Predictive Inorganic Synthesis Based on Machine Learning Using Small Data Sets: A Case Study of Hydrodynamic Diameter-Controlled Copper Nanoparticles
Copper nanoparticles (Cu NPs) have a broad applicability, yet their synthesis is sensitive to subtle changes in reaction parameters. This sensitivity, combined with the time- and resource-intensive nature of experimental optimization, poses a major challenge in achieving reproducible and size-controlled synthesis. Wh...