In the present work, we investigate the potential of aqueous polymer microgels in membrane technology, especially for filtration applications. The poly(N-vinylcaprolactam)-based microgels exhibit thermoresponsive behavior and were employed to coat hollow-fiber membranes used for micro- and ultrafiltration. We discuss the preparation of microgel-modified membranes (by "inside-out" as well as "outside-in" filtration in dead-end mode). The clean-water permeability and stability of these membranes was studied not only as a function of time, but also of temperature. The microgel-modified membranes exhibit a reversible thermoresponsive behavior whereby both the resistance and the retention increased with decreasing temperature.

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Synthetic membranes for desalination and ion separation processes are a prerequisite for the supply of safe and sufficient drinking water as well as smart process water tailored to its application. This requires a versatile membrane fabrication methodology. Starting from an extensive set of new ion separation membranes synthesized with a layer-by-layer methodology, we demonstrate for the first time that an artificial neural network (ANN) can predict ion retention and water flux values based on membrane fabrication conditions. The predictive ANN is used in a local single-objective optimization approach to identify manufacturing conditions that improve permeability of existing membranes. A deterministic global multi-objective optimization is performed in order to identify the upper bound (Pareto front) of the delicate trade-off between ion retention characteristics and permeability. Ultimately, a coupling of the ANN into a hybrid model enables physical insight into the influence of fabrication conditions on apparent membrane properties.

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