Probing atomic environments in alloys by electron spectroscopy

Abstract

In alloys exhibiting substitutional disorder, the variety of atomic environments manifests itself as a 'disorder broadening' in their core level binding energy spectra. Disorder broadening can be measured experimentally, and in principle can be used to deduce information about specific atomic environments within a sample. However, progress in this endeavor is hampered by the lack of a model for this phenomenon which can treat complex systems. In this work we describe such a model. The model is used to elucidate the relationship between charge transfer, atomic environment, and disorder broadening in complex systems, with a focus on the problem of characterizing the interface quality of CuNi multilayers. We also validate the model against the results of ab initio electronic structure calculations. Several counterintuitive aspects of the disorder broadening phenomenon are uncovered, an understanding of which is essential for the correct interpretation of experimental results. For instance, it is shown that systems with inhomogeneous concentration profiles can exhibit disorder broadenings significantly larger than random alloys. Furthermore in some systems a 'disorder narrowing' is even possible.

DL_MONTE: a multipurpose code for Monte Carlo simulation

“…This ∆F was calculated in the N P T ensemble using a system size of N = 216 spheres and 'isotropic' volume moves which preserve the shape of the system. The benchmark ∆F was calculated using the LSMC code in [62]. (3) ∆F between the hcp and fcc phases of the Lennard-Jones solid at pressure P = 0 and temperature T = 0.1ε/k B , where ε is the well depth of the Lennard-Jones potential and k B is the Boltzmann constant.…”

“…This ∆F was calculated in the N P T ensemble using a system size of N = 216 particles and isotropic volume moves. The benchmark ∆F was was calculated using the LSMC code in [62]. (4) ∆F between the hcp and fcc plastic crystal phases of hard dumbbells.…”

“…where ∆E(σ) is the energy change upon performing a switch move from configuration σ. It turns out that this order parameter does the job of distinguishing both phases, as well as, when used with switch moves, defining an efficient path between the phases -see [62] for more details.…”

“…2 A generalisation of LSMC, named phase-switch Monte Carlo, can be used to evaluate the free energy difference between a solid and a fluid. Note that in the manual, source code and training materials for DL MONTE the term phase-switch Monte insight into the phase behaviour of a wide range of systems (see [62] for a brief review of previous LSMC applications). Moreover it has been used to develop force fields which accurately capture the locations of phase transitions [63,64].…”