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Article: The open membrane database: Synthesis–structure–performance relationships of reverse osmosis membranes

TitleThe open membrane database: Synthesis–structure–performance relationships of reverse osmosis membranes
Authors
Issue Date2022
Citation
Journal of Membrane Science, 2022, v. 641, p. 119927 How to Cite?
AbstractSince the advent of thin-film composite polyamide membranes brought forth a breakthrough in desalination and water purification membranes nearly half a century ago, recent years have only witnessed marginal improvements in the water-salt selectivity of these membranes. The slow progression is partly attributable to limited understanding of membrane synthesis–structure–performance relationships. A centralized archive of reverse osmosis membrane (RO) characterization data may lead to a shared understanding of features that maximize RO performance and unify research efforts. The Open Membrane Database (OMD), which can be found at www.openmembranedatabase.org, is a growing database of over 600 water purification and desalination membranes that are sourced from peer-reviewed journals, patents, and commercial product data. Here, we outline the detailed functionality of the database, the transport theory underlying the membrane performance calculations, and best practices for membrane performance testing and reporting. The user-sourced, open-access database may be used to benchmark novel RO membranes against the state of the art, conduct meta-analyses, and develop synthesis–structure–performance relationships, each of which will be critical to advancing membrane development.
Persistent Identifierhttp://hdl.handle.net/10722/314998
ISSN
2023 Impact Factor: 8.4
2023 SCImago Journal Rankings: 1.848
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorRitt, CL-
dc.contributor.authorStassin, T-
dc.contributor.authorDavenport, DM-
dc.contributor.authorDuChanois, RM-
dc.contributor.authorNulens, I-
dc.contributor.authorYang, Z-
dc.contributor.authorBen-Zvi, A-
dc.contributor.authorSegev-Mark, N-
dc.contributor.authorElimelech, M-
dc.contributor.authorTang, C-
dc.contributor.authorRamon, GZ-
dc.contributor.authorVankelecom, IFJ-
dc.contributor.authorVerbeke, R-
dc.date.accessioned2022-08-05T09:38:21Z-
dc.date.available2022-08-05T09:38:21Z-
dc.date.issued2022-
dc.identifier.citationJournal of Membrane Science, 2022, v. 641, p. 119927-
dc.identifier.issn0376-7388-
dc.identifier.urihttp://hdl.handle.net/10722/314998-
dc.description.abstractSince the advent of thin-film composite polyamide membranes brought forth a breakthrough in desalination and water purification membranes nearly half a century ago, recent years have only witnessed marginal improvements in the water-salt selectivity of these membranes. The slow progression is partly attributable to limited understanding of membrane synthesis–structure–performance relationships. A centralized archive of reverse osmosis membrane (RO) characterization data may lead to a shared understanding of features that maximize RO performance and unify research efforts. The Open Membrane Database (OMD), which can be found at www.openmembranedatabase.org, is a growing database of over 600 water purification and desalination membranes that are sourced from peer-reviewed journals, patents, and commercial product data. Here, we outline the detailed functionality of the database, the transport theory underlying the membrane performance calculations, and best practices for membrane performance testing and reporting. The user-sourced, open-access database may be used to benchmark novel RO membranes against the state of the art, conduct meta-analyses, and develop synthesis–structure–performance relationships, each of which will be critical to advancing membrane development.-
dc.languageeng-
dc.relation.ispartofJournal of Membrane Science-
dc.titleThe open membrane database: Synthesis–structure–performance relationships of reverse osmosis membranes-
dc.typeArticle-
dc.identifier.emailYang, Z: zheyang8@hku.hk-
dc.identifier.emailTang, C: tangc@hku.hk-
dc.identifier.authorityYang, Z=rp02847-
dc.identifier.authorityTang, C=rp01765-
dc.identifier.doi10.1016/j.memsci.2021.119927-
dc.identifier.hkuros334779-
dc.identifier.volume641-
dc.identifier.spage119927-
dc.identifier.epage119927-
dc.identifier.isiWOS:000705869000004-

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