We propose a new methodology for ranking the reflectors used in traditional Hough-based indexing of electron backscatter diffraction (EBSD) patterns. Instead of kinematic X-ray or electron structure factors (Fhkl) currently utilized, we propose the integrated Kikuchi band intensity parameter (βhkl) based on integrated dynamical electron backscatter intensities. The proposed parameter is compared with the traditional kinematical intensity, $I_{hkl}^{{\rm kin}} $, as well as the average Hough transform peak intensity, $I_{hkl}^{{\rm HSP}} $ and used to index EBSD patterns for a number of different material systems of varying unit cell complexities including nickel, silicon, rutile, and forsterite. For elemental structures, βhkl closely follows the kinematical ranking. However, significant ranking differences arise for more complex unit cells, with the βhkl parameter showing a better correlation with the integrated Hough intensities. Finally, Hough-based indexing of a simulated forsterite data set showed an appreciable improvement in the median confidence index (0.15 to 0.35) when βhkl is used instead of $I_{hkl}^{{\rm kin}} $ for ranking the reflectors.

Problems involving image segmentation, atomic cluster identification, segmentation of microstructure constituents in images and austenite reconstruction have seen various approaches attempt to solve them with mixed results. No single computational technique has been able to effectively tackle these problems due to the vast differences between them. We propose the application of graph cutting as a versatile technique that can provide solutions to numerous materials data analysis problems. This can be attributed to its configuration flexibility coupled with the ability to handle noisy experimental data. Implementation of a Bayesian statistical approach allows for the prior information, based on experimental results and already ingrained within nodes, to drive the expected solutions. This way, nodes within the graph can be grouped together with similar, neighboring nodes that are then assigned to a specific system with respect to calculated likelihoods. Associating probabilities with potential solutions and states of the system allows for quantitative, stochastic analysis. The promising, robust results for each problem indicate the potential usefulness of the technique so long as a network of nodes can be effectively established within the model system.

We summarize the findings from an interlaboratory study conducted between ten international research groups and investigate the use of the commonly used maximum separation distance and local concentration thresholding methods for solute clustering quantification. The study objectives are: to bring clarity to the range of applicability of the methods; identify existing and/or needed modifications; and interpretation of past published data. Participants collected experimental data from a proton-irradiated 304 stainless steel and analyzed Cu-rich and Ni–Si rich clusters. The datasets were also analyzed by one researcher to clarify variability originating from different operators. The Cu distribution fulfills the ideal requirements of the maximum separation method (MSM), namely a dilute matrix Cu concentration and concentrated Cu clusters. This enabled a relatively tight distribution of the cluster number density among the participants. By contrast, the group analysis of the Ni–Si rich clusters by the MSM was complicated by a high Ni matrix concentration and by the presence of Si-decorated dislocations, leading to larger variability among researchers. While local concentration filtering could, in principle, tighten the results, the cluster identification step inevitably maintained a high scatter. Recommendations regarding reporting, selection of analysis method, and expected variability when interpreting published data are discussed.

Here, a well crystalline 3D flower-like structured MoS2 (~420 nm) has been successfully synthesized on a large scale by a simple hydrothermal technique. The evolution of morphology in the formation process has also been investigated. The crystallinity, purity, and morphology of the sample are characterized by powder X-ray diffraction, Fourier-transform infrared spectroscopy, fieldemission scanning electron microscopy (FESEM), and transmission electron microscopy (TEM) techniques. The FESEM and TEM images reveal that the sample exhibits a uniform 3D flower-like microsphere shape with folded nanosheets, which are stretched out along the edge of the microsphere. The electrochemical performance of the sample has been investigated by cyclic voltammogram, galvanostatic charge–discharge, and electrochemical impedance spectroscopy studies. The results of the electrochemical analysis suggest that the material delivers a maximum specific capacitance (Csp) of 350 F/g at a discharge current density of 0.25 A/g with energy density 17.5 Wh/kg. It also exhibits good capability and excellent cyclic stability (94% capacity retention after 1,000 cycles in 1 A/g) owing to the coupling effect of electrical conductivity with the interesting morphology and larger active surface area. Hence, the sample may be used as a promising electrode material for high-performance energy storage devices.

A scanning ion beam instrument equipped with a gas field ionization source (GFIS) has been commercialized, but only helium and neon are currently available as GFISs. In this study, the characteristics of neon, argon, and krypton ion emissions from a single-atom tip are compared, specifically for faster fabrication by milling of a silicon sample. Although the boiling point of argon is about 87 K, our experiments on characterizing argon ion emission can be carried out at temperatures of about 50 K at an argon gas pressure lower than 0.1 Pa. Argon exhibits ion current characteristics, as a function of tip voltage, between those of neon and krypton. The value obtained by multiplying the ion emission current by the sputtering yield is suitable for a figure of merit (FOM) for faster fabrication. The FOM for argon is the highest among the three ion species. This value must be extensively evaluated from the viewpoint of practical nano-fabrication application. The instabilities of neon, argon, and krypton ion currents (3σ) become as low as 8% in 1 h, which is sufficient for fabrication applications. We conclude that an argon or krypton GFIS ion beam instrument will be a useful tool for nano-fabrication.

Phase retrieval is necessary for propagation-based phase-contrast imaging (PB-PCI). Arhatari established a model for predicting the impact of the sample-to-detector distance and the system noise on the phase retrieval performance. We have extended Arhatari's model to account for the parameters of excessive source size, finite detector resolution, and geometrical magnification for more practical cases. However, there exist interaction effects among these parameters resulting in difficulty of predicting the phase retrieval performance. In this study, we found that optimizing the trade-off among these parameters for phase retrieval is consistent with the improvement of edge enhancement to noise ratio (EE/N) in the “forward problem” of the PB-PCI. Hence, we engaged in establishing a relationship between EE/N and phase retrieval performance in terms of the “forward problem” and “inverse problem” of the PB-PCI, respectively. Our results showed that, at fixed detector resolution, phase retrieval from the phase-contrast projections at the same EE/N level resulted in the consistent phase retrieval performance. Therefore, the performance of phase retrieval can be predicted based on the EE/N level and be quantitatively optimized by increasing EE/N.

Correlated light and electron microscopy (CLEM) has become a popular technique for combining the protein-specific labeling of fluorescence with electron microscopy, both at room and cryogenic temperatures. Fluorescence applications at cryo-temperatures have typically been limited to localization of tagged protein oligomers due to known issues of extended triplet state duration, spectral shifts, and reduced photon capture through cryo-CLEM objectives. Here, we consider fluorophore characteristics and behaviors that could enable more extended applications. We describe how dialkylcarbocanine DiD, and its autoquenching by resonant energy transfer (RET), can be used to distinguish the fusion state of a lipid bilayer at cryo-temperatures. By adapting an established fusion assay to work under cryo-CLEM conditions, we identified areas of fusion between influenza virus-like particles and fluorescently labeled lipid vesicles on a cryo-EM grid. This result demonstrates that cryo-CLEM can be used to localize functions in addition to tagged proteins, and that fluorescence autoquenching by RET can be incorporated successfully into cryo-CLEM approaches. In the case of membrane fusion applications, this method provides both an orthogonal confirmation of functional state independent of the morphological description from cryo-EM and a way to bridge room-temperature kinetic assays and the cryo-EM images.

There is increased interest in the use of cellulose nanomaterials for the mechanical reinforcement of composites due to their high stiffness and strength. However, challenges remain in accurately determining their distribution within composite microstructures. We report the use of a range of techniques used to image aggregates of cellulose nanocrystals (CNCs) greater than 10 µm2 within a model thermoplastic polymer. While Raman imaging accurately determines CNC aggregate size, it requires extended periods of analysis and the limited observable area results in poor reproducibility. In contrast, staining the CNCs with a fluorophore enables rapid acquisition with high reproducibility, but overestimates the aggregate size as CNC content increases. Multi-channel spectral confocal laser scanning microscopy is presented as an alternative technique that combines the accuracy of Raman imaging with the speed and reproducibility of conventional confocal laser scanning microscopy, enabling the rapid determination of CNC aggregate distribution within composites.

It is shown that a xenon plasma focused ion beam (FIB) microscope is an excellent tool for high-quality preparation of functional oxide thin films for atomic resolution electron microscopy. Samples may be prepared rapidly, at least as fast as those prepared using conventional gallium FIB. Moreover, the surface quality after 2 kV final polishing with the Xe beam is exceptional with only about 3 nm of amorphized surface present. The sample quality was of a suitably high quality to allow atomic resolution high-angle annular dark field imaging and integrated differential phase contrast without any further preparation, and the resulting images were good enough for quantitative evaluation of atomic positions to reveal the oxygen octahedral tilt pattern. This suggests that such xenon plasma FIB instruments may find widespread application in transmission electron microscope and scanning transmission electron microscope specimen preparation.

This paper aims at understanding the texture evolution in extruded oxide dispersion strengthened 18Cr ferritic steel during high-temperature uniaxial compression testing at 1,423 K at a strain rate of 0.01/s based on extensive electron back scatter diffraction characterization. The α-fiber texture is observed along the extrusion direction (ED) in the initial microstructure. The flow curves generated during uniaxial compression test are used to determine the associated hardening parameters. In addition, the degree of texture evolution after deformation along the ED and the transverse direction (TD) with respect to the initial condition has been predicted using VPSC-5 constitutive model. The prediction shows that the deformation along the ED produces a dominant γ-fiber texture in contrast to the TD. This is in agreement with the experimental results where γ-fiber texture is observed, due to enhanced dynamic recrystallization at high-temperature deformation.

This paper describes an alternative way to assign elemental identity to atoms collected by atom probe tomography (APT). This method is based on Bayesian assignation of label through the expectation–maximization method (well known in data analysis). Assuming the correct shape of mass over charge peaks in mass spectra, the probability of each atom to be labeled as a given element is determined, and is used to enhance data visualization and composition mapping in APT analyses. The method is particularly efficient for small count experiments with a low signal to noise ratio, and can be used on small subsets of analyzed volumes, and is complementary to single-ion decomposition methods. Based on the selected model and experimental examples, it is shown that the method enhances our ability to observe and extract information from the raw dataset. The experimental case of the superimposition of the Si peak and N peak in a steel is presented.

Needles of Juniperus rigida are used in Chinese traditional medicine for the treatment of brucellosis, dropsy, skin disease, and rheumatoid arthritis. This is the first study that reports anatomical structures of the J. rigida needles collected at different altitudes. The most common anatomical, phytochemical, and histochemical techniques and methods are used. The results show that anatomical structures and chemical composition change significantly at different altitudes. The main anatomical characters are significant xeromorphic structures (thick epidermis, hypodermis, and cuticle), a stomatal band, a developed vascular bundle, and a marginal resin duct. The xeromorphic structures become more pronounced with increasing altitude. The phytochemical and histochemical results demonstrate that the content of the main chemical compounds (phenols and terpenoids) basically increases at a higher elevation. Histochemical analysis localizes the phenols in epidermal cells, sponge tissue, endothelial layer cells, and stomatal bands, and the terpenoids in palisade tissue, sponge tissue, and the edge of the resin duct. This work reveals the relation between anatomy and chemistry in J. rigida needles, contributes to the quality control of its ethno-medicine, and provides the evidence to develop the commercial cultivation.

Herein, we report an efficient method to produce silver (Ag) nanoparticle-decorated silicon (Si) nanowire (NW) arrays on a pyramidal Si (P-Si) substrate by using a pure chemical method and rapid thermal annealing in different atmospheres. A metal-assisted chemical etching technique was used to produce vertical Si NW arrays on pyramidal Si. The etching was observed to be heavily dependent on the substrate type. On planar Si (100), the etching was observed to occur in a uniform manner. However, the etching rate was observed to increase from the top to the base of the Si pyramid. The Si NWs produced from P-Si have zig-zag sidewalls as observed from high-resolution transmission electron microscopy images. However, for the same oxidant concentration, Si NWs produced from planar Si (100) consist of straight and amorphous sidewalls. Local variation of oxidant concentration is responsible for the formation of different sidewalls. The substrates are both surface-enhanced Raman scattering (SERS) active and hydrophobic. The hydrophobicity is due to the dual scale of roughness contributed to by both pyramidal and NW structures. Finite-difference time-domain simulation shows that the gap between two Ag spheres and also the gap between Si NWs and Ag spheres contributed to SERS enhancement.

Computational analysis on altered micro-nano-textural attributes of the oral mucosa may provide precise diagnostic information about oral potentially malignant disorders (OPMDs) instead of an existing handful of qualitative reports. This study evaluated micro-nano-textural features of oral epithelium from scanning electron microscopic (SEM) images and the sub-epithelial connective tissue from light microscopic (LM) and atomic force microscopic (AFM) images for normal and OPMD (namely oral sub-mucous fibrosis, i.e., OSF). Objective textural descriptors, namely discrete wavelet transform, gray-level co-occurrence matrix (GLCM), and local binary pattern (LBP), were extracted and fed to standard classifiers. Best classification accuracy of 87.28 and 93.21%; sensitivity of 93 and 96%; specificity of 80 and 91% were achieved, respectively, for SEM and AFM. In the study groups, SEM analysis showed a significant (p < 0.01) variation for all the considered textural descriptors, while for AFM, a remarkable alteration (p < 0.01) was only found in GLCM and LBP. Interestingly, sub-epithelial collagen nanoscale and microscale textural information from AFM and LM images, respectively, were complementary, namely microlevel contrast was more in normal (0.251) than OSF (0.193), while nanolevel contrast was more in OSF (0.283) than normal (0.204). This work, thus, illustrated differential micro-nano-textural attributes for oral epithelium and sub-epithelium to distinguish OPMD precisely and may be contributory in early cancer diagnostics.