Charge distribution in polyelectrolyte multilayer nanofiltration membranes affects ion separation and scaling propensity

Abstract

Abstract Polyelectrolyte multilayer nanofiltration membranes (PEMMs) achieve tailor-made rejection and selectivity of ions for water treatment applications through a layer-by-layer coating procedure, in which a charged support membrane surface is sequentially contacted with positively and negatively charged polyelectrolytes. This results in the adsorption and formation of such selective multilayer membrane skins with defined molecular compositions. The selective properties of the PEMM depend on the intrinsic properties of the respective layers. Today’s research efforts aim to correlate the membrane’s selective characteristics, its structural parameters, and the operating conditions to a model representation of the membrane’s properties. We use our previously published pE n PE n model, which solves the pressure (p) driven transport of ions through n electrolyte layers (E n ) and n polyelectrolyte layers (PE n ). Here, we expand the model to predict the multi-ionic pressure-induced transport through PEMMs solving one-dimensional Nernst–Planck–Poisson equations. The simulations quantify the influence of asymmetric charge distributions and individual PE layers on the ion selectivity for multi-ion solutions. These asymmetric layer properties represent the nanometer-scale membrane properties emerging from the ionic crosslinking, fixed charge compensation, and overcompensation. The model gives insight into each ion’s concentration profile for n layers of electrolyte and n layers of polyelectrolytes. Now, multi-ion compositions inside and outside of the membrane are simulated, and it is shown that the membrane charge distribution even influences the onset of scaling at the fluid membrane interface. As pE n PE n provides a detailed understanding of the rejection and selectivity characteristics as a function of membrane flux and feed concentration including feed side concentration polarization, it can now predict flux-scaling boundaries for the different membrane charge distributions, making it a powerful tool for choosing process parameters and even for designing tailored PEMMs for specific separation tasks.