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    Data-driven algorithm yields three unique ZIFs with high selectivity for greenhouse gas separation

    3 days ago

    A collaborative research effort between UNIST and the Korea Institute of Science and Technology (KIST) has led to the successful synthesis of three novel porous materials by leveraging a data-driven structure prediction algorithm. These newly developed materials, modeled after zeolites, represent metal-organic frameworks (MOFs) with exceptional selectivity in gas separation, particularly for carbon dioxide (CO₂).
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