Abstract
Silver nanowires (AgNWs) are essential nanomaterials for diverse applications, including medical devices. Their morphology, like length and diameter, significantly affects conductivity, which is crucial for effective electrical signal transmission. Traditional trial-and-error approaches to adjust synthesis conditions for morphology control are time consuming. To overcome the limitation, this study integrates machine learning (ML) with experimental approaches to investigate how nucleants affect AgNWs synthesis. Random forest regression models are developed to analyze the effect of varying nucleant concentrations on morphology. Our approach builds a new framework to optimize synthesis conditions for morphology control, accelerating advancements in manufacturing capabilities.
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