The HLD‐NAC Model for Extended Surfactant Microemulsions
Bibliographic record
Abstract
Abstract It has been confirmed that the structure of the alkyl group of an extended surfactant plays an important role in defining its interfacial properties. Alkyl groups containing a higher degree of β‐branching (C2‐branching) produce microemulsions with a larger characteristic length (ξ, the extent of solubilization in middle phases). This effect is explained on the basis that β‐branching increases the hydrophobicity of the surfactant and decreases the optimal salinity of the microemulsion. Higher salinities produce a dehydration of the surfactant groups that lead to shorter extent of the interactions with the oil and the water. Larger characteristic lengths are desirable if the objective of the formulation is obtaining greater solubilization of oil and water, and lower interfacial tensions. Large characteristic lengths are, in most cases, associated with high interfacial rigidities, which are undesirable if rapid coalescence is required. However, mixtures of branched and linear extended surfactants produce large characteristic lengths and lower interfacial rigidities. The HLD‐NAC model is able to reflect the experimental trends in solubilization of oil and water. The differences between the predictions of the model for the solubilization of oil and water in Type I and II formulations, respectively, highlight the complexities in the conformation of extended surfactants, particularly their PO groups, at oil–water interfaces and the need for advanced scattering techniques to evaluate these conformations.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".