By leveraging a pressure-driven separation mechanism rather than energy-intensive phase changes, organic solvent nanofiltration (OSN) has emerged as a promising alternative to heat-intensive separation processes such as traditional distillation.
1 This approach offers the potential for significant reductions in energy consumption, carbon emissions, and physical footprint across a range of sectors, including oil refining, chemical and pharmaceutical manufacturing, and bioprocessing industries. To realize this potential, solvent-resistant membranes that combine high selectivity for target molecules with efficient solvent transport are essential.
2 In this study, six commercially available OSN membranes were evaluated using a nine-component hydrocarbon mixture as a model crude oil feed to systematically investigate the relationships among separation performance, membrane physicochemical properties, and solvent characteristics under varying transmembrane pressures. Among the evaluated membranes, a clear trade-off between solvent permeance and solute selectivity was observed. Specifically, the Evonik Puramem Selective membrane demonstrated the highest selectivity for key molecules (i.e., Cp/Cr < 0.5 for compounds with molecular weights > 200 g/mol) but exhibited the lowest permeance (~0.4 kg·h
-1·m
-2·bar
-1). In contrast, the Borsig oNF3 membrane showed the lowest selectivity for the same compounds (i.e., Cp/Cr ≈ 0.9) while obtaining the highest permeance (~4.4 kg·h
-1·m
-2·bar
-1). In addition, scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared (FTIR) spectroscopy, and contact angle measurements were used to identify property–performance relationships. Specifically, the results revealed how the characteristics of silicone polymer-based active layers, including such as surface and cross-sectional morphology, chemical composition, and hydrophilicity/hydrophobicity, are related to key performance metrics. In summary, this screening study provides a comprehensive dataset to guide the selection of commercially available OSN membranes for hydrocarbon separations and offers valuable insights into the relationships among membrane properties, solvent characteristics and separation performance.
Reference:
(1) Xu, Q.; Gao, J.; Feng, F.; Chung, T.-S.; Jiang, J. Synergizing Machine Learning, Molecular Simulation and Experiment to Develop Polymer Membranes for Solvent Recovery. Journal of Membrane Science 2023, 678, 121678. https://doi.org/10.1016/j.memsci.2023.121678.
(2) Li, S.; Dong, R.; Musteata, V.-E.; Kim, J.; Rangnekar, N. D.; Johnson, J. R.; Marshall, B. D.; Chisca, S.; Xu, J.; Hoy, S.; McCool, B. A.; Nunes, S. P.; Jiang, Z.; Livingston, A. G. Hydrophobic Polyamide Nanofilms Provide Rapid Transport for Crude Oil Separation. Science 2022, 377 (6614), 1555–1561. https://doi.org/10.1126/science.abq0598.
