Dataset for "Enabling High Throughput Kinetic Experimentation by Using Flow as a Differential Kinetic Technique"

Dataset

Description

Kinetic data is most commonly collected through the generation of time-series data under either batch or flow conditions. Currently, existing methods to generate kinetic data in flow collect integral data (concentration over time) only. Here, we report a method for the rapid and direct collection of differential kinetic data (direct measurement of rate) in flow by performing a series of instantaneous rate measurements on sequential small-scale reactions. This technique decouples the time required to generate a full kinetic profile from the time required for a reaction to reach completion, effectively enabling an approach towards high throughput kinetic experimentation. In addition, comparison of kinetic profiles constructed at different residence times allows the robustness of homogeneously catalysed reactions to be interrogated. This approach makes use of a segmented flow platform which was shown to quantitatively reproduce batch kinetic data. The proline mediated aldol reaction was chosen as a model reaction to perform a high throughput kinetic screen of 216 kinetic profiles in 90 hours, one every 25 minutes or 57 profiles per 24 hours, which would have taken an estimated continuous 3500 hours in batch, a significant increase in experimental throughput matched by an equal reduction in material consumption.

File Contents:
1) Python files and jupyter notebooks required for SPKA
2) Excel files (.xlsx and .csv) containing SPKA profiles
3) IR raw data (Mettler Toledo .icir files as well as exported .txt files)
4) Agilent OpenLab HPLC sequence, method, and injector programme files
Date made available31 Jul 2023
PublisherQueen's University Belfast
Date of data production01 Jun 2022 - 01 Jul 2023

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