High-throughput computational screening and molecular fingerprint design of metal-organic framework adsorbents for separation of C3 components
Chengzhi Cai,
Lifeng Li,
Yafang Guan,
Xiaoshan Huang,
Shiqing Ke,
Wenfei Wang,
Yu Li,
Yujuan Yang,
Hong Liang,
Shuhua Li,
Yufang Wu,
Hanyu Gao,
Zhiwei Qiao
Affiliations
Chengzhi Cai
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Lifeng Li
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Yafang Guan
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Xiaoshan Huang
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Shiqing Ke
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Wenfei Wang
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Yu Li
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Yujuan Yang
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Hong Liang
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Shuhua Li
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China
Yufang Wu
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; Corresponding authors.
Hanyu Gao
Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China; Corresponding authors.
Zhiwei Qiao
Guangzhou Key Laboratory New Energy and Green Catalysis, School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China; Joint Institute of Guangzhou University & Institute of Corrosion Science and Technology, Guangzhou University, Guangzhou 510006, China; Corresponding authors.
To decrease the consumption of energy and material resources caused by the traditional ''two-step'' process for separating propylene from the C3 crude components, including propane, propylene, methylacetylene and propadiene, this work utilizes the metal-organic frameworks (MOFs) involved ''one-step'' process to purify propylene. First, the relationship between the geometric/energy descriptors of MOFs and their performance metrics was established through univariate analysis, and results show that the top-performance MOFs can be screened by the differences of LCD, ρ, VSA and ϕ. Then, different combinations of descriptors and algorithms were used for machine learning. The combination (RF algorithm, 7 basic descriptors + PSD + MorganFPs + nodes + topologies) with the best prediction accuracy (R=0.87) for predicting the performance of MOFs was found. Finally, 4 optimal pore structures for the design of high propylene adsorption performance materials were summarized, which mainly contain cylindrical channels and spherical cavities similar in size to the target gas. The microscopic control of pore structures obtained from our bottom-up approach is useful for the development of MOFs and other nanoporous materials which can be used for ''one-step'' separation of propylene from C3 mixtures in various industrial situations.
