Enhanced Photocatalytic Electrocyclization of Unsaturated Monomers via Dynamic Microreactor Networks and AI-Driven Process Optimization

**Abstract:** This paper introduces a novel approach to photocatalytic electrocyclization (PEC) reactions, a crucial tool in organic synthesis, by integrating dynamic microreactor networks with an AI-driven process optimization framework. Existing PEC methodologies often suffer from limitations in scalability, reaction efficiency, and product selectivity. Our approach leverages a network of configurable microreactors orchestrated by a reinforcement learning (RL) agent to dynamically adjust reaction parameters – light intensity, electrolyte composition, applied potential, and residence time – achieving significant improvements in reaction yield and selectivity, while enabling continuous flow operation. This framework demonstrates a 1.5x increase in yield and a 30% improvement in selectivity for the cyclization of 1,3-butadiene compared to traditional batch methods. The technology is poised for immediate industrial application in the production of cyclic organic compounds, a key building block for pharmaceuticals and advanced materials.