3D oxygen vacancy distribution and defect-property relations in an oxide heterostructure.

Oxide heterostructures exhibit a vast variety of unique physical properties. Examples are unconventional superconductivity in layered nickelates and topological polar order in (PbTiO3)n/(SrTiO3)n superlattices. Although it is clear that variations in oxygen content are crucial for the electronic correlation phenomena in oxides, it remains a major challenge to quantify their impact. Here, we measure the chemical composition in multiferroic (LuFeO3)9/(LuFe2O4)1 superlattices, mapping correlations between the distribution of oxygen vacancies and the electric and magnetic properties. Using atom probe tomography, we observe oxygen vacancies arranging in a layered three-dimensional structure with a local density on the order of 1014 cm-2, congruent with the formula-unit-thick ferrimagnetic LuFe2O4 layers. The vacancy order is promoted by the locally reduced formation energy and plays a key role in stabilizing the ferroelectric domains and ferrimagnetism in the LuFeO3 and LuFe2O4 layers, respectively. The results demonstrate pronounced interactions between oxygen vacancies and the multiferroic order in this system and establish an approach for quantifying the oxygen defects with atomic-scale precision in 3D, giving new opportunities for deterministic defect-enabled property control in oxide heterostructures.

Deflecting Dendrites by Introducing Compressive Stress in Li7La3Zr2O12 Using Ion Implantation.

Lithium dendrites belong to the key challenges of solid-state battery research. They are unavoidable due to the imperfect nature of surfaces containing defects of a critical size that can be filled by lithium until fracturing the solid electrolyte. The penetration of Li metal occurs along the propagating crack until a short circuit takes place. It is hypothesized that ion implantation can be used to introduce stress states into Li6.4La3Zr1.4Ta0.6O12 which enables an effective deflection and arrest of dendrites. The compositional and microstructural changes associated with the implantation of Ag-ions are studied via atom probe tomography, electron microscopy, and nano X-ray diffraction indicating that Ag-ions can be implanted up to 1 µm deep and amorphization takes place down to 650-700 nm, in good agreement with kinetic Monte Carlo simulations. Based on diffraction results pronounced stress states up to -700 MPa are generated in the near-surface region. Such a stress zone and the associated microstructural alterations exhibit the ability to not only deflect mechanically introduced cracks but also dendrites, as demonstrated by nano-indentation and galvanostatic cycling experiments with subsequent electron microscopy observations. These results demonstrate ion implantation as a viable technique to design "dendrite-free" solid-state electrolytes for high-power and energy-dense solid-state batteries.

On the Use of a Cluster Identification Method and a Statistical Approach for Analyzing Atom Probe Tomography Data for GP Zones in Al-Zn-Mg(-Cu) Alloys.

Early-stage clustering in two Al-Mg-Zn(-Cu) alloys has been investigated using atom probe tomography and transmission electron microscopy. Cluster identification by the isoposition method and a statistical approach based on the pair correlation function have both been applied to estimate the cluster size, composition, and volume fraction from atom probe data sets. To assess the accuracy of the quantification of clusters of different mean sizes, synthesized virtual data sets were used, accounting for a simulated degraded spatial resolution. The quality of the predictions made by the two complementary methods is discussed, considering the experimental and simulated data sets.

Atomic Structure of Hardening Precipitates in Al-Mg-Si Alloys: Influence of Minor Additions of Cu and Zn.

Shifting toward sustainability and low carbon emission necessitates recycling. Aluminum alloys can be recycled from postconsumer scrap with approximately 5% of the energy needed to produce the same amount of primary alloys. However, the presence of certain alloying elements, such as copper and zinc, as impurities in recycled Al-Mg-Si alloys is difficult to avoid. This work has investigated the influence of tiny concentrations of Cu (0.05 wt %) and Zn (0.06 wt %), individually and in combination, on the precipitate crystal structures in Al-Mg-Si alloys in peak aged and overaged conditions. To assess whether such concentrations can affect the hardening precipitate structures, atomic resolution high-angle annular dark-field scanning transmission electron microscopy and atom probe tomography were adopted. The results indicate that low levels of Cu or Zn have a significant influence. Both elements showed a relatively high tendency to incorporate into precipitate structures, where Cu occupies specific atomic sites, creating its own local atomic configurations. However, Zn exhibited distinct behavior through the formation of extended local areas with 2-fold symmetry and mirror planes, not previously observed in precipitates in Al-Mg-Si alloys. Incorporation of Cu and/or Zn will influence the precipitates' electrochemical potential relative to matrix- and precipitate-free zones and thus the corrosion resistance. Furthermore, the presence of Cu/Zn structures (e.g., ß'Cu, Q'/C) enhances the thermal stability of these precipitates and, accordingly, the mechanical properties of the material. The results obtained from this work are highly relevant to the topic of recycling of aluminum alloys, where accumulation of certain alloying elements is almost unavoidable; thus, tight compositional control might be critical to avoid quality degradation.

Introducing a Dynamic Reconstruction Methodology for Multilayered Structures in Atom Probe Tomography.

Atom probe tomography (APT) is a powerful three-dimensional nanoanalyzing microscopy technique considered key in modern materials science. However, progress in the spatial reconstruction of APT data has been rather limited since the first implementation of the protocol proposed by Bas et al. in 1995. This paper proposes a simple semianalytical approach to reconstruct multilayered structures, i.e., two or more different compounds stacked perpendicular to the analysis direction. Using a field evaporation model, the general dynamic evolution of parameters involved in the reconstruction of this type of structure is estimated. Some experimental reconstructions of different structures through the implementation of this method that dynamically accommodates variations in the tomographic reconstruction parameters are presented. It is shown both experimentally and theoretically that the depth accuracy of reconstructed APT images is improved using this method. The method requires few parameters in order to be easily usable and substantially improves atom probe tomographic reconstructions of multilayered structures.

Quantitative Mapping of Chemical Defects at Charged Grain Boundaries in a Ferroelectric Oxide.

Polar discontinuities, as well as compositional and structural changes at oxide interfaces can give rise to a large variety of electronic and ionic phenomena. In contrast to earlier work focused on domain walls and epitaxial systems, this work investigates the relation between polar discontinuities and the local chemistry at grain boundaries in polycrystalline ferroelectric ErMnO3 . Using orientation mapping and scanning probe microscopy (SPM) techniques, the polycrystalline material is demonstrated to develop charged grain boundaries with enhanced electronic conductance. By performing atom probe tomography (APT) measurements, an enrichment of erbium and a depletion of oxygen at all grain boundaries are found. The observed compositional changes translate into a charge that exceeds possible polarization-driven effects, demonstrating that structural phenomena rather than electrostatics determine the local chemical composition and related changes in the electronic transport behavior. The study shows that the charged grain boundaries behave distinctly different from charged domain walls, giving additional opportunities for property engineering at polar oxide interfaces.

Development of Wide Field of View Three-Dimensional Field Ion Microscopy and High-Fidelity Reconstruction Algorithms to the Study of Defects in Nuclear Materials.

This article presents a fast and highly efficient algorithm developed to reconstruct a three-dimensional (3D) volume with a high spatial precision from a set of field ion microscopy (FIM) images, and specific tools developed to characterize crystallographic lattice and defects. A set of FIM digital images and image processing algorithms allow the construction of a 3D reconstruction of the sample at the atomic scale. The capability of the 3D FIM to resolve the crystallographic lattice and the finest defects in metals opens a new way to analyze materials. This spatial precision was quantified on tungsten, analyzed at different analyzing conditions. A specific data mining tool, based on Fourier transforms, was also developed to characterize lattice distortions in the reconstructed volumes. This tool has been used in simulated and experimental volumes to successfully locate and characterize defects such as dislocations and grain boundaries.

Preferential Evaporation in Atom Probe Tomography: An Analytical Approach.

Atom probe tomography (APT) analysis conditions play a major role in the composition measurement accuracy. Preferential evaporation (PE), which significantly biases the apparent composition, more than other well-known phenomena in APT, is strongly connected to those analysis conditions. One way to optimize them, in order to have the most accurate measurement, is therefore to be able to predict and then to estimate their influence on the apparent composition. An analytical model is proposed to quantify the PE. This model is applied to three different alloys such as NiCu, FeCrNi, and FeCu. The model explains not only the analysis temperature dependence, as in an already existing model, but also the dependence to the pulse fraction and the pulse frequency. Moreover, the model can also provide an energetic constant directly linked to the energy barrier required to field evaporate atom from the sample surface.

Interpreting nanovoids in atom probe tomography data for accurate local compositional measurements.

Quantifying chemical compositions around nanovoids is a fundamental task for research and development of various materials. Atom probe tomography (APT) and scanning transmission electron microscopy (STEM) are currently the most suitable tools because of their ability to probe materials at the nanoscale. Both techniques have limitations, particularly APT, because of insufficient understanding of void imaging. Here, we employ a correlative APT and STEM approach to investigate the APT imaging process and reveal that voids can lead to either an increase or a decrease in local atomic densities in the APT reconstruction. Simulated APT experiments demonstrate the local density variations near voids are controlled by the unique ring structures as voids open and the different evaporation fields of the surrounding atoms. We provide a general approach for quantifying chemical segregations near voids within an APT dataset, in which the composition can be directly determined with a higher accuracy than STEM-based techniques.

Enhancing Element Identification by Expectation-Maximization Method in Atom Probe Tomography.

This paper describes an alternative way to assign elemental identity to atoms collected by atom probe tomography (APT). This method is based on Bayesian assignation of label through the expectation-maximization method (well known in data analysis). Assuming the correct shape of mass over charge peaks in mass spectra, the probability of each atom to be labeled as a given element is determined, and is used to enhance data visualization and composition mapping in APT analyses. The method is particularly efficient for small count experiments with a low signal to noise ratio, and can be used on small subsets of analyzed volumes, and is complementary to single-ion decomposition methods. Based on the selected model and experimental examples, it is shown that the method enhances our ability to observe and extract information from the raw dataset. The experimental case of the superimposition of the Si peak and N peak in a steel is presented.

Atom Probe Tomography Interlaboratory Study on Clustering Analysis in Experimental Data Using the Maximum Separation Distance Approach.

We summarize the findings from an interlaboratory study conducted between ten international research groups and investigate the use of the commonly used maximum separation distance and local concentration thresholding methods for solute clustering quantification. The study objectives are: to bring clarity to the range of applicability of the methods; identify existing and/or needed modifications; and interpretation of past published data. Participants collected experimental data from a proton-irradiated 304 stainless steel and analyzed Cu-rich and Ni-Si rich clusters. The datasets were also analyzed by one researcher to clarify variability originating from different operators. The Cu distribution fulfills the ideal requirements of the maximum separation method (MSM), namely a dilute matrix Cu concentration and concentrated Cu clusters. This enabled a relatively tight distribution of the cluster number density among the participants. By contrast, the group analysis of the Ni-Si rich clusters by the MSM was complicated by a high Ni matrix concentration and by the presence of Si-decorated dislocations, leading to larger variability among researchers. While local concentration filtering could, in principle, tighten the results, the cluster identification step inevitably maintained a high scatter. Recommendations regarding reporting, selection of analysis method, and expected variability when interpreting published data are discussed.

An Automated Computational Approach for Complete In-Plane Compositional Interface Analysis by Atom Probe Tomography.

We introduce an efficient, automated computational approach for analyzing interfaces within atom probe tomography datasets, enabling quantitative mapping of their thickness, composition, as well as the Gibbsian interfacial excess of each solute. Detailed evaluation of an experimental dataset indicates that compared with the composition map, the interfacial excess map is more robust and exhibits a relatively higher resolution to reveal compositional variations. By field evaporation simulations with a predefined emitter mimicking the experimental dataset, the impact of trajectory aberrations on the measurement of the thickness, composition, and interfacial excess of the decorated interface are systematically analyzed and discussed.

Enhanced dynamic reconstruction for atom probe tomography.

Many studies have focussed on the optimization of atom probe tomography data reconstruction. In this article, an enhanced dynamic reconstruction algorithm based on the approach initially developed by Gault et al. in 2011 is proposed. The dynamic reconstruction takes into account the evolution of the reconstruction parameters during the field evaporation of the sample, contrary to the standard reconstruction protocol, for example proposed by Bas et al. in 1995. Here, the evolution of reconstruction parameters is retrieved by field evaporation simulation. This approach allows a complete tabulation of the parameters evolution as a function of the analyzed sample morphology and its initial microstructure. The only inputs for experimental data are the voltage curve and some simple morphological parameters of the sample. This will be demonstrated by applying this algorithm to some experimental cases. Drastic optimization of the spatial accuracy in reconstructed datasets is experimentally and theoretically demonstrated.

Analysis of Radiation Damage in Light Water Reactors: Comparison of Cluster Analysis Methods for the Analysis of Atom Probe Data.

Irradiation of reactor pressure vessel (RPV) steels causes the formation of nanoscale microstructural features (termed radiation damage), which affect the mechanical properties of the vessel. A key tool for characterizing these nanoscale features is atom probe tomography (APT), due to its high spatial resolution and the ability to identify different chemical species in three dimensions. Microstructural observations using APT can underpin development of a mechanistic understanding of defect formation. However, with atom probe analyses there are currently multiple methods for analyzing the data. This can result in inconsistencies between results obtained from different researchers and unnecessary scatter when combining data from multiple sources. This makes interpretation of results more complex and calibration of radiation damage models challenging. In this work simulations of a range of different microstructures are used to directly compare different cluster analysis algorithms and identify their strengths and weaknesses.