ReviewACS ES&T engineering2024
Unifying the Conversation: Membrane Separation Performance in Energy, Water, and Industrial Applications.
Review in ACS ES&T engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Biphasic Extraction of Selected Heavy Metals From Corn With the Aid of Kosmotropic Salts.Analytical science advances · 2026Article
- Organic photochemistry for direct light-driven separations.Chemical science · 2026Review
- Superhydrophobic Nanocomposite Polysulfone Fibrous Membranes Incorporated with Phenyltriethoxysilane-Modified Silica for Oil-Water Separation.ACS omega · 2026Article
- Interfacial electroneutrality controls transport of asymmetric salts through charge-patterned mosaic membranes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Review
- Contribution of the transmembrane electric potential to the set voltage in a single-anion exchange membrane electrodialysis-cell and the role of solution conditions.Journal of membrane science · 2025Article
- Tailoring Robust 2D Nanochannels by Radical Polymerization for Efficient Molecular Sieving.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dense polymer membranes enable a diverse range of separations and clean energy technologies, including gas separation, water treatment, and renewable fuel production or conversion. The transport of small molecular and ionic solutes in the majority of these membranes is described by the same solution-diffusion mechanism, yet a comparison of membrane separation performance across applications is rare. A better understanding of how structure-property relationships and driving forces compare among applications would drive innovation in membrane development by identifying opportunities for cross-disciplinary knowledge transfer. Here, we aim to inspire such cross-pollination by evaluating the selectivity and electrochemical driving forces for 29 separations across nine different applications using a common framework grounded in the physicochemical characteristics of the permeating and rejected solutes. Our analysis shows that highly selective membranes usually exhibit high solute rejection, rather than fast solute permeation, and often exploit contrasts in the size and charge of solutes rather than a nonelectrostatic chemical property, polarizability. We also highlight the power of selective driving forces (e.g., the fact that applied electric potential acts on charged solutes but not on neutral ones) to enable effective separation processes, even when the membrane itself has poor selectivity. We conclude by proposing several research opportunities that are likely to impact multiple areas of membrane science. The high-level perspective of membrane separation across fields presented herein aims to promote cross-pollination and innovation by enabling comparisons of solute transport and driving forces among membrane separation applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.