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作 者:王磊[1] 方桂英[2] 阳庆元[1] WANG Lei;FANG Guiying;YANG Qingyuan(State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China;School of Sciences, Jiangxi Agricultural University, Nanchang 330045, Jiangxi, China)
机构地区:[1]北京化工大学有机无机复合材料国家重点实验室,北京100029 [2]江西农业大学理学院,江西南昌330045
出 处:《化工学报》2019年第3期1135-1143,共9页CIESC Journal
基 金:国家重大基础研究计划项目(2016YFA0201701);中央高校基本科研业务费专项资金(buctrc201727)
摘 要:全球性温室效应形势的日趋严重,迫切需要研究和开发可用于CO_2捕集的高性能材料。对于含有双铜船桨型片段(Cu_2(COO)_4)的MOF材料,因其结构中含有配位不饱和的Cu金属位点,在低压区域的CO_2捕获方面展现出优异的性能。目前,大规模计算筛选工作主要是基于传统的分子力场,无法对此类Cu-MOFs中主客体分子间的相互作用进行准确描述。基于量化计算获得的精确分子力场,利用Monte Carlo分子模拟方法考察了常温常压条件下763个基于Cu-OMS的MOF材料对CO_2存储和CO_2/N_2的分离行为。不仅筛选出潜在的高性能材料,而且揭示出了材料的结构与其性能之间的关系和具有优良性能的材料结构特征,可为面向特定应用的新材料设计和合成提供理论参考。The global greenhouse effect situation is becoming more and more serious, and there is an urgent need to research and develop high-performance materials that can be used for CO 2 capture. Metal-organic frameworks (MOFs) containing an inorganic unit of copper paddle-wheel (Cu 2 (COO) 4 ) have excellent performance in CO 2 capture in low pressure region because of their coordinatively unsaturated Cu sites. However, large-scale computational screening work available so far mainly used conventional molecular force field, which cannot accurately describe the host-guest molecules involved in current study. Thus, on the basis of accurate molecular force fields derived from quantum mechanics calculations, grand canonical Monte Carlo simulation method was adopted to investigate CO 2 storage and CO 2 / N 2 separation behaviors of 763 MOFs with Cu-OMS under ambient conditions. Not only were promising MOFs found through large-scale screening, but the established structure-performance relationships and the structural characteristics of high-performance materials were revealed, providing a theoretical foundation for design and synthesis of new materials for targeted applications. Citation : WANG Lei, FANG Guiying, YANG Qingyuan. Performance of metal-organic frameworks for CO 2 capture from large-scale computational screening[J]. CIESC Journal, 2019, 70(3): 1135-1143.
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