In the long human effort to restore what industry has fouled, a research team has found a way to let machines learn from sparse evidence — combining classical experimental design with modern algorithms to optimize the tiny iron particles that pull dye from contaminated water. Working with as few as fifteen experiments, they expanded their understanding through data augmentation and gradient-boosted models, discovering that temperature governs crystal size while stirring speed shapes surface area. The work is less about any single nanoparticle and more about a method: a cost-conscious, data-dri