Current industrial alkane/alkene separations rely on energy-intensive distillation-based technologies, with C2 and C3 separations alone estimated to account for approximately 0.3% of global energy consumption.1,2 The separation of propylene (C3H6) from propane (C3H8) is particularly challenging due to their near-identical boiling points, similar molecular sizes, and extremely close relative volatility, yet it remains critically important given propylene's widespread use as a petrochemical feedstock.3
Metal-organic frameworks (MOFs) — porous materials composed of inorganic metal nodes and organic linkers connected by coordination bonds — offer a promising alternative to conventional separation methods. In this work, we investigate MOF-based membrane separation, where gases are distinguished by differences in their diffusion and adsorption coefficients as they permeate through the framework. Guest molecules with stronger host–guest interactions diffuse more slowly, enabling selectivity. Key performance metrics screened in this work include diffusion coefficient, permeability, and membrane selectivity.
MOFs were sourced from the MOSAEC database.4 An initial geometric filter based on pore-limiting diameter (PLD ≥ 2.9 Å) reduced the working dataset from 106,553 to 28,729 structures. Grand Canonical Monte Carlo (GCMC) simulations were then performed to identify top-performing adsorbers for further study.
To model framework flexibility — which is critical for MOFs such as ZIF-8 and CALF-20 that are experimentally known to separate propane/propylene mixtures — we employ a hybrid interatomic potential approach. Machine learning interatomic potentials (MLIPs), trained on periodic DFT calculations using DeePMD-kit, capture the dynamic flexibility of the MOF host, while classical Lennard-Jones (LJ) potentials describe guest–guest and host–guest interactions.5,6,7 LAMMPS molecular dynamics (MD) simulations are then used to compute guest diffusion. As a proof of concept, this hybrid framework has been validated for CO2 diffusion, demonstrating its ability to recover physically meaningful transport behaviour in flexible MOFs. Extending this methodology to C3 mixtures in ZIF-8 and CALF-20 is currently underway, with the goal of identifying selectivity trends that can guide the design of next-generation membrane materials.

References:

1. Sholl, D. S.; Lively, R. P. Seven Chemical Separations to Change the World. Nature 2016, 532 (7600), 435–437. https://doi.org/10.1038/532435a.
2. Eldridge, R. B. Olefin/Paraffin Separation Technology: A Review. Industrial & Engineering Chemistry Research 1993, 32 (10), 2208–2212. https://doi.org/10.1021/ie00022a002.
3. Chen, Y.; Wu, H.; Yu, L.; Tu, S.; Wu, Y.; Li, Z.; Xia, Q. Separation of Propylene and Propane with Pillar-Layer Metal–Organic Frameworks by Exploiting Thermodynamic- Kinetic Synergetic Effect. Chemical Engineering Journal 2021, 431, 133284–133284. https://doi.org/10.1016/j.cej.2021.133284.
4. Gibaldi, M.; Kapeliukha, A.; White, A.; Luo, J.; Mayo, R. A.; Burner, J.; Woo, T. K. MOSAEC-DB: A Comprehensive Database of Experimental Metal–Organic Frameworks with Verified Chemical Accuracy Suitable for Molecular Simulations. Chemical Science 2025. https://doi.org/10.1039/d4sc07438f.
5. Fan, D.; Naskar, S.; Maurin, G. Unconventional Mechanical and Thermal Behaviours of MOF CALF-20. Nature Communications 2024, 15 (1). https://doi.org/10.1038/s41467-024-47695-6.
6. Wang, H.; Zhang, L.; Han, J.; E, W. DeePMD-Kit: A Deep Learning Package for Many-Body Potential Energy Representation and Molecular Dynamics. Computer Physics Communications 2018, 228, 178–184. https://doi.org/10.1016/j.cpc.2018.03.016.
7. Achar, S. K.; Wardzala, J. J.; Bernasconi, L.; Zhang, L.; Johnson, J. K. Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66. Journal of Chemical Theory and Computation 2022, 18 (6), 3593–3606. https://doi.org/10.1021/acs.jctc.2c00010.