科研之友
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摘要
Automated continuous flow systems coupled with online analysis and feedback have been previously demonstrated to model and optimize chemical syntheses with little a priori 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 D-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 a posteriori 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.
- 出版日期2012-11
