Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications.

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.

High-Efficiency Three-Dimensional Visualization of Complex Microstructures via Multidimensional STEM Acquisition and Reconstruction.

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.

High throughput crystal structure and composition mapping of crystalline nanoprecipitates in alloys by transmission Kikuchi diffraction and analytical electron microscopy.

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.

Irradiation-induced ß to α SiC transformation at low temperature.

We observed that ß-SiC, neutron irradiated to 9 dpa (displacements per atom) at ≈1440 °C, began transforming to α-SiC, with radiation-induced Frank dislocation loops serving as the apparent nucleation sites. 1440 °C is a far lower temperature than usual ß â†’ α phase transformations in SiC. SiC is considered for applications in advanced nuclear systems, as well as for electronic or spintronic applications requiring ion irradiation processing. ß-SiC, preferred for nuclear applications, is metastable and undergoes a phase transformation at high temperatures (typically 2000 °C and above). Nuclear reactor concepts are not expected to reach the very high temperatures for thermal transformation. However, our results indicate incipient ß â†’ α phase transformation, in the form of small (~5-10 nm) pockets of α-SiC forming in the ß matrix. In service transformation could degrade structural stability and fuel integrity for SiC-based materials operated in this regime. However, engineering this transformation deliberately using ion irradiation could enable new electronic applications.

Morphologies of tungsten nanotendrils grown under helium exposure.

Nanotendril "fuzz" will grow under He bombardment under tokamak-relevant conditions on tungsten plasma-facing materials in a magnetic fusion energy device. We have grown tungsten nanotendrils at low (50 eV) and high (12 keV) He bombardment energy, in the range 900-1000 °C, and characterized them using electron microscopy. Low energy tendrils are finer (~22 nm diameter) than high-energy tendrils (~176 nm diameter), and low-energy tendrils have a smoother surface than high-energy tendrils. Cavities were omnipresent and typically ~5-10 nm in size. Oxygen was present at tendril surfaces, but tendrils were all BCC tungsten metal. Electron diffraction measured tendril growth axes and grain boundary angle/axis pairs; no preferential growth axes or angle/axis pairs were observed, and low-energy fuzz grain boundaries tended to be high angle; high energy tendril grain boundaries were not observed. We speculate that the strong tendency to high-angle grain boundaries in the low-energy tendrils implies that as the tendrils twist or bend, strain must accumulate until nucleation of a grain boundary is favorable compared to further lattice rotation. The high-energy tendrils consisted of very large (>100 nm) grains compared to the tendril size, so the nature of the high energy irradiation must enable faster growth with less lattice rotation.

When will Low-Contrast Features be Visible in a STEM X-Ray Spectrum Image?

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.

Ferromagnetism and nonmetallic transport of thin-film α-FeSi(2): a stabilized metastable material.

A metastable phase α-FeSi_{2} was epitaxially stabilized on a silicon substrate using pulsed laser deposition. Nonmetallic and ferromagnetic behaviors are tailored on α-FeSi_{2} (111) thin films, while the bulk material of α-FeSi_{2} is metallic and nonmagnetic. The transport property of the films renders two different conducting states with a strong crossover at 50 K, which is accompanied by the onset of a ferromagnetic transition as well as a substantial magnetoresistance. These experimental results are discussed in terms of the unusual electronic structure of α-FeSi_{2} obtained within density functional calculations and Boltzmann transport calculations with and without strain. Our finding sheds light on achieving ferromagnetic semiconductors through both their structure and doping tailoring, and provides an example of a tailored material with rich functionalities for both basic research and practical applications.

Deviation from high-entropy configurations in the atomic distributions of a multi-principal-element alloy.

The alloy-design strategy of combining multiple elements in near-equimolar ratios has shown great potential for producing exceptional engineering materials, often known as 'high-entropy alloys'. Understanding the elemental distribution, and, thus, the evolution of the configurational entropy during solidification, is undertaken in the present study using the Al1.3CoCrCuFeNi model alloy. Here we show that, even when the material undergoes elemental segregation, precipitation, chemical ordering and spinodal decomposition, a significant amount of disorder remains, due to the distributions of multiple elements in the major phases. The results suggest that the high-entropy alloy-design strategy may be applied to a wide range of complex materials, and should not be limited to the goal of creating single-phase solid solutions.

Aberration-corrected X-ray spectrum imaging and Fresnel contrast to differentiate nanoclusters and cavities in helium-irradiated alloy 14YWT.

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.

Anisotropic epitaxial ZnO/CdO core/shell heterostructure nanorods.

Various surface structures and polarities of one-dimensional nanostructures offer additional control in synthesizing heterostructures suitable for optoelectronic and electronic applications. In this work, we report synthesis and characterization of ZnO-CdO nanorod-based heterostructures grown on a-plane sapphire. The heterojunction formed on the sidewall surface of the nanorod shows that wurtzite ZnO {1010} planes are interfaced with rocksalt CdO {100}. This is evidently different from the heterojunction formed on the nanorod top surface, where a ZnO (0001) top plane is interfaced with a CdO (111) plane. Such anisotropic heterostructures are determined by different surface structures of the nanorods and their polarities. Revelation of such anisotropic heterojunctions will provide a clue for understanding charge transport properties in electronic and optoelectronic nanodevices.

Novel cell design for combined in situ acoustic emission and x-ray diffraction study during electrochemical cycling of batteries.

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.

Correlated optical measurements and plasmon mapping of silver nanorods.

Plasmonics is a rapidly growing field, yet imaging of the plasmonic modes in complex nanoscale architectures is extremely challenging. Here we obtain spatial maps of the localized surface plasmon modes of high-aspect-ratio silver nanorods using electron energy loss spectroscopy (EELS) and correlate to optical data and classical electrodynamics calculations from the exact same particles. EELS mapping is thus demonstrated to be an invaluable technique for elucidating complex and overlapping plasmon modes.

Multivariate statistical analysis of atom probe tomography data.

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.

Key parameters affecting quantitative analysis of STEM-EDS spectrum images.

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.

Multivariate statistics applications in phase analysis of STEM-EDS spectrum images.

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.