Oral Presentation 26th ACMM “2020 Visions in Microscopy”

The simplified multicomponent short-range order parameter for measuring the structure of solid solutions (#38)

Anna Ceguerra 1 2 3 , William J Davids 1 2 3 , Jean Vereecke 4 , Mengwei He 1 2 3 , Simon P Ringer 1 2 3
  1. Australian Centre for Microscopy & Microanalysis, The University of Sydney, Sydney, NSW, Australia
  2. School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW, Australia
  3. The University of Sydney, The University Of Sydney, NSW, Australia
  4. ISAE-ENSMA, Futuroscope Chasseneuil Cedex , France

The structure of solid solutions is a mystery. Current crystallography using unit cells does not have the capacity to describe these, and therefore we have not been able to measure it effectively. The work in measuring solute clustering is a way forward, as particles are able to be linked to structure-property relationships. But what happens when the structure is non-random, but difficult to discern, e.g. in partially ordered systems?

A decade ago, a new measure called the generalised multicomponent short-range order (GM-SRO) parameter was introduced [1] to handle the intricacies of the relationships between different elements in multicomponent systems. The main strengths of this formalism is (1) it can be measured directly from atom probe tomography data [2], and (2) there is a sliding scale for interpretation, where -1 indicates shell-depletion, 0 indicates the expected distribution when the atoms are arranged randomly, and +1 indicates shell-enrichment. While it has its strengths, it also had its limitations with regards to interpretation, as the equations were different based on the elements being considered.

To this end, we proposed a simplified multicomponent short-range order (SM-SRO) parameter that simplifies the definitions of the GM-SRO. We also propose ways to interpret this measure. This includes the introduction of pα, which is similar to the pH scale in chemistry; the calculation of a p-value to determine the statistical significance of trends; and a way to normalise the SM-SRO value according to the size and composition of the dataset. We also link the SM-SRO parameter to previous works in clustering, by finding relationships between the SM-SRO parameter and the cluster size distribution through simulation.

 

  1. [1] A. V. Ceguerra †, R. C. Powles, M. P. Moody, and S. P. Ringer, "Quantitative description of atomic architecture in solid solutions: A generalized theory for multicomponent short-range order," Physical Review B, vol. 82, no. 13, p. 132201, 2010, https://doi.org/10.1103/PhysRevB.82.132201.
  2. [2] A. V. Ceguerra, M. P. Moody, R. C. Powles, T. C. Petersen, R. K. W. Marceau, and S. P. Ringer, "Short-range order in multicomponent materials," Acta Crystallographica Section A, vol. 68, no. 5, pp. 547-560, 2012, http://dx.doi.org/10.1107/S0108767312025706.