Characterisation of Nanoclusters on Surfaces Using Scanning Transmission Electron Microscope and Machine Learning

Abstract

Single metal atoms (SMAs) and metal nanoclusters (MNCs) have attracted considerable interest in recent years due to their unique properties; their properties at the surface are important in many potential applications, such as catalysis, sensing, and thin film fabrication. However, it remains unclear what determines metal cluster formation in this process. Therefore, a series of experiments have been designed and performed to address the mystery of filtered and non-filtered metal clusters formed in magnetron sputtering with a size-selected cluster source using several metals, tantalum (Ta), silver (Ag), copper (Cu), and platinum (Pt). We describe how techniques for answering these questions are developed by focusing on mechanisms governed by gas pressure, magnetron power, temperature, substrate voltage, condensation length, and cooling the system. This study employs high-resolution aberration-corrected scanning transmission electron microscopy (AC-STEM) to acquire images with a sufficient spatial resolution to separate individual atoms and characterise metal clusters deposited on different supports. It is possible to study atoms/clusters on surfaces directly by capturing annular dark field (ADF)-STEM images at atomic resolution. This thesis focuses on studying the distribution of single atoms and nanoclusters on the surface under different conditions. Furthermore, STEM stereo imaging was employed to examine the height of Ta nanoclusters on different substrates. Additionally, two machine learning models were developed and evaluated in order to predict the position of clusters in a single simulated image and the parallax shifts in stereo images.