Vicente J. Araullo-Peters

Dynamic reconstruction for atom probe tomography

Progress in the reconstruction for atom probe tomography has been limited since the first implementation of the protocol proposed by Bas et al. in 1995. This approach and those subsequently developed assume that the geometric parameters used to build the three-dimensional atom map are constant over the course of an analysis. Here, we test this assumption within the analyses of low-alloyed materials. By building upon methods recently proposed to measure the tomographic reconstruction parameters, we demonstrate that this assumption can introduce significant limitations in the accuracy of the analysis. Moreover, we propose a strategy to alleviate this problem through the implementation of a new reconstruction algorithm that dynamically accommodates variations in the tomographic reconstruction parameters.

A new systematic framework for crystallographic analysis of atom probe data

In this article, after a brief introduction to the principles behind atom probe crystallography, we introduce methods for unambiguously determining the presence of crystal planes within atom probe datasets, as well as their characteristics: location; orientation and interplanar spacing. These methods, which we refer to as plane orientation extraction (POE) and local crystallography mapping (LCM) make use of real-space data and allow for systematic analyses. We present here application of POE and LCM to datasets of pure Al, industrial aluminium alloys and doped-silicon. Data was collected both in DC voltage mode and laser-assisted mode (in the latter of which extracting crystallographic information is known to be more difficult due to distortions). The nature of the atomic planes in both datasets was extracted and analysed.

New atom probe approaches to studying segregation in nanocrystalline materials

Atom probe is a technique that is highly suited to the study of nanocrystalline materials. It can provide accurate atomic-scale information about the composition of grain boundaries in three dimensions. In this paper we have analysed the microstructure of a nanocrystalline super-duplex stainless steel prepared by high pressure torsion (HPT). Not all of the grain boundaries in this alloy display obvious segregation, making visualisation of the microstructure challenging. In addition, the grain boundaries present in the atom probe data acquired from this alloy have complex shapes that are curved at the scale of the dataset and the interfacial excess varies considerably over the boundaries, making the accurate characterisation of the distribution of solute challenging using existing analysis techniques. In this paper we present two new data treatment methods that allow the visualisation of boundaries with little or no segregation, the delineation of boundaries for further analysis and the quantitative analysis of Gibbsian interfacial excess at boundaries, including the capability of excess mapping.

Restoring the lattice of Si-based atom probe reconstructions for enhanced information on dopant positioning.

The following manuscript presents a novel approach for creating lattice based models of Sb-doped Si directly from atom probe reconstructions for the purposes of improving information on dopant positioning and directly informing quantum mechanics based materials modeling approaches. Sophisticated crystallographic analysis techniques are used to detect latent crystal structure within the atom probe reconstructions with unprecedented accuracy. A distortion correction algorithm is then developed to precisely calibrate the detected crystal structure to the theoretically known diamond cubic lattice. The reconstructed atoms are then positioned on their most likely lattice positions. Simulations are then used to determine the accuracy of such an approach and show that improvements to short-range order measurements are possible for noise levels and detector efficiencies comparable with experimentally collected atom probe data.

A Round Robin Experiment: Analysis of Solute Clustering from Atom Probe Tomography Data.

1. Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, U.S.A. 2. Groupe de Physique des Matériaux, UMR CNRS 6634, Université de Rouen, Saint Etienne du Rouvray Cedex, France 4. NRC “Kurchatov Institute”, Moscow, Russia 3. Institute of Nuclear Safety System, Inc., Kyoto, Japan 5. Commissariat à l’Energie Atomique (CEA), Saclay, France 6. Department of Materials, University of Oxford, U.K. 7. Institute for Materials Research, Tohoku University, Oarai Japan 8. Materials Science Research Laboratory, Central Research Institute of Electric Power Industry, Nagasaka, Japan 9. Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA, U.S.A. 10. Department of Physics, Chalmers University of Technology, Chalmers, Sweden 11. Departement Métallurgie, EDF, Moret sur Loing, France 12. Electric Power Research Institute, Palo Alto, CA, U.S.A.

The effect orientation of features in reconstructed atom probe data on the resolution and measured composition of T1 plates in an A2198 aluminium alloy

Artefacts in atom probe tomography can impact the compositional analysis of microstructure in atom probe studies. To determine the integrity of information obtained, it is essential to understand how the positioning of features influences compositional analysis. By investigating the influence of feature orientation within atom probe data on measured composition in microstructural features within an AA2198 Al alloy, this study shows differences in the composition of T1 (Al2CuLi) plates that indicates imperfections in atom probe reconstructions. The data fits a model of an exponentially-modified Gaussian that scales with the difference in evaporation field between solutes and matrix. This information provides a guide for obtaining the most accurate information possible.

On the Use of Density-Based Algorithms for the Analysis of Solute Clustering in Atom Probe Tomography Data

Because atom probe tomography (APT) provides three-dimensional reconstructions of small volumes by resolving atomic chemical identities and positions, it is uniquely suited to analyze solute clustering phenomena in materials. A number of approaches have been developed to extract clustering information from the 3D reconstructed dataset, and numerous reports can be found applying these methods to a wide variety of materials questions. However, results from clustering analyses can differ significantly from one report to another, even when performed on similar microstructures, raising questions about the reliability of APT to quantitatively describe solute clustering. In addition, analysis details are often not provided, preventing independent confirmation of the results. With the number of APT research groups growing quickly, the APT community recognizes the need for educating new users about common methods and artefacts, and for developing analysis and data reporting protocols that address issues of reproducibility, errors, and variability. To this end, a round robin experiment was organized among ten different international institutions. The goal is to provide a consistent framework for the analysis of irradiated stainless steels using APT. Through the development of more reliable and reproducible data analysis and through communication, this project also aims to advance the understanding between irradiated microstructure and materials performance by providing more complete quantitative microstructural input for modeling. The results, methods, and findings of this round robin will also apply to other clustering phenomena studied using APT, beyond the theme of radiation damage.