Utilising Machine Learning for the Automated Characterisation of In Situ Electron Microscopy Experiments with Catalytic Systems

The data analysis workflow for in situ electron microscopy experiments can require a lot of human-intensive and repetitive effort. The generation of Python-based scripts that incorporate simple machine learning algorithms are quite well established in biological sciences but not often utilised in the study of catalytic systems. Such scripted analysis is not only more efficient, but readily reproducible and allows a wide range of quantitative results to be reported, including but not limited to average and total particle size, particle counting and particle size distributions. In this work we utilise these tools to examine the effect of cycling reducing and oxidising atmospheres on CuO nanoparticles.