Abstract
Membrane capacitive deionization (MCDI) for water desalination is an innovative technique that could help to solve the global water scarcity problem. However, the development of the MCDI field is hindered by the limited choice of ion-exchange membranes. Desalination by MCDI removes the salt (solute) from the water (solvent); which can drastically reduce energy consumption compared to traditional desalination practices such as distillation. Herein, we outline the fabrication and characterization of quaternized anion-exchange membranes (AEMs) based on polymer blends of polyethylenimine (PEI) and polybenzimidazole (PBI) that provides an efficient membrane for MCDI. Flat sheet polymer membranes were prepared by solution casting, heat treatment and phase inversion, followed by modification to impart anion-exchange character. Scanning electron microscopy (SEM), atomic force microscopy (AFM), nuclear magnetic resonance (NMR) and Fourier-Transform infrared (FTIR) spectroscopy were used to characterize the morphology and chemical composition of the membranes. The as-prepared membranes displayed high ion-exchange capacity (IEC), hydrophilicity, permselectivity and low area resistance. Due to the addition of PEI, the high density of quaternary ammonium groups increased the IEC and permselectivity of the membranes, while reducing the area resistance relative to pristine PBI AEMs. Our PEI/PBI membranes were successfully employed in asymmetric MCDI for brackish water desalination and exhibited an increase in both salt adsorption capacity (>3x) and charge efficiency (>2x) relative to membrane-free CDI. The use of quaternized polymer blend membranes could help to achieve the greater realization of industrial scale MCDI.
Original language | English (US) |
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Journal | ACS Applied Polymer Materials |
DOIs | |
State | Published - Jun 1 2020 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: The authors would like to thank Martin Jennings (Dept. of Chemistry, University of Manchester) for elemental analysis measurements. The authors acknowledge the UK’s Engineering and Physical Sciences Research Council (EPSRC) under grant code EP/L01548X/1 for funding Robert McNair’s doctoral studies through the University of Manchester’s Graphene NOWNANO CDT account. Further equipment funding via EPSRC grants EP/S019367/1 and EP/P025021/1 to the Royce Institute is also gratefully acknowledged. The graphical abstract, Figures 1 and 2 were created by Xavier Pita, scientific illustrator at King Abdullah University of Science and Technology (KAUST). The research reported in this publication was supported by funding from KAUST.