Przemyslaw Klups

Precipitation hardened CuCrZr will be used in heat-sink components in the ITER tokomak and is a primary candidate for EU DEMO. The measurement of mechanical properties of irradiated CuCrZr using conventional, standardised techniques is difficult due to the challenges involved in working with radioactive material and the relatively large specimen size required. Spherical nano-indentation offers a technique to measure stress-strain properties from far smaller volumes than conventional tests. In this work, CuCrZr has been heat-treated at different temperatures to vary the Cr precipitate size and spacing. Spherical nano-indentation using multiple tip radii was then used to produce stress-strain curves for all samples, from which values of initial flow stress were calculated. It was found that there was a strong indentation size effect (ISE) in the stress required to initiate plasticity, however at higher indentation strains the flow stress became constant for tip radii, R, ≥8 μm. This suggests that at the initiation of plastic deformation the ISE is dominated by dislocation source activation but in later stages the interaction with microstructural material length-scales dominate the measured mechanical strength. The mechanical response of these small-scale tests is governed by multiple mechanisms, which convolute interpretation of data and must be considered when measuring the effects of irradiation on the mechanical properties.

One of the main capabilities of atom probe tomography (APT) is the ability to not only identify but also characterize early stages of precipitation at length scales that are not achievable by other techniques. One of the most popular methods to identify nanoscale clustering in APT data, based on the density-based spatial clustering of applications with noise (DBSCAN), is used extensively in many branches of research. However, it is common that not all of the steps leading to the selection of certain parameters used in the analysis are reported. Without knowing the rationale behind parameter selection, it may be difficult to compare cluster parameters obtained by different researchers. In this work, a simple open-source tool, PosgenPy, is used to justify cluster search parameter selection via providing a systematic sweep through parameter values with multiple randomizations to minimize a false-positive cluster ratio. The tool is applied to several different microstructures: a simulated material system and two experimental datasets from a low-alloy steel . The analyses show how values for the various parameters can be selected to ensure that the calculated cluster number density and cluster composition are accurate.