Scale-up modeling for manufacturing nanoparticles using microfluidic T-junction

Nanoparticles have great potential to revolutionize industry and improve our lives in various fields such as energy, security, medicine, food, and environmental science. Droplet-based microfluidic reactors serve as an important tool to facilitate monodisperse nanoparticles with a high yield. Depending on process settings, droplet formation in a typical microfluidic T-junction is explained by different mechanisms, squeezing, dripping, or squeezing-to-dripping. Therefore, the manufacturing process can potentially operate under multiple physical domains due to uncertainties. Although mechanistic models have been developed for individual domains, a modeling approach for the scale-up manufacturing of droplet formation across multiple domains does not exist. Establishing an integrated and scalable droplet formation model, which is vital for scaling up microfluidic reactors for large-scale production, faces two critical challenges: the high dimensionality of the modeling space; and ambiguity among the boundaries of physical domains. This work establishes a novel and generic formulation for the scale-up of multiple-domain manufacturing processes and provides a scalable modeling approach for the quality control of products, which enables and supports the scale-up of manufacturing processes that can potentially operate under multiple physical domains due to uncertainties.