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In this article, we review focused ion beam serial sectioning microscopy paired with analytical techniques, such as electron backscatter diffraction or x-ray energy-dispersive spectrometry, to study materials chemistry and structure in three dimensions. These three-dimensional microanalytical approaches have been greatly extended due to advances in software for both microscope control and data interpretation. Samples imaged with these techniques reveal structural features of materials that can be quantitatively characterized with rich chemical and crystallographic detail. We review these technological advances and the application areas that are benefitting. We also consider the challenges that remain for data collection, data processing, and visualization, which collectively limit the scale of these investigations. Further, we discuss recent innovations in quantitative analyses and numerical modeling that are being applied to microstructures illuminated by these techniques.
A new aberration-corrected scanning transmission electron microscope equipped with an array of Si-drift energy-dispersive X-ray spectrometers has been utilized to acquire spectral image data at atomic resolution. The resulting noisy data were subjected to multivariate statistical analysis to noise filter, remove an unwanted and partially overlapping non-sample-specific X-ray signal, and extract the relevant correlated X-ray signals (e.g., channels with L and K lines). As an example, the Y2Ti2O7 pyrochlore-structured oxide (assumed here to be ideal) was interrogated at the  projection. In addition to pure columns of Y and Ti, at this projection, there are also mixed 50-50 at. % Y-Ti columns. An attempt at atomic-resolution quantification is presented. The method proposed here is to subtract the non-column-specific signal from the elemental components and then quantify the data based upon an internally derived k-factor. However, a theoretical basis to predict this non-column-specific signal is needed to make this generally applicable.
Multivariate statistical analysis (MSA) is applied to the extraction of chemically relevant signals acquired with a micro-X-ray fluorescence (μ-XRF) mapping (full-spectral imaging) system. The separation of components into individual histograms enables separation of overlapping peaks, which is useful in qualitatively determining the presence of chemical species that have overlapping emission lines, and holds potential for quantitative analysis of constituent phases via these same histograms. The usefulness of MSA for μ-XRF analysis is demonstrated by application to a geological rock core obtained from a subsurface compressed air energy storage (CAES) site. Coupling of the μ-XRF results to those of quantitative powder X-ray diffraction analysis enables improved detection of trace phases present in the geological specimen. The MSA indicates that the spatial distribution of pyrite, a potentially reactive phase by oxidation, has low concentration and thus minimal impact on CAES operations.
Maximizing power and energy densities of ultracapacitors requires configuring redox-active materials in specific architectures that: 1) maximize electrolyte-electrode contact area, 2) minimize transport distances for both electrons and charge compensating species, and 3) minimize transport barriers. We have developed a simple solution-based method, using an organic template, that enables us to introduce hierarchical porosity in ruthenium oxide down to the nano-scale by controlling the oxidative crystal growth of RuO2. The high capacitances of the resulting nanostructured electrodes were found to be comparable to hydrous ruthenium oxide formed under dramatically different conditions. Materials characterization reveals that the organic template directs structure formation and promotes hydroxyl retention.
Multivariate statistical analysis methods have been applied to
scanning transmission electron microscopy (STEM) energy-dispersive X-ray
spectral images. The particular application of the multivariate curve
resolution (MCR) technique provides a high spectral contrast view of the
raw spectral image. The power of this approach is demonstrated with a
microelectronics failure analysis. Specifically, an unexpected component
describing a chemical contaminant was found, as well as a component
consistent with a foil thickness change associated with the focused ion
beam specimen preparation process. The MCR solution is compared with a
conventional analysis of the same spectral image data set.
A comprehensive three-dimensional (3D) microanalysis procedure using a
combined scanning electron microscope (SEM)/focused ion beam (FIB)
system equipped with an energy-dispersive X-ray spectrometer (EDS) has
been developed. The FIB system was used first to prepare a site-specific
region for X-ray microanalysis followed by the acquisition of an
electron-beam generated X-ray spectral image. A small section of material
was then removed by the FIB, followed by the acquisition of another X-ray
spectral image. This serial sectioning procedure was repeated 10–12
times to sample a volume of material. The series of two-spatial-dimension
spectral images were then concatenated into a single data set consisting
of a series of volume elements or voxels each with an entire X-ray
spectrum. This four-dimensional (three real space and one spectral
dimension) spectral image was then comprehensively analyzed with
Sandia's automated X-ray spectral image analysis software. This
technique was applied to a simple Cu-Ag eutectic and a more complicated
localized corrosion study where the powerful site-specific comprehensive
analysis capability of tomographic spectral imaging (TSI) combined with
multivariate statistical analysis is demonstrated.
Spectral imaging in the scanning electron microscope (SEM)
equipped with an energy-dispersive X-ray (EDX) analyzer has
the potential to be a powerful tool for chemical phase
identification, but the large data sets have, in the past, proved
too large to efficiently analyze. In the present work, we describe
the application of a new automated, unbiased, multivariate
statistical analysis technique to very large X-ray spectral
image data sets. The method, based in part on principal components
analysis, returns physically accurate (all positive) component
spectra and images in a few minutes on a standard personal
computer. The efficacy of the technique for microanalysis is
illustrated by the analysis of complex multi-phase materials,
particulates, a diffusion couple, and a single-pixel-detection