Data rich kinetic experimentation for deeper understanding of organic reactions.

动力学实验对于理解化学反应网络非常重要，并支持学术界和化学工业中的化学研究和工艺开发。由于用于监测化学反应的数字化工具的兴起，如HPLC、原位IR和反应量热法，可以更有效地收集动力学数据。计算机还促进了微动力学建模和机器学习的使用，以更好地理解反应网络，并补充了数据丰富的动力学实验的兴起。在该项目中，这些工具将与高通量实验和半自动流动反应器平台相结合，以更好地了解对化学工业非常重要的化学反应，重点是C（sp2）-N键的形成。所获得的见解将用于创建更好的化学反应模型，并帮助设计更便宜，更可持续的工艺。

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.