Predicting the Morphology and Viscosity of Microemulsions Using the HLD-NAC Model
Bibliographic record
Abstract
This work focuses on extending the HLD-NAC model to help predict the shape and viscosity of SDHS−toluene−water microemulsions from readily available formulation parameters. To do so, a new shape-based NAC model was introduced which relates the net and average curvatures to the length and radius of microemulsion droplets possessing a hypothesized cylindrical core with hemispherical end caps. Knowing the shape of these droplets, theoretical scattering profiles and maximum hydrodynamic radii were predicted. Furthermore, considering the predicted volume fraction of the dispersed droplets alongside the shape allows for the accurate prediction of the microemulsion viscosity. It was found that treating the microemulsion phase as a dilute suspension of rigid rods yielded predicted viscosities close to the experimental values near the bicontinuous phase transition limits. These correlations were further extended to published experimental data with regard to the viscosities of nonionic surfactant systems. The predicted microemulsion morphology and viscosity may be useful in the design of formulations for nanoparticle synthesis, enhanced oil recovery, and various environmental remediation technologies.
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 | 0.001 |
| 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 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".