Microscopy and Microanalysis最新文献

Intensities in high-resolution phase-contrast images from electron microscopes build up discretely in time by detecting single electrons. A wave description of pulse-like coherent-inelastic interaction of an electron with matter implies a time-dependent coexistence of coherent partial waves. Their superposition forms a wave package by phase decoherence of 0.5 - 1 radian with Heisenbergs energy uncertainty ΔEH = ħ/2 Δt-1 matching the energy loss ΔE of a coherent-inelastic interaction and sets the interaction time Δt. In these circumstances, the product of Planck's constant and the speed of light hc is given by the product of the expression for temporal coherence λ2/Δλ and the energy loss ΔE. Experimentally, the self-coherence length was measured by detecting the energy-dependent localization of scattered, plane matter waves in surface proximity exploiting the Goos-Hänchen shift. Chromatic-aberration Cc-corrected electron microscopy on boron nitride (BN) proves that the coherent crystal illumination and phase contrast are lost if the self-coherence length shrinks below the size of the crystal unit cell at ΔE > 200 eV. In perspective, the interaction time of any matter wave compares with the lifetime of a virtual particle of any elemental interaction, suggesting the present concept of coherent-inelastic interactions of matter waves might be generalizable.

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s-1 (87,000 frames s-1), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera's data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

Spectrum imaging with energy-dispersive X-ray spectroscopy (EDS) has become ubiquitous in material characterization using electron microscopy. Multivariate statistical methods, commonly principal component analysis (PCA), are often used to aid analysis of the resulting multidimensional datasets; PCA can provide denoising prior to further analysis or grouping of pixels into distinct phases with similar signals. However, it is well known that PCA can introduce artifacts at low signal-to-noise ratios. Unfortunately, when evaluating the benefits and risks with PCA, it is often compared only against raw data, where it tends to shine; alternative data analysis methods providing a fair point of comparison are often lacking. Here, we directly compare PCA with a strategy based on (the conceptually and computationally simpler) weighted least squares (WLS). We show that for four representative cases, model fitting of the sum spectrum followed by WLS (mfWLS) consistently outperforms PCA in terms of finding and accurately describing compositional gradients and inclusions and as a preprocessing step to clustering. Additionally, we demonstrate that some common artifacts and biases displayed by PCA are avoided with the mfWLS approach. In summary, mfWLS can provide a superior option to PCA for analysis of EDS spectrum images as the signal is simply and accurately modeled.

Amorphous thin films grown by magnetron co-sputtering exhibit changes in atomic structure with varying growth and annealing temperatures. Structural variations influence the bulk properties of the films. Scanning nanodiffraction performed in a transmission electron microscope (TEM) is applied to amorphous Tb17Co83 (a-Tb-Co) films deposited over a range of temperatures to measure relative changes in medium-range ordering (MRO). These measurements reveal an increase in MRO with higher growth temperatures and a decrease in MRO with higher annealing temperatures. The trend in MRO indicates a relationship between the growth conditions and local atomic ordering. By tilting select films, the TEM measures variations in the local atomic structure as a function of orientation within the films. The findings support claims that preferential ordering along the growth direction results from temperature-mediated adatom configurations during deposition, and that oriented MRO correlates with increased structural anisotropy, explaining the strong growth-induced perpendicular magnetic anisotropy found in rare earth-transition metal films. Beyond magnetic films, we propose the tilted FEM workflow as a method of extracting anisotropic structural information in a variety of amorphous materials with directionally dependent bulk properties, such as films with inherent bonding asymmetry grown by physical vapor deposition.

The production of plutonium-238 through irradiation of neptunium-237 (237Np) target materials for the use in radioisotope thermoelectric generators is paramount for continued deep space exploration. This work employs scanning electron microscopy to analyze 237Np materials coupled with a well-developed image analysis framework (Morphological Analysis for Material Attribution, or MAMA) to determine the degree of micron-scale homogeneity in the materials. This work demonstrated how the quantification of particle characteristics can validate production materials and affirm the qualitative similarities observed in micrographs. The 237Np oxide particle analysis determined that the materials from five production runs were quantitatively homogenous (significant at α = 0.05) in particle area, circularity, equivalent circular diameter, and ellipse aspect ratio, with two of the sampling dates having statistically significant different means for one of the four characteristics. These metrics not only confirm general homogeneity of the material but also expand the application of MAMA workflows to 237Np materials, demonstrating the utility of MAMA analysis for a wider breadth of nuclear materials than previously reported. In the open literature, this study is the first time that these microanalytical techniques were applied to 237Np materials to this degree.

In this work, samples of chromia (Cr2O3) scale have been prepared for atom probe tomography and field evaporated with deep ultraviolet laser light (258 nm wavelength). The investigated range of laser energies spans more than three orders of magnitude between 0.03 and 90 pJ. Furthermore, the effects of detection rate and temperature were investigated. Simultaneous voltage and laser pulses were employed on additional needle specimens to reduce the standing voltage and minimize background noise during the measurement. Smooth evaporation with minimal mass spectrum peak tails was maintained over the whole range of measurement parameters. High laser energies result in significant underestimation of the oxygen content. Only laser energies below 1 pJ resulted in measured values near the expected oxygen content of 60 at%, the closest being about 58 at%.

A new method for dark field imaging is introduced, which uses scanned electron diffraction (or 4DSTEM-4-dimensional scanning transmission electron microscopy) datasets as its input. Instead of working on simple summation of intensity, it works on a sparse representation of the diffraction patterns in terms of a list of their diffraction peaks. This is tested on a thin perovskite film containing structural ordering resulting in additional superlattice spots that reveal details of domain structures, and is shown to give much better selectivity and contrast than conventional virtual dark field imaging. It is also shown to work well in polycrystalline aggregates of CuO nanoparticles. In view of the higher contrast and selectivity, and the complete exclusion of diffuse scattering from the image formation, it is expected to be of significant benefit for characterization of a wide variety of crystalline materials.

Stacking faults (SFs) are important structural defects that play an essential role in the deformation of engineering alloys. However, direct observation of SFs at the atomic scale can be challenging. Here, we use the analytical field ion microscopy, including density functional theory-informed contrast estimation, to image local elemental segregation at SFs in a creep-deformed solid-solution single-crystal alloy of Ni-2 at% W. The segregated atoms are imaged brightly, and time-of-flight spectrometry allows for their identification as W. We also provide the first quantitative analysis of trajectory aberration, with a deviation of approximately 0.4 nm, explaining why atom probe tomography could not resolve these segregations. Atomistic simulations of substitutional W atoms at an edge dislocation in face-centered cubic Ni using an analytic bond-order potential indicate that the experimentally observed segregation is due to the energetic preference of W for the center of the SF, contrasting with, for example, Re segregating to partial dislocations. Solute segregation to SF can hinder dislocation motion, increasing the strength of Ni-based superalloys. Yet, direct substitution of Re by W, envisaged to lower the superalloys' costs, requires extra consideration in alloy design since these two solutes do not have comparable interactions with structural defects during deformation.

The levels of nicotinamide adenine dinucleotide (NADH) dehydrogenase [ubiquinone] iron-sulfur protein 2 (NDUFS2, a subunit of NADH dehydrogenase) decrease in aged tissues, and these reductions may be partly associated with age-related conditions such as Parkinson's disease. Aging leads to many mitochondrial defects, such as biogenesis disruption, dysfunction, defects in the mitochondrial membrane potential, and production of reactive oxygen species, that may be highly related to NDUFS2 expression. The relationship between NDUFS2 and postovulatory oocyte aging in pigs remains unknown. In this study, we investigated changes in NDUFS2 expression during postovulatory aging (POA). Furthermore, NDUFS2 was knocked down via dsRNA microinjection at the MII stage to evaluate the effects on mitochondrial-related processes during POA. The mRNA expression of NDUFS2 decreased significantly after 48-h aging compared with that in fresh oocytes. NDUFS2 knockdown (KD) significantly impaired the maintenance of oocyte morphology and blastocyst development of embryos after POA. The levels of PGC1α (mitochondrial biogenesis-related proteins) decreased significantly after NDUFS2 KD, while the level of GSNOR, a protein denitrosylase, was reduced by NDUFS2 KD after 48 h of aging. These data suggest that NDUFS2 is vital for maintaining the oocyte quality during POA in pigs.

Identifying clusters of solute atoms in a matrix of solvent atoms helps to understand precipitation phenomena in alloys, for example, during the age hardening of certain aluminum alloys. Atom probe tomography datasets can deliver such information, provided that appropriate cluster identification routines are available. We investigate algorithms based on the local composition of the neighborhood of solute atoms and compare them with traditional approaches based on the local solute number density, such as the maximum separation distance method. For an ideal solid solution, the pair correlation functions of the kth nearest solute atom in the coordination number representation are derived, and the percolation threshold and the size distribution of clusters are studied. A criterion for selecting optimal control parameters based on maximizing the phase separation by the degree of clustering is proposed for a two-phase system. A map of phase compositions accessible for cluster analysis is constructed. The coordination number approach reduces the influence of density variations commonly observed in atom probe tomography data. Finally, a practical cluster analysis technique applied to the early stages of aluminum alloy aging is described.