Data rich kinetic experimentation for deeper understanding of organic reactions.

Kinetic experiments are important for understanding chemical reaction networks and underpin both chemical research and process development in academia and the chemical industry. Since the rise in digital tools for monitoring chemical reactions such as HPLC, in-situ IR and reaction calorimetry, kinetic data can be collected more efficiently. Computers also facilitate the use of microkinetic modelling as well as machine learning to better understand reaction networks and complements the rise in data-rich kinetic experiments. In this project, these tools will be combined with high throughput experimentation and a semi-automated flow reactor platform to better understand chemical reactions of high importance to the chemical industry, with an emphasis on the formation of C(sp2)-N bonds. The insights gained will be used to create better models of chemical reactivity and help design cheaper and more sustainable processes.