A Simplified Methodology to Measure the Characteristic Curvature (Cc) of Alkyl Ethoxylate Nonionic Surfactants
Bibliographic record
Abstract
Abstract This work introduces a simplified methodology for measuring the characteristic curvature (Cc) of commercial alkyl ethoxylate nonionic surfactants using carefully selected reference surfactants and oils that produce rapid and well defined separations in salinity scans. The Cc of the commercial reference surfactants was calculated using optimal salinities (S*) obtained from solubilization parameter curves, from interfacial tensions (for a selected system), and from emulsion stability tests. The latter provided a fast detection of S*, in a matter of minutes. The calibrated Cc of the reference surfactants was subsequently used to measure the Cc of various commercial alkyl ethoxylate surfactants. The combination of mixtures of test and reference surfactants and emulsion stability tests produced reproducible Cc values that could be obtained with simple bottle tests and in a timely manner. The values obtained using this methodology were cross‐checked, and proved to be consistent, when using different combinations of reference surfactants and oils, and when conducted by different individuals. The standard deviation of Cc from these measurements was typically ±0.2 Cc units, but it could be as large as 25 % of the Cc value for highly hydrophilic surfactants. After comparing the values of Cc obtained experimentally with values calculated from nominal structures (via a group contribution model) it became clear that there are differences between these values, likely because of the polydispersity of alkyl ethoxylate surfactants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".