Inferring layer-by-layer composition in Au-Ag nanoparticles using a combination of X-ray Photoelectron Spectroscopy and Monte Carlo simulations

Abstract

A strategy to estimate the composition profile in the top few atom layers of a binary metal alloy nanoparticle is presented. The method is based on a combination of the X-ray Photoelectron Spectroscopy (XPS) and Monte Carlo (MC) simulations. Au-Ag NPs were synthesized via solution-phase co-reduction followed by reduction in gas phase at 400 °C. The average composition at the NP surface was measured using XPS. The number of surface atomic layers whose compositions differ from bulk was identified using MC. XPS intensities were calculated using the number of independent layers as an input to the NIST database for the Simulation of Electron Spectra for Surface Analysis (SESSA). Upon analyzing the simulated XPS intensity we conclude that not one but several layer-by-layer Au/Ag composition profiles at the surface are consistent with the measured (experimental) XPS intensities. Finally, a procedure to identify the layer-by-layer composition profile consistent with both XPS analysis and MC simulations is proposed. The segregation free energy is calculated from the layer-by-layer composition profile identified. The resulting atomic scale information can guide first-principles prediction of catalyst activity where the elemental distribution at the surface is crucial.