This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Reducing carbon dioxide emissions from industrial processes and energy production remains one of the major technological challenges in addressing climate change. Membrane-based gas separation offers an energy-efficient alternative to conventional separation technologies, but identifying membranes that allow gases to pass through rapidly while also separating them effectively has long presented a major materials-design challenge.
Researchers at Koç University have developed a data-driven framework that combines molecular simulations with machine learning to accelerate the discovery of high-performance membrane materials. The approach allowed the researchers to evaluate more than 100,000 material combinations and predict the performance of promising candidates within seconds. The study, conducted by master's student Feride Neva Yüngül and Professor Seda Keskin from Koç University's Department of Chemical and Biological Engineering, was published in Communications Materials.
Most commercial gas-separation membranes are made from polymers because they are inexpensive, easy to process and suitable for large-scale production. However, polymer membranes are constrained by a well-known trade-off between permeability and selectivity. Polymers that transport gases rapidly generally separate them less efficiently, while highly selective polymers tend to restrict gas transport.
One strategy for overcoming this limitation is to embed metal-organic frameworks, or MOFs, within polymer membranes. MOFs are porous crystalline materials constructed from metal ions and organic linkers. Because their pore sizes and chemical properties can be precisely tuned, they can selectively adsorb and transport particular gas molecules.
The resulting materials, known as mixed-matrix membranes, combine the scalability and processability of polymers with the gas-separation capabilities of MOFs. Yet selecting the right MOF-polymer pairing is difficult: More than 150,000 MOF structures have been reported, creating millions of potential combinations with existing polymers. To explore this enormous design space, the researchers paired 8,683 experimentally synthesized and computationally generated MOFs with 12 commercially relevant polymers.
This produced a dataset comprising 104,196 mixed-matrix membrane combinations. Molecular simulations were used to calculate how carbon dioxide, methane, nitrogen and hydrogen interact with and move through each MOF. The resulting data were then used to train machine-learning models that could rapidly predict the permeability of previously unexplored MOF-polymer combinations.
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