This work introduces a simplified methodology for measuring the characteristic curvature (Cc) of commercial alkyl ethoxylate nonionic surfactants using carefully selected reference surfactants and oils that produce rapid and well defined separations in salinity scans. The Cc of the commercial reference surfactants was calculated using optimal salinities (S*) obtained from solubilization parameter curves, from interfacial tensions (for a selected system), and from emulsion stability tests. The latter provided a fast detection of S*, in a matter of minutes. The calibrated Cc of the reference surfactants was subsequently used to measure the Cc of various commercial alkyl ethoxylate surfactants. The combination of mixtures of test and reference surfactants and emulsion stability tests produced reproducible Cc values that could be obtained with simple bottle tests and in a timely manner. The values obtained using this methodology were cross‐checked, and proved to be consistent, when using different combinations of reference surfactants and oils, and when conducted by different individuals. The standard deviation of Cc from these measurements was typically ±0.2 Cc units, but it could be as large as 25 % of the Cc value for highly hydrophilic surfactants. After comparing the values of Cc obtained experimentally with values calculated from nominal structures (via a group contribution model) it became clear that there are differences between these values, likely because of the polydispersity of alkyl ethoxylate surfactants.

The hydrophobicity of surfactants has been described through different concepts used to guide the formulation of surfactant-water (SW) and surfactant-oil-water (SOW) systems. An integrated framework of hydrophobicity indicators could provide a complete tool for surfactant characterization, and insights on how their relationship may influence the overall phase behavior of the system. The hydrophilic-lipophilic difference (HLD) and the characteristic curvature (Cc) parameter, included in the HLD, have been shown to correlate with different hydrophobicity indicators including the hydrophilic-lipophilic balance (HLB), packing factor (Pf), phase inversion temperature (PIT), spontaneous curvature (Ho), surfactant partition (K(o-w)), and the critical micelle concentration (CMC). This work aims to investigate whether the HLD can further describe a concomitant hydrophobicity parameter, the cloud point (CP) of alkyl ethoxylates. After applying group contribution models to calculate the Cc of monodisperse (pure) nonionic alkyl ethoxylates, a linear correlation between the calculated Cc and the CP was observed for pure surfactants with 8 ethylene oxide (EO) units or less. Furthermore, using an apparent equivalent alkane carbon number (EACN) to represent the hydrophobicity of the micelle core, the HLD equation was capable of predicting cloud point temperatures of pure alkyl ethoxylates, typically within 5 °C. Polydisperse surfactants did not follow the linear CP-Cc correlation found for pure surfactants. After treating polydisperse samples using a liquid-liquid extraction procedure used to remove the most hydrophobic components in the mixture, the resulting treated surfactants fell in the correlation line of pure alkyl ethoxylates. A closer look at the partition behavior of these treated surfactants showed that their partition, Cc and cloud point are dominated by the most abundant ethoxymers in the treated surfactant. The HLD also predicted the cloud point depression of treated surfactants with increasing sodium chloride concentration. This work shows how the HLD framework could be extended to predict the behavior of SW systems.

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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