‐Based <scp>HLD</scp> Parameters for Single Ionic Surfactants via Small Angle X‐Ray Scattering ( <scp>SAXS</scp> )
Bibliographic record
Abstract
ABSTRACT The hydrophilic–lipophilic difference (HLD) for ionic surfactant microemulsions (μEs) can be interpreted as the curvature normalized by the tail length parameter (L), if it is corrected by a factor “bi”, bi·HLD = HLD curv = HLD bi = bi·ln(S) – k bi ·EACN + Cc bi , with bi and k bi representing the curvature sensitivity to changes in salinity (S, g NaCl/100 mL) and in the equivalent alkane carbon number (EACN) of the oil, respectively; and Cc bi representing the normalized curvature when S = 1% NaCl and EACN = 0. This study determines the core radius ( ξ H ) of drop‐type μEs from a fractal core–shell analysis of Small Angle X‐ray Scattering (SAXS) profiles of μEs obtained with different salinities (S) and oils (EACN), from where HLD curv = −2 L/ ξ H , or + 2 L/ ξ H , for oil‐swollen micelles, or water‐swollen reverse micelles, respectively. A linear regression analysis between HLD curv , ln(S), and EACN yields bi, k bi , and Cc bi . This SAXS‐based HLD curv was tested using sodium dioctyl sulfosuccinate (AOT), sodium laureth sulfate (SLES), and cationic surfactant, dodecyltrimethyl ammonium chloride (DTAC). The bi, k bi , and Cc bi parameters for these surfactants are consistent with those obtained using phase scans and solubilization studies. Various recommendations are offered to help reduce the uncertainty of the fitted bi, k bi , and Cc bi values.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".