Extended Donnan-Manning theory for selective ion partition and transport in ion exchange membrane
The use of ion exchange membranes (IEMs) for electrochemical ion-ion separation leverages the selectivity of the IEMs toward like-charged species via valence difference and/or other ion-membrane interactions. A mechanistic model that relates selectivity with membrane structural and chemical properti...
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Veröffentlicht in: | Journal of membrane science 2023-09, Vol.681 (C), p.121782, Article 121782 |
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Sprache: | eng |
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Zusammenfassung: | The use of ion exchange membranes (IEMs) for electrochemical ion-ion separation leverages the selectivity of the IEMs toward like-charged species via valence difference and/or other ion-membrane interactions. A mechanistic model that relates selectivity with membrane structural and chemical properties is lacking in the literature. Here, we extend the Manning's counter-ion condensation model for describing ion partition and ion mobility inside IEMs to mixed salts scenarios. We evaluate the extended Donnan-Manning model against experimental data from literature and compare the performance of the Donnan-Manning model to that of the ideal Donnan model and the Donnan-Affinity model. Our analysis shows that, despite its structural complexity, the Donnan-Manning model has less fitting parameters than the Donnan-Affinity model and generally outperforms the two other models in predicting counter-ion and co-ion partition. With the assumption of a higher mobility of condensed ions than that of uncondensed ions, the generalized Manning's model can also predict counter-ion mobility selectivity for cation exchange membranes, but its performance for predicting mobility selectivity for anion exchange membranes is still unsatisfactory.
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•Counter-ion condensation model is extended to describe selective ion transport in IEMs.•The Donnan-Manning model fits counter-ion and co-ion partition data accurately.•Mobility selectivity can be predicted assuming condensed ions are more mobile. |
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ISSN: | 0376-7388 1873-3123 |
DOI: | 10.1016/j.memsci.2023.121782 |