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. 2016 Dec 1;1(6):658-666.
doi: 10.1039/c6re00153j. Epub 2016 Oct 18.

Suzuki-Miyaura cross-coupling optimization enabled by automated feedback

Brandon J Reizman  1 , Yi-Ming Wang  2 , Stephen L Buchwald  2 , Klavs F Jensen  1
Affiliations

Suzuki-Miyaura cross-coupling optimization enabled by automated feedback

Brandon J Reizman et al. React Chem Eng. .

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.

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Figures

Scheme 1
Scheme 1. Optimization scheme for Suzuki–Miyaura cross-couplings in the presence of 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) and THF/water.
Fig. 1
Fig. 1. (a) Concept and (b) flow diagram for automated Suzuki–Miyaura cross-coupling optimization. See ESI Fig. S1 for a complete system process and instrumentation diagram.
Fig. 2
Fig. 2. Precatalyst selection frequency by case study.
Fig. 3
Fig. 3. Optimization trajectories followed by the automated system for case I. a) DoE initialization (32 experiments); b) quadratic response surface refinement and discrete variable elimination (39 experiments); c) further response surface refinement with P1-L4 and convergence (21 experiments).
Fig. 4
Fig. 4. For case II, (a) maximum observed TON during optimization for each precatalyst as a function of temperature range (blue—30–60 °C, yellow—60–90 °C, green—90–110 °C) and (b) final response surface models predicting TON as a function of temperature for five best-performing precatalysts.
Fig. 5
Fig. 5. Optimization trajectory for case III.
Fig. 6
Fig. 6. For case IV with 1.0% P1-L1: (a) response surface for catalytic TON extracted from optimization; (b) overlay of automated screening experiments (markers) upon response surface predictions (solid line).
Scheme 2
Scheme 2. Optimization of temperature, ligand selection, and added ligand equivalents in the synthesis of 5.
Fig. 7
Fig. 7. Optimization trajectory for Scheme 2 on the basis of moles 5 per total moles ligand.
Fig. 8
Fig. 8. Experimental (markers) and model-fit (lines) kinetic profiles for 6 and 12 at 110 °C. Blue curve and diamonds – evolution of benzofuran-2-boronic acid (6) starting with 0.25 M 6. Red curve and circles – evolution of the combined benzofuran-2-boronic acid pinacol ester (12) and 6 starting with 0.25 M 12. Black curve – model-predicted evolution of intermediate 6 starting with 0.25 M 12 (see the ESI for model assumptions).
Scheme 3
Scheme 3. Effect of 2-benzofuranboron reagent and precatalyst upon synthesis of 7.
Fig. 9
Fig. 9. Optimal conditions for case IV.
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