PFAS “forever chemicals” contaminate water, food, and air and accumulate in the body, causing serious health risks. This research develops a light-activated porous material that traps and breaks down PFAS molecules. Tested in real-world water and now being scaled up, the method aims to provide a practical, permanent solution for removing PFAS and protecting safe drinking water.

My research uses artificial intelligence to detect water pollution by analysing DNA traces left by aquatic species. Instead of relying on visual signs or costly expert identification, supervised machine learning reads species patterns to determine water quality. The method is faster, cheaper, and more accurate than traditional analysis.