Jens Wagner | Searcharxiv

arXiv2026

Measurements of Diffusion Coefficients of CO$_{2}$ in 1-Butanol with a New Laminar Jet Apparatus and PFG-NMR, Pointing to Interfacial Mass Transfer Effects

A novel laminar jet apparatus (LJA) was constructed for precise gas-liquid mass transfer measurements up to 12 bar, significantly extending the technique's operating window. It was applied to carbon dioxide + 1-butanol between 283 K and 333 K. Corresponding measurements were performed using pulsed field gradient NMR spectroscopy (PFG-NMR), which does not involve interfacial mass transfer. Fick diffusion coefficients from LJA and self-diffusion coefficients from PFG-NMR were compared in the limit of infinite dilution, where both must coincide. Both methods show consistent trends, but LJA data are systematically lower. Experimental errors cannot explain the deviations. We therefore hypothesize an additional gas-liquid interfacial mass transfer resistance. PCP-SAFT combined with density gradient theory predicts high CO$_{2}$ enrichment at the interface, and the deviations correlate with this enrichment. Whether such an interfacial resistance exists and is caused by enrichment remains to be established in future studies.

physics.chem-ph↗

arXiv2026

Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids

Diffusion coefficients are key thermophysical properties for modeling mass transport in liquids, but experimental data are scarce, making reliable prediction methods indispensable. In the present work, we introduce a new method for predicting diffusion coefficients of molecular components at infinite dilution in pure liquid solvents by integrating the Stokes-Einstein (SE) equation with machine learning (ML). Unlike previous ML approaches, the resulting hybrid Enhanced Stokes-Einstein (ESE) model provides strictly physically consistent predictions for diffusion coefficients as a function of temperature across a broad range of binary mixtures. Trained and validated using an extensive compilation of literature data for infinite-dilution diffusion coefficients in binary liquid systems, ESE achieves significantly higher prediction accuracies than the previous state-of-the-art model, SEGWE, while requiring only the SMILES strings encoding of the molecular formulae of the components of interest as additional inputs, which are always available. This simplicity makes ESE broadly applicable, e.g., for process design and optimization. The ESE model and its source code are fully disclosed and are directly accessible via an interactive web interface at https://ml-prop.mv.rptu.de/.

physics.chem-ph↗