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Combinatorial strategies are for the first time applied in membrane technology and prove to be a powerful new tool in the search for novel membrane materials. The selected system for this study is a polyimide solvent-resistant nanofiltration membrane prepared via phase inversion. The phase inversion process is a typical membrane synthesis procedure involving a large number of compositional components, which can each be varied in a wide concentration range. The optimization of the membrane dope composition was performed using evolutionary optimization via genetic algorithms. Compared with the best commercially available membranes, a substantially improved membrane performance could be realized, both on the level of membrane selectivity and on that of permeability. The miniaturized high-throughput synthesis procedure could be scaled up successfully when the polymer dope was sufficiently viscous. It can be anticipated that application of combinatorial techniques can potentially lead to major improvements in all fields of membrane technology, for example water treatment, gas separation, and dialysis, not only on the compositional level but also for instance on the level of membrane synthesis posttreatment and operational conditions.

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We present a high-throughput computing scheme based on density functional theory (DFT) to generate a class of oxides and screen them with the aim of identifying those that might be electronically appropriate for transparent conducting oxide (TCO) applications. The screening criteria used are a minimum band gap to ensure sufficient transparency, a band edge alignment consistent with easy n- or p-type dopability, and a minimum thermodynamic phase stability to be experimentally synthesizable. Following this scheme we screened 23 binary and 1518 ternary bixbyite oxides in order to identify promising candidates, which can then be a subject of an in-depth study. The results for the known TCOs are in good agreement with the reported data in the literature. We suggest a list of several new potential TCOs, including both n- and p-type compounds.