There is a need for developing reliable models for water and solute transport in graphene oxide (GO) membranes for advancing their emerging industrial water processing applications. In this direction, we develop predictive transport models for GO and reduced-GO (rGO) membranes over a wide solute concentration range (0.01–0.5 M) and compositions, based on the extended Nernst–Planck transport equations, Donnan equilibrium condition, and solute adsorption models. Some model parameters are obtained by fitting experimental permeation data for water and unary (single-component) aqueous solutions. The model is validated by predicting experimental permeation behavior in binary solutions, which display very different characteristics. Sensitivity analysis of salt rejections as a function of membrane design parameters (pore size and membrane charge density) allows us to infer design targets to achieve high salt rejections. Such models will be useful in accelerating structure-separation property relationships of GO membranes and for separation process design and optimization.

中文翻译：

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氧化石墨烯纳滤膜的传输特性：电动建模和实验验证

需要开发可靠的氧化石墨烯 (GO) 膜中的水和溶质传输模型，以推进其新兴的工业水处理应用。在这个方向上，我们基于扩展的 Nernst-Planck 输运方程、Donnan 平衡条件和溶质，在很宽的溶质浓度范围（0.01-0.5 M）和成分上开发了 GO 和还原-GO（rGO）膜的预测输运模型吸附模型。一些模型参数是通过拟合水和一元（单组分）水溶液的实验渗透数据获得的。该模型通过预测二元溶液中的实验渗透行为来验证，二元溶液显示出非常不同的特征。作为膜设计参数（孔径和膜电荷密度）函数的脱盐率的敏感性分析使我们能够推断设计目标以实现高脱盐率。这些模型将有助于加速 GO 膜的结构-分离特性关系以及分离过程的设计和优化。