The HLD Framework for Complex Fluids with Polydisperse Nonionic Surfectants
Bibliographic record
Abstract
Polydisperse ethoxylated nonionic surfactants are among the most common surfactants used in the formulation of a variety of products because of their mildness and low toxicity. To design complex fluids such as microemulsions and micelle-laden ionotropic hydrogels, it is important to understand the hydrophilic-lipophilic nature of these surfactants and their impact on the surfactant-oil-water (SOW) and surfactant-water (SW) phase behavior. In this work, this nature was determined via the characteristic curvature (Cc) parameter of the Hydrophilic-LipophilicDifference (HLD) framework. A simple and rapid method based on emulsion stability, was introduced for the determination of Cc of polydisperse surfactant samples. The measured values of Cc were consistent among different methodologies and technicians. The HLD framework, originally designed for SOW systems, was extended to SW systems with the objective of predicting the cloud point (CP) of ethoxylated surfactants. The HLD framework was successful in predicting the CP of pure ethoxylated surfactants, but failed to predict the CP of polydisperse surfactants. A liquid-liquid extraction method was then introduced to remove the more hydrophobic fraction of the polydisperse mixture, which allowed the surfactants to reach the CP expected for pure alkyl ethoxylates. The CP of the ethoxylated surfactants was then shown to dominate the behavior of surfactant-water-polymer (SWP) systems, where the polymer was gellan gum. Micelles of ethoxylated surfactants were laden in gellan gum systems that could produce strong gels in the presence of artificial tear fluid. These formulations allowed for an increased solubilization capacity of dexamethasone in the hydrogel, as well as an increase in the release time from 2 hours (no surfactant) to approximately 2 days (with surfactants). The extended drug release was then explained by the association of micelles to gellan gum repeating units.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".