Single Nanoparticle Catalysis- Implementing Machine Learning Algorithms for the Identification of Kinetic Phase Transitions on the Surface of Individual Catalyst Nanoparticles during Surface Chemical Reactions through Nanoplasmonic Spectroscopy

Abstract

The importance of catalysis within industrial and scientific endeavours can hardly be overstated, playing a crucial role in most processes that contain chemical reactions of any kind. This ubiquitous nature of catalysts makes them very relevant for study, yet there is a clear divide between scientific understanding and practical purpose of catalysts - too little is known of the nature of catalysts at the nanoscale within atmospheric conditions. Research within single nanoparticle catalysis, a hitherto elusive field of chemical physics, aims to bridge this gap by analyzing the surface of individual nanoparticles, during reaction conditions that are relevant for practical applications, using a relatively cheap spectroscopy setup. However, the resulting data is so complex and multivariate that standard analysis methods are incapable of elucidating the relevant physical correlations between nanoparticle surface kinetics and spectroscopic results. To remedy this, this thesis’ focus is the implementation of a deep densely connected neural network and a long short-term memory recurrent neural network to identify a specific surface kinetic behaviour, known as the kinetic phase transition, within each individual nanoparticle, given only the results of spectroscopy. We find that both networks are reliable, and with a few reservations and admonissions, give generalizable results that can be applied across samples of nanoparticles.