Prediction of Cloud Point Curves of Alkyl Ethoxylates with the Hydrophilic–Lipophilic‐Difference and Net‐Average‐Curvature (HLD‐NAC) Framework
Bibliographic record
Abstract
Abstract Understanding and predicting cloud point phenomena is important for the formulation of nonionic surfactant systems, and the design of cloud‐phenomena‐associated separation processes. There have been several approaches to fit and predict the cloud point phenomena, in most cases using bulk thermodynamic approaches. In this work, we introduced the hydrophilic–lipophilic‐difference and net‐average‐curvature (HLD‐NAC) as an interfacial (curvature) approach to predict cloud point values at different surfactant concentrations (cloud point curve). The HLD‐NAC method could fully predict the cloud point of alkyl ethoxylate of pure surfactants, typically within 4 °C of the experimental values, using published HLD constants, and the molecular structure of the surfactants. For commercial (polydispersed) surfactants, the same level of accuracy can be achieved if the experimental cloud point at 1 wt.% is used to adjust the HLD values. One additional benefit of using the HLD framework is the ability to predict changes in the cloud point curve with the introduction of electrolytes. While other models can fit the experimental data within 1 °C, the greater uncertainty of the HLD‐NAC (~4 °C) is a reasonable compromise given the simplicity of the approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".