分离(统计)
吸附
化学
化学工程
空气分离
工艺工程
材料科学
有机化学
计算机科学
工程类
机器学习
氧气
作者
Shigen Shen,Feng Xu,Xin Chen,Guang Miao,Zhong Li,Xin Zhou,Xun Wang
标识
DOI:10.1016/j.seppur.2022.122054
摘要
• A new MOF dptz-CuGeF 6 with ultra-microporous structure was synthesized at room temperature. • dptz-CuGeF 6 showed good stability against water vapor and some organic solvents. • Its CO 2 capacity at 15 kPa and 100 kPa reached as high as 2.17 mmol/g and 3.23 mmol/g at 298 K separately. • Its CO 2 /CH 4 and CO 2 /N 2 selectivity reached as high as 80 and 175.5, respectively. • Molecule simulation revealed that the fluorine groups are mainly responsible for its high selectivity of CO 2. Efficient capture of CO 2 from flue gas and removal of CO 2 from biomethane by physical adsorption are in great need. Herein, we report the facile synthesis of a new pillared-layer metal-organic frameworks, dptz-CuGeF 6 for CO 2 separation from the flue gas or the biomethane. The resulting samples were characterized. Structurally, the dptz-CuGeF 6 possess double-interpenetrated frameworks formed by two staggered, independent sql sheets, in which the cations Cu 2+ are coordinated by four dptz ligands, making the dptz-CuGeF 6 exhibit narrow and uniform ultra-micropores of 5.25 Å. Its CO 2 capacity reached 2.17 mmol/g at 15 kPa and 3.23 mmol/g at 100 kPa separately, and its selectivity of CO 2 /CH 4 (50:50) and CO 2 /N 2 (15:85) reached as high as 80 and 175.5, respectively. The stability experiments demonstrated the intact structure of dptz-CuGeF 6 was retained after exposure in moisture air (RH = 50% or RH = 80%) as well as in several polar solvents. Breakthrough experiments confirmed that CO 2 /CH 4 or CO 2 /N 2 binary mixture were efficiently separated. Molecule simulation revealed that both F···C CO2 and multiple C-H···O CO2 interactions in the pores of dptz-CuGeF 6 played an important role in enhancing its selectivity of CO 2 adsorption. The dptz-CuGeF 6 would be promising for CO 2 capture and separation from flue gas or biomethane.
科研通智能强力驱动
Strongly Powered by AbleSci AI