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A review of water treatment membrane nanotechnologies
Abstract
Nanotechnology is being used to enhance conventional ceramic and polymeric water treatment membrane materials through various avenues. Among the numerous concepts proposed, the most promising to date include zeolitic and catalytic nanoparticle coated ceramic membranes, hybrid inorganic–organic nanocomposite membranes, and bio-inspired membranes such as hybrid protein–polymer biomimetic membranes, aligned nanotube membranes, and isoporous block copolymer membranes. A semi-quantitative ranking system was proposed considering projected performance enhancement (over state-of-the-art analogs) and state of commercial readiness. Performance enhancement was based on water permeability, solute selectivity, and operational robustness, while commercial readiness was based on known or anticipated material costs, scalability (for large scale water treatment applications), and compatibility with existing manufacturing infrastructure. Overall, bio-inspired membranes are farthest from commercial reality, but offer the most promise for performance enhancements; however, nanocomposite membranes offering significant performance enhancements are already commercially available. Zeolitic and catalytic membranes appear reasonably far from commercial reality and offer small to moderate performance enhancements. The ranking of each membrane nanotechnology is discussed along with the key commercialization hurdles for each membrane nanotechnology.
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Metadata
- Authors
- Pendergast, M. M., & Hoek, E. M. V.
- Venue
- Energy & Environmental Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2011
- Volume
- 4
- Issue
- 6
- Pages
- 1946
- ISSN
- 1754-5692 (ISSN-L)
- 1754-5706
- OpenAlex
- W2046034814
- Cited by
- 2,078
- References
- 150
- FWCI
- 48.66
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
