The Characteristic Curvature (Cc) of Ionic Surfactants Assessed via Small-Angle X-ray Scattering
Bibliographic record
Abstract
The characteristic curvature (Cc), within the hydrophilic–lipophilic difference + net ( H n ) – average ( H a ) curvature (HLD-NAC) framework, is the dimensionless net curvature, − H n · L ( L is the surfactant’s tail length parameter), that a surfactant acquires at the characteristic condition ( T = 25 °C, no added cosurfactants, oil with an equivalent alkane carbon number (EACN) of zero and for ionic surfactants, a total salinity ( S ) of 1 g NaCl/100 mL). A recent article demonstrated the validity of the Cc concept, where H n was assessed via oil and water solubilization radii. Here, we assess H n from the characteristic length (ξ) obtained from the analysis of SAXS profiles of microemulsions produced at semicharacteristic conditions (characteristic condition but varying S ). The predicted relationship, − L · H n,semicharacteristic = Cc bi + bi·ln( S ), was confirmed with the five ionic surfactants explored. The SAXS-assessed Cc (Cc = Cc bi /bi) values are consistent with those obtained from solubilization studies and phase inversion scans. The Cc-SAXS method provides a way to assess the hydrophobicity of ionic surfactants directly, avoiding the bias that could be introduced by cosurfactants in phase inversion studies and minimizing the impact of potential uncertainties in the surfactant volume to area ratio ( v s / a s ) required to calculate the solubilization radii in the solubilization method.
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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".