Predictive Modeling of Liquid Density and Surface Tension for Sustainable Aviation Fuels Using Nuclear Magnetic Resonance Atom Types

Prescreening of sustainable aviation fuels (SAFs) is crucial for early stage development and ASTM D4054 evaluation. This study develops models to predict two key properties: temperature-dependent liquid density and surface tension of complex hydrocarbon mixtures. 1H 13C heteronuclear single quantum coherence nuclear magnetic resonance spectroscopy is used to determine atom type compositions. Multiple linear regression models, trained on 1241 liquid density and 1260 surface tension experimental data points, identified seven key atom types and a temperature-dependent term as predictors. Applied to fossil-derived and synthetic fuels, density predictions had an error range of 0.00–5.35%, and surface tension predictions ranged from 0.29–4.41%. The prescreening method proved to be effective for predicting critical fuel properties in early stage SAF development.