Utilizing Machine Learning to Optimize Metal-Organic Framework-Derived Polymer Membranes for Gas Separation

Metal-organic frameworks (MOFs) have gained substantial attention as promising materials for gas separation membranes due to their exceptional porosity, tailorability, and functionalizability. In this study, we present a novel approach to further enhance the properties of porous polymer membranes emerging from MOFs through crosslinking of the organic linker molecules and subsequent metal-atom removal. To ensure reproducibility of the multi-step synthesis process and high quality of the resulting polymeric membranes, we automated the process and followed a machine learning optimization approach. The high-quality MOF-thin films (SURMOFs) were prepared in a layer-by-layer fashion directly on gold-coated porous alumina substrates. This direct synthesis proved crucial to preserve the structural integrity of the membranes and thus avoiding defect formation caused by a substrate-transfer process, which is usually required when advanced materials are used to fabricate a membrane. The initial SURMOF membrane exhibits moderate gas separation performance, once crosslinked, its gas selectivity could be significantly enhanced although with the compromise of lower gas permeance. Interestingly, once we removed the metal centers and thereby converted the SURMOF into a purely organic polymeric membrane, the membrane gas permeance could be restored almost to its initial condition while preserving the enhanced selectivities. In particular, the resulting polymeric membrane outperforms most commercially available polymer membranes for H2 /CO2 gas separation. This research outlines a promising approach to employ MOFs as template in the generation of advanced polymer membranes for various gas and liquid phase separation applications.