Advances in adsorptive separation of benzene and cyclohexane by metal-organic framework adsorbents

环己烷 化学 金属有机骨架 吸附 选择性 蒸馏 化学工程 有机化学 催化作用 工程类
作者
Soumya Mukherjee,Debobroto Sensharma,Omid T. Qazvini,Subhajit Dutta,Lauren K. Macreadie,Sujit K. Ghosh,Ravichandar Babarao
出处
期刊:Coordination Chemistry Reviews [Elsevier BV]
卷期号:437: 213852-213852 被引量:156
标识
DOI:10.1016/j.ccr.2021.213852
摘要

The chemical industry represents ca. 7% of the global GDP and 40% of its immense energy footprint stems from the separation/purification processes of commodity chemicals, particularly downstream processing of hydrocarbons. Of critical importance is the separation of C6 cyclic hydrocarbons benzene (C6H6) and cyclohexane (C6H12). Supplanting thermally driven distillation protocols such as azeotropic and extractive distillation methods by recyclable adsorbents, such as metal-organic framework (MOF) physisorbents, holds great promise for the reduction of this energy footprint. Whilst MOFs have come of age as physisorbents, they have been studied as benzene or cyclohexane selective adsorbents only rarely. Thanks to their amenability to crystal engineering, intensive research efforts have enabled metal-organic chemists to offer tunable coordination nanospaces in MOF sorbents in an adsorbate-specific manner, including aromatic benzene or aliphatic cyclohexane molecules. Despite the ever-expanding library of MOFs that often features families or isoreticular platforms of high surface-area materials with electron-rich or electron-deficient local pore environments, this research topic is underexplored and represents a niche area with a high upside potential. This review captures the progress made in MOF adsorbents to accomplish adsorption selectivity guided separation of the foregoing pair of C6 azeotropic hydrocarbons, which is crucial to the production of high-grade cyclohexane and benzene -important feedstock chemicals for further conversion into more useable commodity products, or as liquid organic hydrogen carriers. We also critically interrogate these examples to understand key structural and compositional approaches in order to efficiently design MOFs to extract benchmark selectivities and consequent high separation performances.
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