Statistical analysis of atom probe data: detecting the early stages of solute clustering and/or co-segregation

Statistical analysis of atom probe data has improved dramatically in the last decade and it is now possible to determine the size, the number density and the composition of individual clusters or precipitates such as those formed in reactor pressure vessel (RPV) steels during irradiation. However, the characterisation of the onset of clustering or co-segregation is more difficult and has traditionally focused on the use of composition frequency distributions (for detecting clustering) and contingency tables (for detecting co-segregation). In this work, the authors investigate the possibility of directly examining the neighbourhood of each individual solute atom as a means of identifying the onset of solute clustering and/or co-segregation. The methodology involves comparing the mean observed composition around a particular type of solute with that expected from the overall composition of the material. The methodology has been applied to atom probe data obtained from several irradiated RPV steels. The results show that the new approach is more sensitive to fine scale clustering and co-segregation than that achievable using composition frequency distribution and contingency table analyses.