Effect of surfactant concentration on the hydrophobicity of polydisperse alkyl ethoxylates
Bibliographic record
Abstract
Abstract The effect of ethylene oxide number (EON) polydispersity on the phase behavior of alkyl ethoxylates has been well documented in the surfactant literature. These previous studies show that polydisperse alkyl ethoxylates appear more hydrophilic as the surfactant concentration decreases or as the oil‐to‐water ratio increases. This becomes a troubling issue considering that most surfactant formulations undergo dilution during use, and they experience a wide range of water‐to‐oil volume ratios. Within the hydrophilic–lipophilic difference framework, the surfactant hydrophobicity is assessed via the sigma (σ) term (also known as the characteristic curvature or Cc). In this work, the effect of surfactant concentration on the apparent value of sigma (σapp) is evaluated as a function of surfactant concentration. The experimental observations are then explained using a bifunctional model for alkyl ethoxylates that consider the dual nature of polar oils (free alcohol and low EON ethoxymers) as surfactants and as oil components. A segregation‐based model and a partition‐based model are implemented to account for the distribution of the ethoxymers in the surfactant pseudophase and the oil phase. Combining these distribution models with the bifunctional model and a group contribution model for sigma, one can predict the σ term versus surfactant concentration for a given water/oil ratio, starting from the EON distribution of the surfactant. The practical applications of the model are discussed.
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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 | 0.002 |
| 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) | 0.001 | 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".