An automated, droplet-flow microfluidic system explores and optimizes Pd-catalyzed Suzuki-Miyaura cross-coupling reactions. A smart optimal DoE-based algorithm is implemented to increase the turnover number and yield of the catalytic system considering both discrete variables-palladacycle and ligand-and continuous variables-temperature, time, and loading-simultaneously. The use of feedback allows for experiments to be run with catalysts and under conditions more likely to produce an optimum; consequently complex reaction optimizations are completed within 96 experiments. Response surfaces predicting reaction performance near the optima are generated and validated. From the screening results, shared attributes of successful precatalysts are identified, leading to improved understanding of the influence of ligand selection upon transmetalation and oxidative addition in the reaction mechanism. Dialkylbiarylphosphine, trialkylphosphine, and bidentate ligands are assessed.

中文翻译：

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通过自动反馈实现铃木-宫浦交叉耦合优化。

自动化液滴流微流体系统探索并优化 Pd 催化的 Suzuki-Miyaura 交叉偶联反应。实施基于 DoE 的智能优化算法，同时考虑离散变量（钯环和配体）和连续变量（温度、时间和负载），以提高催化系统的周转次数和产量。反馈的使用允许使用催化剂并在更有可能产生最佳结果的条件下进行实验；因此，复杂的反应优化在 96 次实验内完成。生成并验证了预测接近最佳反应性能的响应面。从筛选结果中，确定了成功预催化剂的共同属性，从而更好地了解配体选择对反应机制中的金属转移和氧化加成的影响。评估了二烷基联芳基膦、三烷基膦和二齿配体。