膜
气体分离
材料科学
石墨烯
化学工程
氧化物
分离(统计)
基质(化学分析)
金属有机骨架
金属
纳米技术
复合材料
有机化学
吸附
化学
计算机科学
生物化学
工程类
冶金
机器学习
作者
Long Feng,Qiuning Zhang,Jianwen Su,Bing Ma,Yinji Wan,Rui‐Qin Zhong,Ruqiang Zou
出处
期刊:Nanomaterials
[Multidisciplinary Digital Publishing Institute]
日期:2023-12-21
卷期号:14 (1): 24-24
被引量:11
摘要
MOF-74 (metal–organic framework) is utilized as a filler in mixed-matrix membranes (MMMs) to improve gas selectivity due to its unique one-dimensional hexagonal channels and high-density open metal sites (OMSs), which exhibit a strong affinity for CO2 molecules. Reducing the agglomeration of nanoparticles and improving the compatibility with the matrix can effectively avoid the existence of non-selective voids to improve the gas separation efficiency. We propose a novel, layer-by-layer modification strategy for MOF-74 with graphene oxide. Two-dimensional graphene oxide nanosheets as a supporting skeleton creatively improve the dispersion uniformity of MOFs in MMMs, enhance their interfacial compatibility, and thus optimize the selective gas permeability. Additionally, they extended the gas diffusion paths, thereby augmenting the dissolution selectivity. Compared with doping with a single component, the use of a GO skeleton to disperse MOF-74 into Pebax®1657 (Polyether Block Amide) achieved a significant improvement in terms of the gas separation effect. The CO2/N2 selectivity of Pebax®1657-MOF-74 (Ni)@GO membrane with a filler concentration of 10 wt% was 76.96, 197.2% higher than the pristine commercial membrane Pebax®1657. Our results highlight an effective way to improve the selective gas separation performance of MMMs by functionalizing the MOF supported by layered GO. As an efficient strategy for developing porous MOF-based gas separation membranes, this method holds particular promise for manufacturing advanced carbon dioxide separation membranes and also concentrates on improving CO2 capture with new membrane technologies, a key step in reducing greenhouse gas emissions through carbon capture and storage.
科研通智能强力驱动
Strongly Powered by AbleSci AI