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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.

Statistically significant crystal structure and composition identification of nanocrystalline features such as nanoparticles/nanoprecipitates in materials chemistry and alloy designing using electron microscopy remains a grand challenge. In this paper, we reveal that differing crystallographic phases of nanoprecipitates in alloys can be mapped with unprecedented statistics using transmission Kikuchi diffraction (TKD), on typical carbon-based electron-transparent samples. Using a case of multiphase, multicomponent nanoprecipitates extracted from an improved version of 9% chromium Eurofer-97 reduced-activation ferritic-martensitic steel we show that TKD successfully identified more than thousand M23C6, MX, M7C3, and M2X (M=Fe, Cr, W, V, Ta; X = C, N) nanoprecipitates in a single scan, something that is currently unachievable using a transmission electron microscope (TEM) without incorporating a precision electron diffraction (PED) system. Precipitates as small as ∼20–25 nm were successfully phase identified by TKD. We verified the TKD phase identification using high-resolution transmission electron microscopy (HRTEM) and convergent beam electron diffraction (CBED) pattern analysis of a few precipitates that were identified by TKD on same sample. TKD study was combined with state-of-art analytical scanning transmission electron microscopy (STEM)-energy dispersive X-ray (EDX) spectroscopy and multivariate statistical analysis (MVSA) which provided the complete crystal structure and distinct chemistries of the precipitates in the steel in a high throughput automated way. This technique should be applicable to characterizing any multiphase crystalline nanoparticles or nanomaterials. The results highlight that combining phase identification by TKD with analytical STEM and modern data analytics may open new pathways in big data material characterization at nanoscale that may be highly beneficial for characterizing existing materials and in designing new materials.

Surface morphologies of tungsten surfaces, both polycrystalline and single-crystal [1 1 0], were investigated using SEM and FIB/SEM techniques after implantations at elevated surfaces temperatures (1200–1300 K) using well-characterized, mono-energetic He ion beams with a wide range of ion energies (218 eV–250 keV). Nanofuzz was observed on polycrystalline tungsten (PCW) following implantation of 100-keV He ions at a flux threshold of 0.9 × 1016 cm−2 s−1, but not following 200-keV implantations with similar fluxes. No nanofuzz formation was observed on single-crystal [1 1 0] tungsten (SCW), despite fluxes exceeding those demonstrated previously to produce nanofuzz on polycrystalline tungsten. Pre-damaging the single-crystal tungsten with implanted C impurity interstitials did not significantly affect the surface morphologies resulting from the high-flux He ion implantations. The main factor leading to the different observed surface structures for the pristine and C-implanted single-crystal W samples appeared to be the peak He ion flux characterizing the different exposures. It was speculated that nanofuzz formation was not observed for any SCW target exposures because of increased incubation fluences required for such targets.

The application of spectrum imaging multivariate statistical analysis methods, specifically principal component analysis (PCA), to atom probe tomography (APT) data has been investigated. The mathematical method of analysis is described and the results for two example datasets are analyzed and presented. The first dataset is from the analysis of a PM 2000 Fe-Cr-Al-Ti steel containing two different ultrafine precipitate populations. PCA properly describes the matrix and precipitate phases in a simple and intuitive manner. A second APT example is from the analysis of an irradiated reactor pressure vessel steel. Fine, nm-scale Cu-enriched precipitates having a core-shell structure were identified and qualitatively described by PCA. Advantages, disadvantages, and future prospects for implementing these data analysis methodologies for APT datasets, particularly with regard to quantitative analysis, are also discussed.

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.

Spectrum imaging (SI) methods are displacing traditional spot analyses as the predominant paradigm for spectroscopic analysis with electron beam instrumentation. The multivariate nature of SI provides clear advantages for qualitative analysis of multiphase specimens relative to traditional gray-scale images acquired with non-spectroscopic signals, where different phases with similar average atomic number may exhibit the same intensity. However, with the improvement in qualitative analysis with the SI paradigm has come a decline in the quantitative analysis of the phases thus identified, since the spectra from individual pixels typically have insufficient counting statistics for proper quantification. The present paper outlines a methodology for quantitative analysis within the spectral imaging paradigm, which is illustrated through X-ray energy-dispersive spectroscopy (EDS) of a multiphase (Pb,La)(Zr,Ti)O 3 ceramic in scanning transmission electron microscopy (STEM). Statistical analysis of STEM-EDS SI is shown to identify the number of distinct phases in the analyzed specimen and to provide better segmentation than the STEM high-angle annular dark-field (HAADF) signal. Representative spectra for the identified phases are extracted from the segmented images with and without exclusion of pixels that exhibit spectral contributions from multiple phases, and subsequently quantified using Cliff–Lorimer sensitivity factors. The phase compositions extracted with the method while excluding pixels from multiple phases are found to be in good agreement with those extracted from user-selected regions of interest, while providing improved confidence intervals. Without exclusion of multiphase pixels, the extracted composition is found to be in poor statistical agreement with the other results because of systematic errors arising from the cross-phase spectral contamination. The proposed method allows quantification to be performed in the presence of discontinuous phase distributions and overlapping phases, challenges that are typical of many nanoscale analyses performed by STEM-EDS.

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.

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.

Multivariate statistical analysis (MVSA) methods, such as used in analytical electron microscopy and surface science, were applied to atom probe tomography (APT) data. It was found that MVSA methods allow fast and intuitive interpretation of APT data, where both spatial and spectral variance are described simultaneously in an unbiased manner. In this work, a FeCr-Al-Ti alloy (PM2000) subjected to different heat treatments was examined via APT. The different scale three-phase microstructures resulting from these heat treatments provided an interesting test of the capabilities and limits of the present MVSA methodology. Principal component analysis (PCA)-based MVSA identified the precipitate populations in all three samples, including ultrafine precipitates with the use of small (0.5 nm) voxel size, and high resolution (0.02 u) mass binning.

2 páginas, 3 figuras.-- Trabajo presentado al "Physical Sciences Symposia" celebrado en Oregon (USA) del 1 al 5 de Agosto de 2010., This research was supported by the Shared Research Equipment (SHaRE) User Program, which is sponsored at Oak Ridge National Laboratory by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy and by the Laboratory Directed Research and Development Weinberg Fellowship Program at Oak Ridge National Laboratory.

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

A small error in the mathematics described in the previous paper (C.M. Parish and M.K. Miller: Multivariate statistical analysis of atom probe tomography data, Ultramicroscopy 110(11) (2010) 1362–1373) has been found. Although the results and conclusions are completely unchanged, it is important to correct the error.

An in situ acoustic emission (AE) and x-ray diffraction cell for use in the study of battery electrode materials has been designed and tested. This cell uses commercially available coin cell hardware retrofitted with a metalized polyethylene terephthalate (PET) disk, which acts as both an x-ray window and a current collector. In this manner, the use of beryllium and its associated cost and hazards is avoided. An AE sensor may be affixed to the cell face opposite the PET window in order to monitor degradation effects, such as particle fracture, during cell cycling. Silicon particles, which were previously studied by the AE technique, were tested in this cell as a model material. The performance of these cells compared well with unmodified coin cells, while providing information about structural changes in the active material as the cell is repeatedly charged and discharged.