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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.

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.

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.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.

Extended abstract of a paper presented at Microscopy and Microanalysis 2012 in Phoenix, Arizona, USA, July 29 – August 2, 2012.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Extended abstract of a paper presented at Microscopy and Microanalysis 2010 in Portland, Oregon, USA, August 1 – August 5, 2010.

Extended abstract of a paper presented at Microscopy and Microanalysis 2009 in Richmond, Virginia, USA, July 26 – July 30, 2009

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005