Ben Jenkins

Changes in the chemistry of internal interfaces, particularly grain boundaries, are known to affect the macroscopic properties of a wide range of material systems. Solute segregation to grain boundaries is dependent on, amongst other factors, the physical structure of the grain boundary. We demonstrate how complementary use of transmission Kikuchi diffraction (TKD) and atom probe tomography (APT) can provide a more holistic characterisation of grain boundaries in a variety of materials. Structural information is reported from TKD data for a model steel, a titanium alloy, and a multicrystalline silicon sample. Complementary APT analyses are used to determine the segregation behaviour to these interfaces. A novel specimen preparation protocol allows for the grain boundary to be positioned more reliably within the apex of an APT specimen. Meanwhile, a method that allows a grain boundary’s five macroscopic degrees of freedom to be determined from TKD data alone is also proposed.

A set of low alloy model reactor pressure vessel steels, with systematic variations in their Mn, Ni, and Si contents, were neutron-irradiated to high fluence (1.4 × 1020 n/cm2) in the Advanced Test Reactor at Idaho at 290°C and a flux of 3.6 × 1012 n/cm2s. The alloys were analysed using atom probe tomography and solute clusters were observed in each alloy, including in one alloy that contained low nominal levels of Mn (0.04 at. %) and Si (0.06 at. %). Changes in the mechanical properties of the alloys were correlated with cluster volume fractions. Whilst the effect of nominal composition was observed to influence cluster composition, cluster nucleation site was not observed to affect composition. Several grain boundaries were also analysed and the segregation behaviour of certain elements is discussed.

An automated way to accurately and reproducibly identify edge clusters within atom probe tomography datasets has been developed. The alpha-hull algorithm is used to generate a concave alpha-shape around an atom probe dataset. Information from core-linkage cluster searches is used, in combination with the calculated alpha-shape, to determine which clusters are on the edge of the dataset. The potential effects that not removing edge clusters may have on calculated cluster sizes, number densities and compositions is discussed. The viability of the methodology is demonstrated via application to real datasets, one of which was a non-standard shape. The sensitivity of the method to user parameter selection is explored. Sampling fractions >0.1% and an alpha value, used to make the alpha shape, greater than twice the maximum measured nearest neighbour distance were found to be suitable.