We investigate the effect of 2D interfaces, such as water-oil or water-oil-gas interfaces, on the reactivity of photoredox catalysis. Photochemical reactions such as cyclizations, cycloadditions, dearomatization, deracemization, and carboxylation will be performed at the water-oil interface. For continuous reaction conditions, we will employ a photo-flow-spray reactor producing water-oil aerosols. 

Organic Chemistry, Synthesis
Prof. Dr. Burkhard König

C03 studies supramolecular H-bonding networks and their impact on photochemistry to establish them as tools to control and steer reactivity and selectivity in photocatalysis. The first funding period focused on creating supramolecular photocatalytic environments for polyene cyclizations, thereby uncovering an unprecedented mechanism for eosin Y-catalyzed hydrofunctionalizations. In the second funding period we will continue these investigations and expand the reaction scope to unactivated alkene and aryl functionalizations. The project will also explore the properties of photoacids in F-alcohol environments and their suitability in transformations usually requiring strong acids. 

Organic Chemistry, Synthesis
Prof. Dr. Tanja Gulder

We will develop novel quantum-chemical excited-state methods for describing vibronic effects, magnetic properties, and non-adiabatic molecular dynamics simulations based on our constraint-based orbital-optimized excited state method (COOX), make them available to the CRC, and apply them collaboratively within the CRC. In collaboration with experimental groups, new photosensitizers based on ring-contracted flavins and photocatalytic reaction mechanisms in enzymatic environments will be studied. Furthermore, we will support the development of a novel ultra-sensitive NMR technique and a time-resolved EPR method by calculating the relevant spectroscopic parameters.
 

Theoretical Chemistry, Theory
Prof. Dr. Christian Ochsenfeld

Project C07 aims to enhance our understanding of light-driven chemical reactions within the CRC by applying advanced machine learning methods. On one side, computationally efficient machine learning-driven photodynamics simulations on longer time and length scales will be used to study long-lived excited states of ring-contracted flavins and reactions at interfaces, respectively. In addition, machine learning will be used to guide rational catalyst and substrate design and will be integrated into experimental workflows to optimize reaction conditions and experimental planning.