Catalysis, chemistry, and automation

Abstract

Catalysis is a critical enabling technology that directly contributes to higher standards of living. This is a remarkable feat for a technology that most consumers have no direct contact with and perhaps only vaguely know from their car’s catalytic converter. Even many technical users regard catalysts as ideal substances that promote a target transformation without being consumed in the reaction. Reality, however, is much more complex because catalysts can also produce undesirable side-products or stop working before the target reaction is complete. This dissertation explores such complexity.
The aim of this work was to study real catalytic systems under relevant reaction conditions. This challenge was approached from two different directions, and the dissertation is therefore divided into two parts. Part I describes our study of non-noble Mn complexes as catalysts for a variety of reduction chemistries. Particular attention was given to the study of deactivation phenomena to understand why some catalysts stop operating early in their lifetime. This work led to catalytic methods that enable improved operation at significantly lower catalyst loading. The second part of this dissertation focuses on the development and application of automation and data-rich experimentation methods for catalysis R&D. An automated reaction analysis platform was developed that simultaneously produces kinetic data and several representative data streams of operando spectroscopy. These tools were used to study and optimise the performance of a variety of chemistries, and are expected to provide the required dense data for contemporary data-driven methods.