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Cited by 304

The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.

Design of omniphobic interfaces for membrane distillation – A review

Lu, Kang Jia, Chen, Yuanmiaoliang, Chung, Tai-Shung·2019·Water Research

Abstract

Membrane distillation (MD) has a great potential in treating high salinity industrial wastewater due to its unique characteristics. Nevertheless, the implementation of MD for industrial wastewater reclamation must be conducted with precaution because low-surface-tension contaminates in feed solutions may easily wet the membranes. In recent years, omniphobic membranes that exhibit strong repellence towards liquids with a wide range of surface tensions have been proposed as a promising solution to deal with the wetting problem. In this paper, we aim to provide a comprehensive review of omniphobic interfaces and illustrate their fundamental working principles, innovative design approaches and novel applications on membrane distillation. The review may provide insights in designing stable solid-liquid-vapor interfaces and serve as a guidance for the development of robust anti-wetting membranes for industrial wastewater reclamation via membrane distillation.

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Metadata

Authors
Lu, K. J., Chen, Y., & Chung, T.-S.
Venue
Water Research
Publisher
Elsevier BV
Published
2019-10
Volume
162
Pages
64-77
ISSN
0043-1354 (ISSN-L)
OpenAlex
W2951170506
Cited by
304
References
55
FWCI
12.85
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Design of omniphobic interfaces for membrane distillation – A review. https://app.cochat.ai/papers/10.1016/j.watres.2019.06.056
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Lu, K. J., Chen, Y., & Chung, T.-S. (2019). Design of omniphobic interfaces for membrane distillation – A review. Water Research. https://doi.org/10.1016/j.watres.2019.06.056

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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.