Abstract
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.
Highlights
Heralding the era of the “robo-chemist”,1 automated chemical development continues to gain prominence in academia and industry, ushering in novel ways to synthesize libraries, optimize reaction conditions, and evaluate kinetics.[2,3] Such growth in popularity, has been met with growing skepticism[4] that the job of the chemist could ever truly be replaced with a robot
In the context of the Suzuki–Miyaura cross-coupling mechanism,[35] we reasoned for case I that a more rapid oxidative addition step for electron-rich dialkylbiarylphosphine or trialkylphosphine ligands did not confer an advantage over L4 in terms of rate or yield
We first measured the combined amount of 6 and 12 remaining as a function of time at 110 °C, under the conditions previously employed to examine the protodeboronation of 6 (Fig. 8, red curve).‡ Combining these results with the measurements of the rate of protodeboronation of 6 (Fig. 8, blue curve), we proposed a pseudo-first order kinetic model that allowed for estimation of the availability of boronic acid 6 over time
Summary
Heralding the era of the “robo-chemist”,1 automated chemical development continues to gain prominence in academia and industry, ushering in novel ways to synthesize libraries, optimize reaction conditions, and evaluate kinetics.[2,3] Such growth in popularity, has been met with growing skepticism[4] that the job of the chemist could ever truly be replaced with a robot. Drawing especially from this last point, recent research has steered not necessarily in the direction of replacing the chemist with automation but instead in using automation to help guide the decision-making process of the chemist, helping to minimize the number of reactions necessary to achieve a satisfactory result.[5] To this end, there has been an emergence of automated feedback systems for reaction development which use flow chemistry[6] together with real-time analytical data to optimize reactions in lieu of undirected screening.[7] The use of flow allows for accurate control of reaction conditions, mixing, and heat transfer[8] and facilitates access to more hazardous and/or extreme conditions than those that could be achieved in batch.[9] Optimization algorithms controlling reaction conditions and interpreting online data can direct the system toward higher yields[10] and improved understanding of reaction kinetics.[11]
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