A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

Complex material systems in which microstructure and microchemistry are nonuniformly dispersed require three-dimensional (3D) rendering(s) to provide an accurate determination of the physio-chemical nature of the system. Current scanning transmission electron microscope (STEM)-based tomography techniques enable 3D visualization but can be time-consuming, so only select systems or regions are analyzed in this manner. Here, it is presented that through high-efficiency multidimensional STEM acquisition and reconstruction, complex point cloud-like microstructural features can quickly and effectively be reconstructed in 3D. The proposed set of techniques is demonstrated, analyzed, and verified for a high-chromium steel with heterogeneously situated features induced using high-energy neutron bombardment.

This paper provides an overview of advanced scanning transmission electron microscopy (STEM) techniques used for characterization of irradiated BCC Fe-based alloys. Advanced STEM methods provide the high-resolution imaging and chemical analysis necessary to understand the irradiation response of BCC Fe-based alloys. The use of STEM with energy dispersive x-ray spectroscopy (EDX) for measurement of radiation-induced segregation (RIS) is described, with an illustrated example of RIS in proton- and self-ion irradiated T91. Aberration-corrected STEM-EDX for nanocluster/nanoparticle imaging and chemical analysis is also discussed, and examples are provided from ion-irradiated oxide dispersion strengthened (ODS) alloys. Finally, STEM techniques for void, cavity, and dislocation loop imaging are described, with examples from various BCC Fe-based alloys.

When will a small or low-contrast feature, such as an embedded second-phase particle, be visible in a scanning transmission electron microscopy (STEM) X-ray map? This work illustrates a computationally inexpensive method to simulate X-ray maps and spectrum images (SIs), based upon the equations of X-ray generation and detection. To particularize the general procedure, an example of nanostructured ferritic alloy (NFA) containing nm-sized Y2Ti2O7 embedded precipitates in ferritic stainless steel matrix is chosen. The proposed model produces physically appearing simulated SI data sets, which can either be reduced to X-ray dot maps or analyzed via multivariate statistical analysis. Comparison to NFA X-ray maps acquired using three different STEM instruments match the generated simulations quite well, despite the large number of simplifying assumptions used. A figure of merit of electron dose multiplied by X-ray collection solid angle is proposed to compare feature detectability from one data set (simulated or experimental) to another. The proposed method can scope experiments that are feasible under specific analysis conditions on a given microscope. Future applications, such as spallation proton–neutron irradiations, core-shell nanoparticles, or dopants in polycrystalline photovoltaic solar cells, are proposed.

Helium accumulation negatively impacts structural materials used in neutron-irradiated environments, such as fission and fusion reactors. Next-generation fission and fusion reactors will require structural materials, such as steels, that are resistant to large neutron doses yet see service temperatures in the range most affected by helium embrittlement. Previous work has indicated the difficulty of experimentally differentiating nanometer-sized cavities such as helium bubbles from the Ti–Y–O rich nanoclusters (NCs) in radiation-tolerant nanostructured ferritic alloys (NFAs). Because the NCs are expected to sequester helium away from grain boundaries and reduce embrittlement, experimental methods to study simultaneously the NC and bubble populations are needed. In this study, aberration-corrected scanning transmission electron microscopy (STEM) results combining high-collection-efficiency X-ray spectrum images (SIs), multivariate statistical analysis (MVSA), and Fresnel-contrast bright-field STEM imaging, have been used for such a purpose. Fresnel-contrast imaging, with careful attention to TEM-STEM reciprocity, differentiates bubbles from NCs. MVSA of X-ray SIs unambiguously identifies NCs. Therefore, combined Fresnel-contrast STEM and X-ray SI is an effective STEM-based method to characterize helium-bearing NFAs.

Thin films of nanocrystalline ceria on a Si substrate have been irradiated with 3 MeV Au+ ions to fluences of up to 1x1016 ions cm-2, at temperatures ranging between 160 to 400 K. During the irradiation, a band of contrast is observed to form at the thin film/substrate interface. Analysis by scanning transmission electron microscopy in conjunction with energy dispersive and electron energy loss spectroscopy techniques revealed that this band of contrast was a cerium silicate amorphous phase, with an approximate Ce:Si:O ratio of 1:1:3.

In this article, we use simulated and experimental data to explore how three operator-controllable parameters—(1) signal level, (2) detector resolution, and (3) number of factors chosen for analysis—affect quantitative analyses of scanning transmission electron microscopy–energy dispersive X-ray spectroscopy spectrum images processed by principal component analysis (PCA). We find that improvements in both signal level and detector resolution improve the precision of quantitative analyses, but that signal level is the most important. We also find that if the rank of the PCA solution is not chosen properly, it may be possible to improperly fit the underlying data and degrade the accuracy of results. Additionally, precision is degraded in the case when too many factors are included in the model.

Erbium dihydride Er(H,D,T)2 is a fluorite structure rare-earth dihydride useful for the storage of hydrogen isotopes in the solid state. However, thermodynamic predictions indicate that erbium oxide formation will proceed readily during processing, which may detrimentally contaminate Er(H,D,T)2 films. In this work, transmission electron microscopy (TEM) techniques including energy-dispersive x-ray spectroscopy, energy-filtered TEM, selected area electron diffraction, and high-resolution TEM are used to examine the manifestation of oxygen contamination in ErD2 thin films. An oxide layer ∼30–130 nm thick was found on top of the underlying ErD2 film, and showed a cube-on-cube epitaxial orientation to the underlying ErD2. Electron diffraction confirmed the oxide layer to be Er2O3. While the majority of the film was observed to have the expected fluorite structure for ErD2, secondary diffraction spots suggested the possibility of either nanoscale oxide inclusions or hydrogen ordering. In situ heating experiments combined with electron diffraction ruled out the possibility of hydrogen ordering, so epitaxial oxide nanoinclusions within the ErD2 matrix are hypothesized. TEM techniques were applied to examine this oxide nanoinclusion hypothesis.