| Project Detail |
This project focuses on developing and implementing an automated organic electrosynthesis process of cyclopropane rings. Electrochemical (EC) methods, which often suffering from passivation phenomena, will be applied in a continuous-flow microreactor integrated into a global chemical robot, addressing critical challenges in the field of synthetic chemistry. Cyclopropane rings are vital intermediates in organic synthesis, yet their scalable production via green and efficient methods remains underdeveloped. This study aims to optimize EC cyclopropanation with the aid of machine learning algorithms (e.g. Bayesian optimization) to refine reaction conditions and minimize electrode passivation. The key objectives include: (i) optimizing reaction parameters to maximize yields, conversion rates, and selectivity, (ii) implementing advanced machine learning tools to address and mitigate foulinga major barrier to EC process scalabilityand, (iii) developing robust numerical models to enable the scaling up of EC reactions. The integration of machine learning with EC flow synthesis is expected to enhance reaction efficiency and reproducibility, creating a framework for the systematic screening of reactions and mechanisms, thus reducing the manual experimental time required by traditional optimization methods. The EC microreactor, implemented within automated continuous-flow platforms, will facilitate dataset generation, real-time control over reaction dynamics, and material production, pushing the boundaries of electrosynthetic methodologies. The ultimate goal is to establish a versatile, high-throughput approach for synthesizing cyclopropane rings and other high-value molecules, paving the way for significant advancements in sustainable synthetic strategies and expanding the potential of EC processes in organic chemistry. |
