An Automated Continuous-Flow Platform for the Estimation of Multistep Reaction Kinetics

Abstract

Automated continuous flow systems coupled with online analysis and feedback have been previously demonstrated to model and optimize chemical syntheses with little <i>a priori</i> reaction information. However, these methods have yet to address the challenge of modeling and optimizing for product yield or selectivity in a multistep reaction network, where low selectivity toward desired product formation can be encountered. Here we demonstrate an automated system capable of rapidly estimating accurate kinetic parameters for a given reaction network using maximum likelihood estimation and a <i>D</i>-optimal design of experiments. The network studied is the series–parallel nucleophilic aromatic substitution of morpholine onto 2,4-dichloropyrimidine. To improve the precision of the estimated parameters, we demonstrate the use of the automated platform first in optimization of the yield of the less kinetically favorable 2-substituted product. Then, upon isolation of the intermediates, we use the automated system with maximum <i>a posteriori</i> estimation to minimize uncertainties in the network parameters. From considering the steps of the reaction network in isolation, the kinetic parameter uncertainties are reduced by 50%, with less than 5 g of the dichloropyrimidine substrate consumed over all experiments. We conclude that isolating pathways in the multistep reaction network is important to minimizing uncertainty for low sensitivity rate parameters

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