Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141078
Title: Relating transport modeling to nanofiltration membrane fabrication : navigating the permeability-selectivity trade-off in desalination pretreatment
Authors: Labban, Omar
Liu, Chang
Chong, Tzyy Haur
Lienhard, John Henry
Keywords: Engineering::Environmental engineering
Issue Date: 2018
Source: Labban, O., Liu, C., Chong, T. H., & Lienhard, J. H. (2018). Relating transport modeling to nanofiltration membrane fabrication : navigating the permeability-selectivity trade-off in desalination pretreatment. Journal of Membrane Science, 554, 26-38. doi:10.1016/j.memsci.2018.02.053
Journal: Journal of Membrane Science
Abstract: Faced with a pressing need for membranes with a higher permeability and selectivity, the field of membrane technology can benefit from a systematic framework for designing membranes with the necessary physical characteristics. In this work, we present an approach through which transport modeling is employed in fabricating specialized nanofiltration membranes, that experimentally demonstrate enhanced selectivity. Specifically, the Donnan-Steric Pore Model with dielectric exclusion (DSPM-DE) is used to probe for membrane properties desirable in desalination pretreatment. Nanofiltration membranes are systematically fabricated in-house using layer-by-layer (LbL) deposition to validate model predictions and to develop a new specialized membrane for this application. The new membrane presents a 30% increase in permeability and a 50% reduction in permeate hardness relative to state-of-the-art NF membranes. Our results indicate that a ‘specialized’ tight membrane can outperform looser counterparts in both permeability and selectivity. Given the possibility of extending this framework to other applications, the work furthers our understanding of the relationships governing membrane form and function, while having broad potential implications for future nanofiltration membranes used in chemical separation and purification.
URI: https://hdl.handle.net/10356/141078
ISSN: 0376-7388
DOI: 10.1016/j.memsci.2018.02.053
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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