Information-driven catalyst design based on high-throughput intrinsic kinetics

A novel methodology is presented for more comprehensive catalyst development by maximizing the acquired information rather than relying on statistical methods or tedious, elaborate experimental testing. Two dedicated high-throughput kinetics (HTK) set-ups are employed to achieve this objective, i.e., a screening (HTK-S) and a mechanistic investigation one (HTK-MI). While the former aims at evaluating a wide range of candidate catalysts, a limited selection is more elaborately investigated in the latter one. It allows focusing on an in-depth mechanistic analysis of the reaction mechanism resulting in so called kinetic descriptors and on the effect of key catalysts properties, also denoted as catalyst descriptors, on the catalyst performance. Both types of descriptors are integrated into a (micro)kinetic model that allows a reliable extrapolation towards operating conditions and catalyst properties beyond those included in the high-throughput testing. A case study on ethanol conversion to hydrocarbons is employed to illustrate the concept behind this methodology. The methodology is believed to be particularly useful for potentially large-scale chemical reactions