journal article·2025-12Open access

Deciphering the Maze of Monomer Concentrations for Customizing Ion‐sieving Performance of Polyamide Membranes

Cheng‐Ye Zhu, Jin‐Bo Li, Jia‐Hui Xin, Hong‐Yu Fan, Yu Fang, Chao Zhang, Hao‐Cheng Yang, Hong‐Qing Liang, Zhi‐Kang Xu

Advanced Functional Materials· Volume 35· Issue 50

Abstract

Abstract Devising polyamide membranes with favorable pore size and surface charge is highly promising for boosting their ion‐sieving performance. Despite tremendous achievements, existing polyamide membranes suffer from limited pore size tunability and highly negative charges due to a haphazard or nonoptimized choice of monomer concentrations, significantly locking their ion‐sieving potential, especially for different ion species. Here, a straightforward strategy is proposed to synthesize a series of highly selective polyamide membranes with customized ion‐sieving performance through deciphering the maze of monomer concentrations and ratios to engineer pore size and charge density and systematically elucidating the impact of structure on performance. Beyond conventional concentration ranges, individual increases of either acyl chloride or amine monomer concentration can be harnessed to produce carboxy‐rich or amino‐rich polyamide membranes with enhanced charge group density, demonstrating improved selectivity for mono/divalent anions or cations, respectively. Notably, the simultaneous elevation of both monomer concentrations yields polyamide membranes with the highest amide bond density and smallest pore size, achieving ultrahigh selectivity above 100 for both ion types. Moreover, the optimal polyamide membranes demonstrate superior and durable Li + /Mg 2+ selectivity ranging from 79 to 366 toward simulated salt‐lake brines across varying salinities, Li + /Mg 2+ ratios, and operating pressures.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Membrane Separation Technologies · Fuel Cells and Related Materials · Membrane-based Ion Separation Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

MOE Key Lab of Macromolecular Synthesis and Functionalization Zhejiang Key Laboratory of Advanced Organic Materials and Technologies Department of Polymer Science and Engineering Zhejiang University Hangzhou 310058 China

The “Belt and Road” Sino‐Portugal Joint Lab on Advanced Materials International Research Center for X Polymers Zhejiang University Hangzhou 310058 China

Funding

Natural Science Foundation of Zhejiang Province · National Natural Science Foundation of China

References (44)

Show source-provided references
1. 10.1126/science.120048810.1126/science.1200488 ↗
2. 10.1126/science.aab053010.1126/science.aab0530 ↗
3. 10.1126/science.abb851810.1126/science.abb8518 ↗
4. 10.1021/acs.est.0c0810110.1021/acs.est.0c08101 ↗
5. 10.1016/j.desal.2011.04.00410.1016/j.desal.2011.04.004 ↗
6. 10.1016/j.memsci.2010.12.03610.1016/j.memsci.2010.12.036 ↗
7. 10.1039/D0CS00502A10.1039/D0CS00502A ↗
8. 10.1016/j.progpolymsci.2021.10145110.1016/j.progpolymsci.2021.101451 ↗
9. 10.1039/D0CS01599G10.1039/D0CS01599G ↗
10. 10.1038/s41467-024-46580-610.1038/s41467-024-46580-6 ↗
11. 10.1021/acsami.9b0192310.1021/acsami.9b01923 ↗
12. 10.1038/s41467-022-35681-910.1038/s41467-022-35681-9 ↗
13. 10.1016/j.memsci.2023.12220710.1016/j.memsci.2023.122207 ↗
14. 10.1039/C7TA08635K10.1039/C7TA08635K ↗
15. 10.1016/j.memsci.2022.12086010.1016/j.memsci.2022.120860 ↗
16. Fang Y. · Langmuir · 2024 · 40
17. 10.1016/j.memsci.2022.12132110.1016/j.memsci.2022.121321 ↗
18. 10.1016/j.memsci.2020.11886210.1016/j.memsci.2020.118862 ↗
19. 10.1002/adfm.20230581510.1002/adfm.202305815 ↗
20. 10.1038/s41467-023-41169-x10.1038/s41467-023-41169-x ↗
21. 10.1016/j.memsci.2022.12125110.1016/j.memsci.2022.121251 ↗
22. 10.1126/science.ade531310.1126/science.ade5313 ↗
23. 10.1016/j.progpolymsci.2016.06.00410.1016/j.progpolymsci.2016.06.004 ↗
24. 10.1016/j.progpolymsci.2024.10181510.1016/j.progpolymsci.2024.101815 ↗
25. 10.1016/j.memsci.2017.06.01610.1016/j.memsci.2017.06.016 ↗
26. 10.1126/science.aar212210.1126/science.aar2122 ↗
27. 10.1039/D3CS00803G10.1039/D3CS00803G ↗
28. 10.1007/s10118-022-2654-z10.1007/s10118-022-2654-z ↗
29. 10.1021/acsami.0c0516610.1021/acsami.0c05166 ↗
30. 10.34133/research.035910.34133/research.0359 ↗
31. 10.1016/j.memsci.2021.11987110.1016/j.memsci.2021.119871 ↗
32. 10.1039/D3CC04171A10.1039/D3CC04171A ↗
33. 10.1038/s41467-024-45918-410.1038/s41467-024-45918-4 ↗
34. 10.1021/j150579a01110.1021/j150579a011 ↗
35. 10.1038/s41467-020-15771-210.1038/s41467-020-15771-2 ↗
36. 10.1016/j.memsci.2023.12146810.1016/j.memsci.2023.121468 ↗
37. 10.1016/j.memsci.2017.02.02010.1016/j.memsci.2017.02.020 ↗
38. 10.1016/j.memsci.2021.11944110.1016/j.memsci.2021.119441 ↗
39. 10.1021/acsmacrolett.9b0007710.1021/acsmacrolett.9b00077 ↗
40. 10.1002/anie.20210355510.1002/anie.202103555 ↗
41. 10.1016/j.memsci.2023.12233010.1016/j.memsci.2023.122330 ↗
42. 10.1021/acsmaterialslett.4c0056810.1021/acsmaterialslett.4c00568 ↗
43. 10.1016/j.compositesb.2024.11195110.1016/j.compositesb.2024.111951 ↗
44. 10.1002/adfm.20231302610.1002/adfm.202313026 ↗