基于高通量计算筛选的金属有机骨架材料甲醛吸附性能

doi:10.6023/A18010026

摘要/Abstract

随着大量新型金属有机骨架（MOFs）吸附材料的出现，传统“试错式”的甲醛吸附剂研究方法具有效率低、周期长、成本高等问题.为实现高性能甲醛净化MOFs的快速研发，采用基于巨正则蒙特卡洛模拟（GCMC）的高通量计算筛选方法对2932种MOFs材料进行了甲醛吸附性能的快速评价.基于高通量计算筛选结果，我们挑选并制备出Y-BTC、ZnCar和Ni-BIC等3种对甲醛有较高吸附量的吸附剂，并采用粉末X射线衍射（PXRD）、比表面积分析（BET）对材料进行了表征.通过甲醛吸附实验，明确了筛选出的MOFs以及参照材料（Cu-BTC、活性炭）在甲醛初始浓度为100 mg/m³条件下的甲醛吸附量分别为0.38、0.25、0.11、0.08、0.06 mol/kg.同时，筛选出的吸附剂还具有良好的甲醛吸附循环利用性能.该结果表明筛选出的Y-BTC、ZnCar和Ni-BIC的甲醛吸附量均高于Cu-BTC和活性炭等参照吸附剂，证明了高通量计算筛选方法在指导甲醛吸附材料开发方面的有效性.

关键词: 金属有机骨架, 高通量计算筛选, 甲醛吸附, 蒙特卡洛分子模拟, 可再生性能

With the rapidly increasing number of reported metal-organic frameworks (MOFs), conventional trial-and-error method is obviously not applicable to the development of high-performance MOFs for formaldehyde adsorption, due to its low efficiency, high cost and long developing period. Thus, high-throughput computational screening (HTCS) strategy based on grand canonical Monte Carlo (GCMC) simulation is proposed to quickly explore the top-performing MOFs with high adsorption capability towards formaldehyde. In this work, the computation-ready experimental (CoRE)-MOF database consisting of 2932 MOF structures carrying density derived electrostatic and chemical (DDEC) charges obtained from density function (DFT) theory calculations, were employed in high-throughput GCMC simulations for formaldehyde adsorption from the air. The structure-property relationship from HTCS revealed that the MOF candidates with high formaldehyde uptakes exhibited small pore sizes, relatively high selectivity and moderate heat of adsorption (Q_st). Afterwards, the top MOFs with both high uptake and selectivity towards formaldehyde were chosen for further experimental evaluation. Three selected MOFs Y-BTC, ZnCar and Ni-BIC were successfully synthesized and characterized by powder X-ray diffraction (PXRD) and BET surface area analysis. In order to validate our HTCS strategy, the representative Cu-BTC and activated carbon (AC) were also adopted as controls. The formaldehyde adsorption test was performed in a sealed container with the formaldehyde concentration of 100 mg/m³ at 298 K. After 24 h adsorption, the formaldehyde uptakes of the adsorbents were obtained according to the concentration changes prior to and after formaldehyde exposure by UV-vis spectrometer. It was found that the adsorption capacities of Y-BTC, ZnCar and Ni-BIC were 0.38, 0.25 and 0.11 mol/kg, respectively, which were remarkably higher than Cu-BTC (0.08 mol/kg) and AC (0.06 mol/kg). The recyclability of the best performer Y-BTC was also verified. These findings open up the possibility of employing HTCS strategy for highly efficient exploration of MOF adsorbents for formaldehyde removal.

Key words: metal-organic frameworks, high-throughput computational screening, formaldehyde adsorption, grand canonical Monte Carlo simulations, recyclability

引用此文

卞磊, 李炜, 魏振振, 刘晓威, 李松. 基于高通量计算筛选的金属有机骨架材料甲醛吸附性能[J]. 化学学报, 2018, 76(4): 303-310.

Bian Lei, Li Wei, Wei Zhenzhen, Liu Xiaowei, Li Song. Formaldehyde Adsorption Performance of Selected Metal-Organic Frameworks from High-throughput Computational Screening[J]. Acta Chim. Sinica, 2018, 76(4): 303-310.

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