Advances in Reverse Osmosis Membrane Research Through Computer Simulation
A special issue of Membranes (ISSN 2077-0375). This special issue belongs to the section "Membrane Applications for Water Treatment".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 11
Special Issue Editors
Interests: molecular dynamics
Special Issue Information
Dear Colleagues,
Reverse osmosis (RO) membranes are critical for addressing global water scarcity, serving as a cornerstone technology for desalination and purification. Despite significant advancements, challenges persist, including membrane fouling, high energy consumption, trade-offs in selectivity–permeability, and unclear structure–property relationships. The integration of advanced computational methods—particularly molecular dynamics simulations, machine learning technologies, and multi-scale modeling approaches—with experimental validation offers exciting pathways to overcome these limitations and accelerate the development of next-generation RO membranes.
This Special Issue will showcase cutting-edge computational research in RO membrane science and engineering, emphasizing how simulation-driven approaches are revolutionizing our understanding of membrane design and performance. We invite original research and comprehensive reviews that demonstrate the power of computational methods in unveiling formation mechanisms, predicting membrane behaviors, optimizing material properties, and guiding experimental syntheses. This collection aims to establish computational modeling as an indispensable tool for developing more sustainable and efficient water treatment solutions.
Topics of interest include, but are not limited to, the following:
- Molecular-Scale Simulations and Modeling: Molecular dynamics simulations of interfacial polymerization, water transport, salt rejection, and fouling–antifouling interactions; quantum mechanical calculations for membrane–water–pollutant interactions; and coarse-grained modeling for polymer membrane structures.
- Machine Learning and AI-Driven Membrane Design: Predictive models for membrane performance, and automated discovery of novel membrane structures and properties.
- Multi-Scale Computational Frameworks: Integration of molecular simulations with continuum models, computational fluid dynamics for membrane module design, and bridging atomistic insights with macroscopic performance metrics.
Dr. Size Zheng
Prof. Dr. Hongyang Ma
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Membranes is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- reverse osmosis membrane
- molecular dynamics
- machine learning
- computational fluid dynamics
- interfacial polymerization
- crosslinking
- structure–property relationship
- water treatment
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