Classical Density Functional Theory Study on Liquid–Liquid Interfacial Properties of Methanol– <i>n</i> -Alkane ( <i>n</i> -Hexane, <i>n</i> -Heptane, and <i>n</i> -Octane) Mixtures

Liquid-liquid interfacial tension (LL-IFT) and the associated nanoscale interfacial structure are key to an extraction-based separation process. Here, we apply the perturbed-chain statistical associating fluid theory (PC-SAFT)-based classical density functional theory (cDFT) to predict the LL-IFT, interfacial density, and hydrogen-bonding profiles of methanol-<i>n</i>-alkane (<i>n</i>-hexane, <i>n</i>-heptane, and <i>n</i>-octane) mixtures under atmospheric pressure. Each mixture is modeled by using a single temperature-independent binary interaction parameter. To determine the optimal modeling strategy, we systematically assess six combinations derived from four published PC-SAFT parameter sets for methanol and three nonlocal association functionals (i.e., aFMT, aWDA, and iSAFT). The first three parameter sets incorporated the vapor pressure, saturated liquid density, and vapor-liquid interfacial tension (VL-IFT) into the fitting process, with VL-IFT calculated by aFMT, aWDA, and iSAFT for sets 1, 2, and 3, respectively. Parameter set 4 was optimized exclusively to the bulk phase equilibrium data. Notably, despite their superior accuracy in predicting binary VL-IFT, parameter sets 1-3 do not outperform set 4 in predicting binary LL-IFT. Furthermore, both iSAFT and aWDA show good agreement with experimental LL-IFT data, whereas aFMT consistently overestimates the values across all systems and temperatures, irrespective of the methanol parameters used. Although the optimal combination varies by system, the overall performance of the current cDFT framework demonstrates remarkable precision in reproducing LL-IFT. From a structural perspective, monotonic density and hydrogen-bonding profiles with intersection points have been identified: two for density profiles (one per component) and one for hydrogen-bonding profiles.