Machine learning potential simulations reveal coupled thermodynamic and kinetic control of C2H6/C2H4 retention in a prototypical ultramicroporous MOF.

Abstract

The separation of ethane from ethylene is important in the petrochemical industry but remains highly energy-intensive. How adsorption-site stability and diffusion mechanisms jointly govern C2H6/C2H4 selectivity in ultramicroporous metal-organic frameworks (MOFs) is an important yet underexplored question. Copper quinoline-5-carboxylate (Cu(Qc)2), a prototypical C2H6-selective ultramicroporous MOF with narrow aromatic pore channels, was selected as the model system. Machine learning potentials developed with the DeePMD-kit framework were combined with long-timescale molecular dynamics, OPES enhanced sampling, and DFT calculations to investigate the host-guest interactions and diffusion behavior of C2H6 and C2H4. The results show that the C2H6 forms a more stable adsorption state and exhibits a higher diffusion free energy barrier than C2H4, together with a lower self-diffusion coefficient and a more complex two-step migration pathway. Two-dimensional free energy surfaces indicate that C2H6 experiences stronger local confinement near the pore window. DFT calculations at representative diffusion sites reveal more pronounced site-dependent changes in host-guest interaction strength for C2H6. These results indicate that the stronger retention of C2H6 in Cu(Qc)2 is consistent with both enhanced binding energy and hindered diffusion within the pore channel, underscoring the coupled thermodynamic and kinetic contributions to C2H6/C2H4 separation. The molecule level insights reported here provide a basis for the rational design of ultramicroporous materials with enhanced C2H6/C2H4 separation performance.

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