Research project Toxicity guided inverse design of materials for environmental remediation

The vast majority of contaminated sites and waste streams are polluted with complex mixtures of chemicals, where the chemical complexity poses a challenge to developing highly efficient (nano)materials for remediation.
For efficient remediation we need to focus on all hazardous chemicals present in the contaminated environment. Recent developments in LC/HRMS have opened up possibilities for the detection of a broad spectrum of chemicals from contaminated sites and my group has recently expanded this methodology to evaluate for toxicity and concentration of detected chemicals with machine learning. These discoveries will allow us to combine the screening for chemical contaminants and material design that have remained independent processes until now. The goal of this project is to reach highly efficient materials for environmental remediation that perform under real world conditions by using the removal efficiency of toxic chemicals for the machine learning based inverse design.