选择性
吸附
共价键
共价有机骨架
化学
工作(物理)
材料科学
纳米技术
有机化学
热力学
物理
催化作用
作者
Xiaohao Cao,Zhengqing Zhang,Yanjing He,Wenjuan Xue,Hongliang Huang,Chongli Zhong
标识
DOI:10.1021/acs.iecr.2c01385
摘要
The efficient separation of ethane/ethene (C2H6/C2H4) is imperative yet challenging in industrial processes. We herein combine machine learning (ML) and molecular simulation to predict optimal covalent organic frameworks (COFs) for reversed C2H6/C2H4 separation before experimental efforts. Using molecular simulations, two out of 601 CoRE COFs were identified with excellent separation performance, and eight CoRE COFs exhibit high C2H6/C2H4 selectivity surpassing all of the reported values, although these COFs have a relatively low working capacity. As for ML, we found that the random forest (RF) algorithm displays the highest accuracy (R2 = 0.97) among the four different models, and the density (ρ) of COFs was identified as the key factor that influences the C2H6/C2H4 selectivity. Moreover, the 10 best hypothetical COFs (hCOFs) with excellent selectivity were further predicted. Ultimately, the competitive adsorption behaviors of guests in COF-303 were disclosed, and the adsorption selectivity of COF-303 was enhanced by introducing the fluorine group. Results of this work could provide molecular-level insights for future design and synthesis of novel COFs that can directly remove low-concentration ethane from the C2H4/C2H6 mixture.
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