Abhinandan Chopada

Azo dyes constitute one of the largest and most commercially important classes of synthetic colorants, widely applied in textiles, plastics, inks, and food. However, their manufacture through traditional batch processes is often constrained by safe-ty risks, poor heat and mass transfer, and inconsistent product quality. To address these challenges, we present a continuous telescopic strategy for the synthesis of the azo dye Sudan Yellow-3G. The workflow integrates Amar-made MicroFLO™ reactors for coupler and diazonium salt preparation, followed by azo coupling in a SlurryFLO™ reactor. This continuous approach mitigates hazards associated with exothermic and potentially runaway reactions while enabling tighter process con-trol. Optimization is further enhanced through the ReactWise© software platform, which applies machine learning to guide reaction conditions, minimizes experimental effort, and provides in-depth analytics. Together, this hardware-software integra-tion demonstrates a scalable, safer, and more resource-efficient alternative to conventional batch synthesis. Beyond the case of Sudan Yellow-3G, this work illustrates a transferable framework for modernizing azo dye manufacturing and highlights the broader potential of combining continuous flow chemistry with AI-driven optimization in industrial colorant production.

Azo dyes constitute one of the largest and most commercially important classes of synthetic colorants, widely applied in textiles, plastics, inks, and food. However, their manufacture through traditional batch processes is often constrained by safe-ty risks, poor heat and mass transfer, and inconsistent product quality. To address these challenges, we present a continuous telescopic strategy for the synthesis of the azo dye Sudan Yellow-3G. The workflow integrates Amar-made MicroFLO™ reactors for coupler and diazonium salt preparation, followed by azo coupling in a SlurryFLO™ reactor. This continuous approach mitigates hazards associated with exothermic and potentially runaway reactions while enabling tighter process con-trol. Optimization is further enhanced through the ReactWise© software platform, which applies machine learning to guide reaction conditions, minimizes experimental effort, and provides in-depth analytics. Together, this hardware-software integra-tion demonstrates a scalable, safer, and more resource-efficient alternative to conventional batch synthesis. Beyond the case of Sudan Yellow-3G, this work illustrates a transferable framework for modernizing azo dye manufacturing and highlights the broader potential of combining continuous flow chemistry with AI-driven optimization in industrial colorant production.