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H2在二维共价有机骨架上吸附的蒙特卡罗模拟研究 被引量:3

The Monte Carlo simulation study on H_2 adsorption in two-dimensional covalent-organic frameworks
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摘要 使用巨正则蒙特卡罗方法预测H2分子在六种具有不同结构和组分的二维共价有机骨架材料(2D COFs)中的物理吸附行为.研究结果表明:(1)在所研究的温度和压力范围内,ILCOF-1表现出最优异的氢存储性能,是目前2D COFs中储氢量最高的材料之一;其它5种COFs的储氢量低于其它2D COFs中储氢量较高的COF-5,-8,-10和TP-COF,而与COF-1,-6和Pc-PBBA COF相差不多.(2)与3D COFs相比,本文所研究的所有COFs的储氢量均低于目前储氢量最高的COF-105和COF-108;除ILCOF-1外,其它5种COFs材料均低于COF-102和COF-103.根据模拟计算的结果,分析了表面积、孔直径和等量吸附热等因素对COFs储氢性能的影响.对COFs储氢能力的强化提出了一些建设性意见. The physisorption properties of H 2 in two dimensional covalent organic frameworks (2D COFs) with different structures and compositions were studied using Grand Canonical Monte Carlo method .The simulated re-sults show that, (1) among all of 2D COFs studied, the ILCOF-1 has the best performance for hydrogen stor-age owing to its highest surface area .Compared with other 2 D COFs studied by other scientific groups , ILCOF-1 is the one of 2D COFs with the highest H2 uptakes.The adsorption amounts of other five 2D COFs are lower than those of COF-5, -8, -10, TP-COF, and are almost the same with those of COF -1, -6, Pc-PBBA COF.(2) Compared to 3D COFs, the adsorption capacities of 2D COFs studied in this paper are lower than those of COF-105 and COF-108.Except for ILCOF-1, the other five 2D COFs have lower adsorption capaci-ties than COF-102 and COF-103.The effects of pore size , surface area and isosteric heats of hydrogen on ad-sorption amounts are considered , which indicate that all of these three factors are important for determining the H2 uptakes.Finally, we put forward some constructive suggestions about how to improve the adsorption capacity of COFs according to the results of our GCMC simulation .This may be useful for further investigation .
出处 《原子与分子物理学报》 CAS CSCD 北大核心 2015年第2期296-302,共7页 Journal of Atomic and Molecular Physics
基金 国家自然科学基金(11247275 11304079) 河南省教育厅自然科学基金(2011B140005) 河南工业大学科技创新人才培育计划(2012CXRC16) 河南省教育厅重点研究基金(13A140195)资助的课题
关键词 氢吸附 共价有机骨架 巨正则蒙特卡罗方法 Hydrogen adsorption Covalent-organic frameworks Grand canonical Monte Carlo simulation
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