Predicting the Morphology and Viscosity of Microemulsions Using the HLD-NAC Model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it