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Efficient Kinetic Data Acquisition and Model Prediction: Continuous Flow Microreactors, Inline Fourier Transform Infrared Spectroscopy, and Self-Modeling Curve Resolution

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posted on 27.04.2020, 21:46 by Verena Fath, Philipp Lau, Christoph Greve, Norbert Kockmann, Thorsten Röder
This work presents and evaluates an approach to obtain and model kinetic data by combining a microreactor setup and real-time reaction monitoring through inline Fourier transform infrared spectroscopy with nonsteady-state conditions and self-modeling curve resolution (SMCR). Two model reactions, imine synthesis of benzaldehyde with benzylamine and deprotonation reaction with n-butyllithium, serve as a proof of concept and additionally demonstrate the method’s broad range of application, which includes simple reactions as well as complex mechanisms. Subsequent replications of the model reactions above (in terms of collection and modeling of kinetic data) using a more common approach (steady-state conditions and spectra evaluation using calibration curves) outline that the presented approach possesses greater time-efficiency compared to traditional methods (based on batch or steady-state studies), but that reliability of the resulting kinetic parameters should be reviewed carefully. However, when quick estimates are needed (analyzing the elementary reaction mechanism rather than developing a detailed scale-up model), research and industry alike may achieve significant time and cost savings through applying the outlined approach. To guide them in using this method in the most effective manner, this paper concludes by comparing two types of SMCR, soft- and hard-modeling, and argues for combining them.

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