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• W4377137231 abstract "Discovering optimum materials to realize an efficient CH4/N2 mixture is of vital industrial significance but scientifically challenging. To solve this key obstacle, an efficient strategy from theoretical screening to scalable synthesis was thus proposed. A large-scale computational screening was first executed to identify high-performance adsorbents for CH4/N2 separation from the experimental copper metal–organic framework (Cu-MOF) libraries. A series of MOFs with kagomé-type lattice characteristics were computationally defined as candidates with good CH4/N2 selectivity. As a proof-of-concept example, one of the candidates (SIWPAS) was further prepared in an experiment. Intriguingly, the pore sizes can be precisely regulated by altering the degassing temperature to induce the dynamic switch of the initial two-dimensional (2D) framework, further enhancing the separation performance for CH4/N2. The obtained selectivity of 15.0 at 298 K and 1.0 bar is consistent with the simulation prediction value, which is also one of the highest values presently. Meanwhile, the CH4/N2 uptake ratio of 7.5 (298 K and 1.0 bar) and comprehensive separation performance index sorbent selection parameter (Ssp) of 283.0 are higher than those of all materials as yet. Significantly, the scalable continuous synthesis of this MOF was finally realized with the highest space-time yield (STY) of 18982 kg m–3 day–1 for all 2D MOFs with the aid of the process intensification approach based on high gravity technology. As a foremost finding, this work not only greatly shortens the discovery period of high-performance MOF materials but also effectively promotes their practical applications." @default.
• W4377137231 created "2023-05-21" @default.
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• W4377137231 date "2023-05-19" @default.
• W4377137231 modified "2023-10-11" @default.
• W4377137231 title "Discovery of a Scalable Metal–Organic Framework with a Switchable Structure for Efficient CH₄/N₂ Separation" @default.
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• W4377137231 doi "https://doi.org/10.1021/acs.chemmater.3c00350" @default.
• W4377137231 hasPublicationYear "2023" @default.
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