Journal
CHEMICAL ENGINEERING SCIENCE
Volume 195, Issue -, Pages 230-238Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ces.2018.11.055
Keywords
Graphene oxide membranes; Dopamine; Zinc ions; Synergistic manipulation; CO2 separation
Categories
Funding
- National Key RAMP
- D Program of China [2017YFB0603400]
- National Natural Science Foundation of China [21490583, 21621004]
- State Key Laboratory of Organic-Inorganic Composites [oic-201701004]
- National Science Fund for Distinguished Young Scholars [21125627]
- State Key Laboratory of Separation Membranes and Membrane Processes (Tianjin Polytechnic University) [M1-201701, M1-201501]
- Program of Introducing Talents of Discipline to Universities [B06006]
- National Key Laboratory of United Laboratory for Chemical Engineering [SKL-ChE-17B01]
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In this study, a kind of functionalized graphene oxide (GO) membranes was fabricated. GO nanosheets were pre-coated by polydopamine (PDA) via spontaneous polymerization of dopamine (DA) and then cross-linked with zinc ions (Zn2+) via dopamine-mediated complexation reaction. The PDA coating rendered GO nanosheets a large number of evenly distributed oxygen-containing functional groups, which are beneficial to bond zinc ions effectively and to create favorable microenvironments of the nanochannels in GO membranes. Both PDA and Zn2+ acted as cross-linkers to tune the interlayer spacing of GO nanosheets. Zinc ions also afforded facilitated transport ability towards CO2 in dry state. Taking CO2/CH4 separation as the model system, the GO-PDA-Zn2+ membrane exhibited significantly enhanced CO2 permeance of 175 GPU with CO2/CH4 selectivity of 19.1 in dry state owing to the large transport channels and facilitated transport carriers. Moreover, the GO-PDA-Zn2+ membrane exhibited high CO2/CH4 selectivity of 32.9 in wet state owing to the stable intergalleries and moderate interlayer spacing. It is envisioned that the synergistic manipulation of PDA and metal ions can be utilized to exploit a variety of 2D membranes with superior gas separation performance. (C) 2018 Elsevier Ltd. All rights reserved.
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