Efficient membrane design for removal of toxic pollutants from wastewater supported by machine learning

• Applicant: Dr. Martin Schmidt
• Subject Area: Polymer Materials
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 552615050

Micropollutants in aquatic systems have been recognized for many years as a serious global problem that poses a potential threat to ecosystems and human health. Advanced wastewater treatment methods to reduce the release of micropollutants in aquatic environments include adsorption techniques that, unlike some advanced oxidation processes, do not generate toxic by-products. Polymer membrane filtration, particularly nanofiltration or reverse osmosis, is a viable option to replace conventional adsorption strategies. Recently, adsorptive microfiltration membranes have emerged that allow much higher throughput but are not capable of (effectively) removing contaminants from wastewater due to insufficient adsorption sites. Therefore, the membrane surface must be engineered to enhance the accumulation of pollutants. A platform technology for the functionalization of polymer membranes has been developed at the Leibniz Institute of Surface Engineering (IOM). Nearly any molecular compound can be immobilized onto the polymer membrane surface by a reagent-free, electron beam-based approach that does not require pre-functionalization. However, an efficient strategy is missing to select a suited molecular functionalization for polymer membranes with high capacity to remove micropollutants from wastewater. Thus, the aim of this project is to develop a machine learning approach that uses solely data generated by quantum chemistry calculations as input for material design. In addition, experimental conditions during the membrane functionalization and adsorption process are considered in the machine learning model and used to rationally design adsorptive polymer membranes for the removal of 20 model micropollutants from aqueous solution.

• DFG Programme: Research Grants