2D lamellar membranes with defect-engineered hierarchical channels impart ultrafast and selective dye/salt separation
Year: 2025
Type: article
Abstract: Two-dimensional (2D) lamellar membranes hold immense promise for sustainable separation technologies, yet their inherent dense interlayer architecture compromises permeability and selectivity. Herein, we propose a defect engineering strategy to construct hierarchical nanofluidic channels within graphene oxide (GO) membranes, by using intercalating polypyrrole (PPy) nanosheets with in-plane nanopores. The synergistic assembly of GO and PPy nanosheets enables tuning of both interlayer spacing and intra-plane nanopores, thereby generating multiscale transport pathways. The optimized PPy/GO membrane achieves a trade-off between selectivity and permeability: >96% rejection for Congo red, <15% rejection for salts (NaCl, Na 2 SO 4 , CaCl 2 ), and a remarkable water flux of 310 L m -2 h -1 bar -1 — 5-fold higher than state-of-the-art 2D membranes. Interface selective sum frequency generation spectroscopy results reveal that PPy intercalation greatly enhances the order of hydrogen-bonded water at the membrane-water interface, which correlates with the fast and selective separation of dye and salt. Molecular dynamics and hydrodynamic simulations consolidate that the hierarchical channels enhance water permeation via defect-pore sieving, while selectively block organic dyes through size exclusion. Our work not only disentangles the intricate role of defect engineering in tailoring organic/salt separation for 2D membranes, but also provides a scalable approach for advanced separation systems in hypersaline wastewater treatment and resource recovery.
Source: Journal of Membrane Science
Authors: Tianmeng Zhang, Tianmeng Zhang, Zhongyang Huang, Dengshuo Cui, Zicheng Luo, Xiuping Zhuo, Yanfeng Liu, Xiuyan Wang, Kai Wu, Jianfeng Zhang, Guibin Li, Shumei Sun, Han Zuilhof, Hao Lu
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,890
OpenAlex ID: W4413581733
