Separation of water–alcohol mixtures using carbon nanotubes under an electric field

Literature Information

Publication Date 2019-07-01
DOI 10.1039/C9CP01799B
Impact Factor 3.676
Authors

Winarto, Eiji Yamamoto, Kenji Yasuoka


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Abstract

Carbon nanotubes (CNTs) are a promising candidate for separation membranes because of their ability to transport substances at very high flow rates. However, there is a tradeoff between achieving a high selectivity using small pore sizes and the reduction of water flux. Here, using molecular dynamics simulations, we report that CNTs can effectively separate water–methanol mixtures under an electric field. Without an electric field and under piston pressure, both water and methanol flow through a CNT, resulting in no separation effect. In contrast, under an electric field and high piston pressure, CNTs allow selective water permeation while rejecting the permeation of methanol molecules. This separation effect is caused by the ordered structures of water molecules in the CNT. A high filtering effect is observed under the conditions of high methanol concentration in the solution or even with large-diameter CNTs up to 3.39 nm. As long as the ordered structure of water in the CNTs can be maintained, the strong filtering effect can be maintained.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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