Computational prediction of core-level spectroscopy of metal-organic interfaces to reveal chemical interactions, bonding and behaviours

Abstract

Molecules adsorbed onto metal surfaces, especially carbon-based aromatic molecules, can provide systems that offer tunable properties and can be used in organic light-emitting diodes (OLEDs). It is important to understand the behaviour of these systems at a molecular level in order to rationally engineer interfaces with specific properties. Core-level spectroscopy can provide a beneficial method to probe aspects of molecule-metal interfaces such as geometrical structure, stability, chemical bonding and electronic structure. X-ray photoelectron spectroscopy (XPS) and near-edge absorption fine structure (NEXAFS) spectroscopy can be used in tandem to gain significant insight into the studied system. However, the resulting spectra from these techniques can often prove hard to fully analyse as they contain multiple close-lying features and loss of clarity from broadening. This is where simulations of spectra can come in to help to disentangle and interpret spectra. This thesis establishes practical simulation workflows to predict XPS and NEXAFS spectra of metal-organic interfaces based on Density Functional Theory (DFT). These methods are applied to study the adsorption of aromatic adsorbates on metal surfaces, two-dimensional networks, and an oxidised diamond surface. As part of this work, the assessment of the performance and accuracy of simulations against experiment was carried out. Core-level simulations on various systems were performed to rationalise experimental findings on structure, stability, and surface chemical bonding.

Samuel J. Hall

Postdoctoral Asisstant

Computational chemist specialising in electronic structure calculations and machine learning for core-level x-ray spectroscopy simulations.