COSMO-RS prediction of hydrocarbons EACNs calibrated on the n-alkanes reference scale
Year: 2026
Type: article
Abstract: Surfactant–Oil–Water (SOW) systems are commonly described within the Hydrophilic–Lipophilic Deviation (HLD) framework, in which the Equivalent Alkane Carbon Number (EACN) quantifies oil hydrophobicity. The EACN values are usually determined from phase-behaviour studies (fish diagrams, temperature or salinity scans), which are experimentally demanding. Existing predictive approaches (QSPR, Machine-Learning) are all explicitly calibrated against experimental EACN data. This work proposes a physics-based strategy to predict the EACN of structurally diverse hydrocarbons (linear, branched, cyclic, saturated, unsaturated, aromatic) exclusively from their molecular structures, without using any experimental EACN values as input. The method relies on COSMO-RS calculations of the natural logarithm of the activity coefficient ln γ of the oil in the C 10 E 4 surfactant, which is used as a descriptor of oil penetration into the interfacial surfactant film. For the n-alkane reference series, a linear ACN - ln γ relationship is first established, providing a purely thermodynamic calibration between Alkane Carbon Number and activity coefficient. The EACNs of more complex hydrocarbons are then estimated as the ACN of the n-alkane exhibiting the same calculated ln γ. The predictive power of this model is assessed on a chemically diverse set of 78 hydrocarbons, ranging from C₆ to C₃₀ and including alkanes, alkenes, alkynes and aromatic oils for which reliable experimental EACN values are available. A strong linear correlation is obtained between predicted and experimental EACN (R 2 = 0.97, RMSE = 1.2), indicating that COSMO-RS-based predictions provide a reliable alternative to experimentally determined EACN values.
Source: Journal of Molecular Liquids
Language: English
Subfield: Physical and Theoretical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,480
OpenAlex ID: W7138878774