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Enhanced separation of tetrafluoropropanol from water via carbon nanotubes membranes: insights from molecular dynamics simulations

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Abstract

Fluorinated alcohols exhibit promising prospects in chemical industry because of their special structure and many exciting properties, in which tetrafluoropropanol (TFP) is extensive applied in synthesis of pesticides, dyestuffs, variety of solvents and detergents. However, the presence of TFP in water garners increasing attention globally because of their intrinsic potential to threat ecosystems and human health. Carbon nanotubes (CNTs) membranes are burgeoning candidates for TFP-water separation owing to well-endowed extraordinary structural and transport properties. However, a grand challenge lies in the rational design of CNTs for improving separation performance. Herein, molecular dynamics (MD) simulations were performed to investigate the effects of various parameters on the separation of TFP-water mixtures including feed temperature, CNTs pore diameters, and fluorine functionalization position. It was found that TFP was pre-selected in CNTs ranging from 9.48 to 18.98 Å due to preferential adsorption and diffusion mechanism. Excellent separation factor of 16 was achieved by (7,7) CNTs and the mass fraction of TFP was purified from 75% to 97.51%. Fluorine modified CNTs separated TFP and water by preferentially permeating water due to hydrogen bonding interaction. Simulation results showed that CNTs modified at both the entrance and interior had better separation performance than CNT modified only at one of these positions. The 100wt% water content in permeate was achieved by (11,11) CNTs modified with fluorine at the entrance and interior. These findings provide valuable insights for designing potential candidates for fluorinated alcohol-water azeotropic mixtures membrane separation, and promise extensive application aspects for the reclamation of fluorinated alcohol.

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Acknowledgements

This work was supported by the Shandong Provincial Natural Science Foundation (Nos. ZR2020MB118 and ZR2020QB175), the National Natural Science Foundation of China (Nos. 2197080534, 22008143, and 52070123), the State Key Laboratory of High-efficiency Utilization of Coal and Green Chemical Engineering (No. 2022-K10), the Project funded by China Postdoctoral Science Foundation (No. 2022M711958) and the Applied Research Project of Qingdao Postdoctoral (No. 01020240119), the China Postdoctoral Science Foundation (No. 2022M720083).

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  1. These authors contributed equally to this work.

Authors and Affiliations

  1. College of Chemical and Biological Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Qing Li, Xiaomeng Wang, Ying Liu, Zhun Ma, Qun Wang, Dongmei Xu & Jun Gao

  2. SEPCOIII Electric Power Construction Co. Ltd., Qingdao, 266100, China

    Ruirui Wu

  3. State Key Laboratory of High-Efficiency Utilization of Coal and Green Chemical Engineering, Ningxia University, Yinchuan, 750021, China

    Hui Sun

  4. Key Laboratory of Marine Chemistry Theory and Technology (Ministry of Education), Ocean University of China, Qingdao, 266100, China

    Xueli Gao

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  1. Qing Li
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  2. Xiaomeng Wang
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  3. Ying Liu
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  4. Zhun Ma
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  5. Qun Wang
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  6. Dongmei Xu
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  7. Jun Gao
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  8. Ruirui Wu
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  9. Hui Sun
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  10. Xueli Gao
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Corresponding authors

Correspondence to Zhun Ma, Dongmei Xu or Xueli Gao.

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The authors declare no competing interests.

Highlights

• MD simulations unveil the transport mechanism for TFP-water mixture through CNTs.

• The (7,7) CNTs provided a dramatic mass fraction (97.51%) of TFP.

• Fluorine modified CNTs favor water preferential transport compare to pristine CNTs.

• CNTs modified at entrance and interior prompt permselectivity for water molecules.

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Enhanced separation of tetrafluoropropanol from water via carbon nanotubes membranes: insights from molecular dynamics simulations

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Li, Q., Wang, X., Liu, Y. et al. Enhanced separation of tetrafluoropropanol from water via carbon nanotubes membranes: insights from molecular dynamics simulations. Front. Environ. Sci. Eng. 17, 140 (2023). https://doi.org/10.1007/s11783-023-1740-y

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  • DOI: https://doi.org/10.1007/s11783-023-1740-y

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