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- W4360615770 abstract "Freshwater scarcity has emerged as a prominent bottleneck for global sustainable development, and all countries are sharing a serious reality. Currently, the search for a high-performance, two-dimensional (2D) desalination membrane remains a tremendous challenge. A high-throughput screening involving density functional theory–machine learning (DFT-ML) framework is considered to be an essential avenue to tackle this dilemma. Here, desalination membranes with desirable salt ion retention, water flux, mechanical properties, and service life are selected by particle swarm algorithms. Subsequently, a typical screened-out structure is verified by multiple simulation technology. The results of DFT and ML show that the 2D desalination membranes can be ingeniously singled out by energy density (8.4–8.8 eV/atom), adsorption energy (<−0.5 eV), and mechanical properties parameters (C(θ) > 32 GPa, v(θ) > 0.25). To test the effectiveness of this high-throughput screening method, a 2D desalination membrane (named Dadri-C) was investigated in detail by first-principles and molecular dynamics. We found that Dadri-C has favorable mechanical, thermodynamic, and dynamical stabilities. Dadri-C features sufficient salt ion adsorption sites and flexible electronic structure and confirms excellent selectivity. Further, it exhibits 100% Cl– salt rejection efficiency in the simulation, while achieving a 98.2% Na+ rejection efficiency even at 90 MPa. The maximum water flux obtained while maintaining efficient salt rejection amounts to 525.9 L·cm–2·day–1·MPa–1. Through elaborate screening, we also discovered that the mechanical properties of Dadri-C are acceptable. It also features a self-cleaning function conducive to recycling, indicating that the screening strategy fulfilled the intended target. This proposed research could introduce innovative ideas for efficient screening and design of desalination membrane, which is expected to confer significant opportunities in promoting desalination technology." @default.
- W4360615770 created "2023-03-24" @default.
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- W4360615770 date "2023-03-23" @default.
- W4360615770 modified "2023-10-05" @default.
- W4360615770 title "Data-Driven Design of High-Performance Graphene-Based Seawater Desalination Membranes" @default.
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- W4360615770 doi "https://doi.org/10.1021/acsanm.3c00256" @default.
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