Kinetically guided exploration of photocatalytic reactions by combining automation with in situ measurements

Photocatalysis enables valuable reactions such as synthetic transformations or energy conversion processes like water splitting. To rationally improve photocatalytic reactions, mechanistic insights are required. These can be obtained with kinetic measurements, which are, however, difficult to obtain for a large enough number of reaction conditions to provide systematic and valuable insights. To this end, we present a system for performing photocatalytic reactions that combines time-resolved in situ measurements with a fully automated process for liquid handling, light source control, dynamic feedback and automated kinetic data evaluation, enabled by an open-source Python framework. The system is applied to study photocatalytic water oxidation using [Ru(bpy)3]2+ as both photosensitizer and water oxidation catalyst, with oxygen formation being measured in situ. Using this benchmark reaction, we investigate the effect of different reaction parameters (catalyst and sacrificial oxidant concentration, irradiance, pH-value) on the rate of oxygen evolution. The results show that the automated process enables highly reproducible experiments while obtaining full time-concentration curves with a high temporal resolution (seconds) for each experiment. Using the obtained data we derive a chemical reaction network and rate constants to describe the detailed mechanism of the photocatalytic reaction. This kinetic model reveals an unexpected light-driven step to convert [Ru(bpy)3]3+ and water to hydrogen peroxide (which ultimately disproportionates to form oxygen) as well as two competing deactivation pathways, unimolecular and bimolecular decomposition, the latter of which is autocatalytic. These results demonstrate how the combination of in situ kinetic measurements and automation unlocks the data- driven exploration of the chemical space, producing novel mechanistic insights.