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Surface roughness of membranes is often perceived by many as a factor that promotes fouling during filtration, and thus is undesirable. Almost all liquid-based separation membranes display flat surfaces with intrinsic surface roughness that is associated with the membrane manufacturing process. Recently, polymer ultrafiltration (UF) and thin film composite (TFC) membranes containing regular, periodic surface patterns were fabricated using cost-effective lithographic methods. Here, we review the work to-date on the fabrication and characterization of these patterned membranes with a focus on processing-structure-performance relationships.In addition, the antifouling performance of these membranes against model foulants including colloidal suspensions and protein solutions are also highlighted.

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Abstract. Artificial neural networks (ANNs) were used in this study to determine factors that control the polydispersity index (PDI) in an acetaminophen nanosuspension which was prepared using nanoprecipitation in microfluidic devices. The PDI of prepared formulations was measured by dynamic light scattering. Afterwards, the ANNs were applied to model the data. Four independent variables, namely, surfactant concentration, solvent temperature, and flow rate of solvent and antisolvent were considered as input variables, and the PDI of acetaminophen nanosuspension was taken as the output variable. The response surfaces, generated as 3D graphs after modeling, were used to survey the interactions happening between the input variables and the output variable. Comparison of the response surfaces indicated that the antisolvent flow rate and the solvent temperature have reverse effect on the PDI, whereas solvent flow rate has direct relation with PDI. Also, the effect of the concentration of the surfactant on the PDI was found to be indirect and less influential. Overall, it was found that minimum PDI may be obtained at high values of antisolvent flow rate and solvent temperature, while the solvent flow rate should be kept to a minimum.

The purpose of this study was to find an artificial neural networks model for determining major factors impacting the stability of an acetaminophen nanosuspansion that was prepared using nanoprecipitation in microfluidic reactors. Four variables, namely concentration of surfactant, solvent and antisolvent flow rate and solvent temperature were used as input variables and time of sedimentation of nanoparticles was considered as output variable. The particle size of optimized formulation was measured by transmission electron microscope and dynamic light scattering. Comparing the 3D graphs from the model showed that antisolvent flow rate and temperature have direct relation with time of sedimentation, whereas solvent flow rate generally has reverse relation with the time of sedimentation. Concentration of surfactant was found to be the most important factor in determining the stability of nanosuspension.

It is commonly believed that the overall permeation resistance of thin film composite (TFC) membranes is dictated by the crosslinked, ultrathin polyamide barrier layer, while the porous support merely serves as the mechanical support. Although this assumption might be the case under low transmembrane pressure, it becomes questionable under high transmembrane pressure. A highly porous support normally yields under a pressure of a few MPa, which can result in a significant level of compressive strain that may significantly increase the resistance to permeation. However, quantifying the influence of porous support deformation on the overall resistance of the TFC membrane is challenging. In particular, it is difficult to determine the deformation/strain of the membrane during active separation. In this study, we use nanoimprint lithography (NIL) to achieve precise compressive deformation in commercial TFC membranes. By adjusting the NIL conditions, membranes were compressed to strain levels up to 60%. SEM and AFM measurements showed that the compression had minimal impact on the barrier-layer surface morphology and total surface area with most of the deformation occurring in the support layer. DI water permeation measurements revealed that the water flux reduction decreases with an increase of strain level. Most significantly, the intrinsic membrane resistance showed negligible changes at strain levels lower than 30%–40%, but increased exponentially at higher strain levels, reaching 250%–500% of pristine (unstrained) membrane values. Using a resistance-in-series model, the strain dependency of the TFC membrane resistance can be described.