This research develops a low-cost water-monitoring system using nanofabricated diffraction surfaces and image analysis. As water flows over a “rainbow film,” distinct optical patterns reveal chemical or biological contaminants. The system has already detected dyes, algae, and particulates, offering a rapid, affordable tool for identifying pollution in water pipelines.

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.