Mapping the local stoichiometry in Cu nanoparticles during controlled oxidation by STEM-EELS spectral images

Abstract

Copper nanoparticles (NPs) can be coupled with cuprous oxide, combining photoelectrocatalytic properties with a broad range optical absorption. The present study aims to correlate the changes in morphology, electronic structure and plasmonic properties of Cu NPs at different stages of oxidation. This work demonstrates the possibility to monitor the NPs oxidation at the nanometric level using STEM-EELS spectral maps analyzed with machine learning algorithms. The oxidation process is explored by exposing Cu NPs to air plasma, revealing systematic changes in morphology and composition. Initial plasma exposure creates a Cu₂O shell, while prolonged exposure results in hollow structures with a CuO shell. In this study, the procedures to obtain a material with the Cu₂O surface stoichiometry and with absorption extended to the near infrared range are identified. Moreover, this study introduces a novel application of machine learning clustering techniques to analyze the morphological and chemical evolution of a nanostructured sample.