Ben Jenkins

As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative hydrogen (H) analysis at high spatial resolution and, if possible, at the atomic scale. H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enabling H mapping including local hydrogen concentration analyses at specific microstructural features is essential for understanding the multiple ways that H affect the properties of materials including embrittlement mechanisms and their synergies. In addition, spatial mapping and quantification of hydrogen isotopes is essential to accurately predict tritium inventory of future fusion power plants thus ensuring their safe and efficient operation. Atom probe tomography (APT) has the intrinsic capability to detect H and deuterium (D), and in principle the capacity for performing quantitative mapping of H within a material's microstructure. Yet, the accuracy and precision of H analysis by APT remain affected by complex field evaporation behavior and the influence of residual hydrogen from the ultrahigh vacuum chamber that can obscure the signal of H from within the material. The present article reports a summary of discussions at a focused workshop held at the Max-Planck Institute for Sustainable Materials in April 2024. The workshop was organized to pave the way to establishing best practices in reporting APT data for the analysis of H. We first summarize the key aspects of the intricacies of H analysis by APT and then propose a path for better reporting of the relevant data to support interpretation of APT-based H analysis in materials.

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.

Interfaces play critical roles in materials and are usually both structurally and compositionally complex microstructural features. The precise characterization of their nature in three-dimensions at the atomic scale is one of the grand challenges for microscopy and microanalysis, as this information is crucial to establish structure–property relationships. Atom probe tomography is well suited to analyzing the chemistry of interfaces at the nanoscale. However, optimizing such microanalysis of interfaces requires great care in the implementation across all aspects of the technique from specimen preparation to data analysis and ultimately the interpretation of this information. This article provides critical perspectives on key aspects pertaining to spatial resolution limits and the issues with the compositional analysis that can limit the quantification of interface measurements. Here, we use the example of grain boundaries in steels; however, the results are applicable for the characterization of grain boundaries and transformation interfaces in a very wide range of industrially relevant engineering materials.

The local electrode atom probe (LEAP) has become the primary instrument used for atom probe tomography measurements. Recent advances in detector and laser design, together with updated hit detection algorithms, have been incorporated into the latest LEAP 5000 instrument, but the implications of these changes on measurements, particularly the size and chemistry of small clusters and elemental segregations, have not been explored. In this study, we compare data sets from a variety of materials with small-scale chemical heterogeneity using both a LEAP 3000 instrument with 37% detector efficiency and a 532-nm green laser and a new LEAP 5000 instrument with a manufacturer estimated increase to 52% detector efficiency, and a 355-nm ultraviolet laser. In general, it was found that the number of atoms within small clusters or surface segregation increased in the LEAP 5000, as would be expected by the reported increase in detector efficiency from the LEAP 3000 architecture, but subtle differences in chemistry were observed which are attributed to changes in the way multiple hit detection is calculated using the LEAP 5000.