Early detection of altered epithelium can help in controlling the further progression by timely intervention. Alterations in cellular adhesion are one of the hallmarks of cancer progression, which can be detected at the intracellular level using high-resolution electron microscopy. This study aimed to evaluate the role of electron microscopy in the establishment of ultrastructural markers for early detection of altered epithelium using tissues from 4-Nitroquinoline-1-Oxide (4NQO) induced rat tongue carcinogenesis. Our previous study using light microscopy displayed no histopathological alterations in 4NQO treated tissues until 40 days of treatment, while dysplasia, papilloma and carcinoma were detected at 80/120, 160 and 200 days, respectively. However, electron microscopy detected alterations such as detachment of desmosomes from cell membranes and their clustering in the cytoplasm, increased tonofilaments, keratohyaline granules and thickened corneum in 40 days treated corresponding tissues. These alterations are apparent with hyperkeratosis/hyperplasia but remained undetected using light microscopy. Further, in dysplasia, papilloma and carcinoma, gradual and significant loss of desmosomes, leading to the significant widening of intercellular spaces, was observed using iTEM software. These parameters may serve as indicators for progression of oral cancer. Our results highlight the importance of electron microscopy in the early detection of subcellular changes in the altered epithelium.

We report on a new algorithm for the detection of crystallographic information in three-dimensional, as retained in atom probe tomography (APT), with improved robustness and signal detection performance. The algorithm is underpinned by one-dimensional distribution functions (DFs), as per existing algorithms, but eliminates an unnecessary parameter as compared to current methods.

By examining traditional DFs in an automated fashion in real space, rather than using Fourier transform approaches, we utilize an error metric based upon the expected value for a spatially random distribution for detecting crystallography. We show cases where the metric is able to successfully obtain orientation information, and show that it can function with high levels of additive and displacive background noise. We additionally compare this metric to Fourier transform methods, showing fewer artifacts when examining simulated datasets. An extension of the approach is used to aid the automatic detection of high-quality data regions within an entire dataset, albeit with a large increase in computational cost.

This extension is demonstrated on acquired aluminum and tungsten APT datasets, and shown to be able to discern regions of the data which have relatively improved spatial data quality. Finally, this program has been made available for use in other laboratories undertaking their own analyses.

A reliable quantitative analysis in electron tomography, which depends on the segmentation of the three-dimensional reconstruction, is challenging because of constraints during tilt-series acquisition (missing wedge) and reconstruction artifacts introduced by reconstruction algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) and Discrete Algebraic Reconstruction Technique (DART). We have carefully evaluated the fidelity of segmented reconstructions analyzing a disordered mesoporous carbon used as support in catalysis. Using experimental scanning transmission electron microscopy (STEM) tomography data as well as realistic phantoms, we have quantitatively analyzed the effect on the morphological description as well as on diffusion properties (based on a random-walk particle-tracking simulation) to understand the role of porosity in catalysis. The morphological description of the pore structure can be obtained reliably both using SIRT and DART reconstructions even in the presence of a limited missing wedge. However, the measured pore volume is sensitive to the threshold settings, which are difficult to define globally for SIRT reconstructions. This leads to noticeable variations of the diffusion coefficients in the case of SIRT reconstructions, whereas DART reconstructions resulted in more reliable data. In addition, the anisotropy of the determined diffusion properties was evaluated, which was significant in the presence of a limited missing wedge for SIRT and strongly reduced for DART.

In this research, in situ high-temperature electron backscattered diffraction (EBSD) mapping is applied to record and analyze the migration of the α/γ interfaces during cyclic austenite–ferrite phase transformations in a medium manganese steel. The experimental study is supplemented with related 3D phase field (PF) simulations to better understand the 2D EBSD observations in the context of the 3D transformation events taking place below the surface. The in situ EBSD observations and PF simulations show an overall transformation behavior qualitatively similar to that measured in dilatometry. The behavior and kinetics of individual austenite–ferrite interfaces during the transformation is found to have a wide scatter around the average interface behavior deduced on the basis of the dilatometric measurements. The trajectories of selected characteristic interfaces are analyzed in detail and yield insight into the effect of local conditions in the vicinity of interfaces on their motion, as well as the misguiding effects of 2D observations of processes taking place in 3D.

Point spread function (PSF) deconvolution is an attractive software-based technique for resolution improvement in the scanning electron microscope (SEM) because it can restore information which has been blurred by challenging operating conditions. In Part 1, we studied a modern PSF determination method for SEM and explored how various parameters affected the method's ability to accurately estimate the PSF. In Part 2, we extend this exploration to PSF deconvolution for image restoration. The parameters include reference particle size, PSF smoothing (K), background correction, and restoration denoising (λ). Image quality was assessed by visual inspection and Fourier analysis. Overall, PSF deconvolution improved image quality. Low λ enhanced image sharpness at the cost of noise, while high λ created smoother restorations with less detail. λ should be chosen to balance feature preservation and denoising based on the application. Reference particle size within ±0.9 nm and K within a reasonable range had little effect on restoration quality. Restorations using background-corrected PSFs had superior quality compared with using no background correction, but if the correction was too high, the PSF was cut off causing blurrier restorations. Future efforts to automatically determine parameters would remove user guesswork, improve this method's consistency, and maximize interpretability of outputs.

Giant cell tumor (GCT) of bone is a common benign lesion that causes significant morbidity due to the failure of modern medical and surgical treatment. Surface ultra-structures of giant cells (GCs) may help in distinguishing aggressive tumors from indolent GC lesions. This study aimed to standardize scanning electron microscopic (SEM) imaging of GC from GCT of bone. Fresh GCT collected in Dulbecco's Modified Eagle Medium was washed to remove blood, homogenized, or treated with collagenase to isolate the GCs. Mechanically homogenized and collagenase-digested GCs were imaged on SEM after commonly used drying methodologies such as air-drying, tetramethylsilane (TMS)-drying, freeze-drying, and critical point-drying (CPD) for the optimization of sample processing. The collagenase-treated samples yielded a greater number of isolated GC and showed better surface morphology in comparison to mechanical homogenization. Air-drying was associated with marked cell shrinkage, and freeze-dried samples showed severe cell damage. TMS methodology partially preserved the cell contour and surface structures, although the cell shape was distorted. GC images with optimum surface morphology including membrane folding and microvesicular structures on the surface were observed only in collagenase-treated and critical point-dried samples. Collagenase digestion and critical point/TMS-drying should be performed for optimal SEM imaging of individual GCs.

During recrystallization, the growth of fresh grains initiated within a deformed microstructure causes dramatic changes in the dislocation structure and density of a heavily deformed matrix. In this paper, the microstructure of a cold rolled and partially recrystallized Al-Mg alloy (AA5052) was studied via electron backscattered diffraction (EBSD) analysis. The structure and density of the geometrically necessary dislocations (GNDs) were predicted using a combination of continuum mechanics and dislocation theory. Accordingly, the Nye dislocation tensor, which determines the GND structure, was estimated by calculation of the lattice curvature. To do so, five components of the Nye dislocation tensor were directly calculated from the local orientation of surface points of the specimen, which was determined by two-dimensional EBSD. The remaining components of GNDs were determined by minimizing a normalized Hamiltonian equation based on dislocation energy. The results show the elimination of low angle boundaries, lattice curvature, and GNDs in recrystallized regions and the formation of low angle boundaries with orientation discontinuities in deformed grains, which may be due to static recovery.

Scanning electron microscopy (SEM) is one of the most common inspection methods in the semiconductor industry and in research labs. To extract the height of structures using SEM images, various techniques have been used, such as tilting a sample, or modifying the SEM tool with extra sources and/or detectors. However, none of these techniques focused on extraction of height information directly from top-down images. In this work, using Monte Carlo simulations, we studied the relation between step height and the emission of secondary electrons (SEs) resulting from exposure with primary electrons at different energies. It is found that part of the SE signal, when scanning over a step edge, is determined by the step height rather than the geometry of the step edge. We present a way to quantify this, arriving at a method to determine the height of structures from top-down SEM images. The method is demonstrated on three different samples using two different SEM tools, and atomic force microscopy is used to measure the step height of the samples. The results obtained are in qualitative agreement with the results from the Monte Carlo simulations.

Adding Au to Pd nanoparticles (NPs) can impart high catalytic activity with respect to hydrogenation of a wide range of substances. These materials are often synthesized by reducing metallic precursors; hence, sonochemical and solvothermal processes are commonly used to anchor these bimetals onto thin supports, including graphene. Although similar NPs have been studied reasonably well, a clear understanding of structural characteristics relative to their synthesis parameters is lacking, due to limitations in characterization techniques, which may prevent optimization of this very promising catalyst. In this report, a strategic approach has been used to identify this structural and material synthesis correlation, starting with controlled sample preparation and followed by detailed characterization. This includes advanced scanning transmission electron microscopy and electron energy loss spectroscopy; the latter using a state-of-the-art instrumentation to map the distribution of Pd and Au, and to identify chemical state of the Pd NPs, which has not been previously reported. Results show that catalytic bimetal NP clusters were made of small zero-valent Pd NPs aggregating to form a shell around an Au core. Not only can the described characterization approach be applied to similar material systems, but the results can guide the optimization of the synthesis procedures.

Atom probe tomography (APT) is a powerful technique to characterize buried three-dimensional nanostructures in a variety of materials. Accurate characterization of those nanometer-scale clusters and precipitates is of great scientific significance to understand the structure–property relationships and the microstructural evolution. The current widely used cluster analysis method, a variant of the density-based spatial clustering of applications with noise algorithm, can only accurately extract clusters of the same atomic density, neglecting several experimental realities, such as density variations within and between clusters and the nonuniformity of the atomic density in the APT reconstruction itself (e.g., crystallographic poles and other field evaporation artifacts). This clustering method relies heavily on multiple input parameters, but ideal selection of those parameters is challenging and oftentimes ambiguous. In this study, we utilize a well-known cluster analysis algorithm, called ordering points to identify the clustering structures, and an automatic cluster extraction algorithm to analyze clusters of varying atomic density in APT data. This approach requires only one free parameter, and other inputs can be estimated or bounded based on physical parameters, such as the lattice parameter and solute concentration. The effectiveness of this method is demonstrated by application to several small-scale model datasets and a real APT dataset obtained from an oxide-dispersion strengthened ferritic alloy specimen.

GaN films have been grown on SiC substrates with an AlN nucleation layer by using a metal organic chemical vapor deposition technique. Micro-cracking of the GaN films has been observed in some of the grown samples. In order to investigate the micro-cracking and microstructure, the samples have been studied using various characterization techniques such as optical microscopy, atomic force microscopy, Raman spectroscopy, scanning electron microscopy and transmission electron microscopy (TEM). The surface morphology of the AlN nucleation layer is related to the stress evolution in subsequent overgrown GaN epilayers. It is determined via TEM evidence that, if the AlN nucleation layer has a rough surface morphology, this leads to tensile stresses in the GaN films, which finally results in cracking. Raman spectroscopy results also suggest this, by showing the existence of considerable tensile residual stress in the AlN nucleation layer. Based on these various observations and results, conclusions or propositions relating to the microstructure are presented.

The recent availability of Schottky-type field emission electron microprobes provides incentive to consider analyzing micrometer-sized features. Yet, to quantify sub-micrometer-sized features, the electron interaction volume must be reduced by decreasing accelerating voltage. However, the K lines of the transition elements (e.g., Fe) then cannot be excited, and the L lines must be used. The Fe Lα1,2 line is the most intense of the L series but bonding effects change its atomic parameters because it involves a valence band electron transition. For successful traditional electron probe microanalysis, the mass absorption coefficient (MAC) must be accurately known, but the MAC of Fe Lα1,2 radiation by Fe atoms varies from one Fe-compound to another and is not well known. We show that the conventional method of measuring the MAC by an electron probe cannot be used in close proximity to absorption edges, making its accurate determination impossible. Fortunately, we demonstrate, using a set of Fe–silicide compounds, that it is possible to derive an accurate calibration curve, for a given accelerating voltage and takeoff angle, which can be used to quantify Fe in Fe–silicide compounds. The calibration curve can be applied to any spectrometer without calibration and gives accurate quantification results.

A procedure to analyze the elemental concentration distribution inside solute clusters after detection of clusters from atom probe tomography data set was proposed. We developed a code which can directly illustrate an average concentration profile inside a cluster even in the case of including various sizes of ellipsoidal clusters. The profile can be with respect to absolute distance and includes errors in each data point. The reliability of the developed code was verified by analyzing an artificial cluster model which has inhomogeneous elemental distribution. It was found that the precise estimation of cluster centroids is important and that the preferable conditions for targeting clusters are a detection efficiency of over 20%, over 30 atoms in a cluster on average, and over 100 atoms for each concentration data point.

An automated approach to fully reconstruct spherical Kikuchi maps from experimentally collected electron backscatter diffraction patterns and overlay each pattern onto its corresponding position on a simulated Kikuchi sphere is presented in this study. This work demonstrates the feasibility of warping any Kikuchi pattern onto its corresponding location of a simulated Kikuchi sphere and reconstructing a spherical Kikuchi map of a known phase based on any set of experimental patterns. This method consists of the following steps after pattern collection: (1) pattern selection based on multiple threshold values; (2) extraction of multiple scan parameters and phase information; (3) generation of a kinematically simulated Kikuchi sphere as the “skeleton” of the spherical Kikuchi map; and (4) overlaying the inverse gnomonic projection of multiple selected patterns after appropriate pattern center calibration and refinement. The proposed method is the first automated approach to reconstructing spherical Kikuchi maps from experimental Kikuchi patterns. It potentially enables more accurate orientation calculation, new pattern center refinement methods, improved dictionary-based pattern matching, and phase identification in the future.

Secondary fluorescence effects are important sources of characteristic X-ray emissions, especially for materials with complicated geometries. Currently, three approaches are used to calculate fluorescence X-ray intensities. One is using Monte Carlo simulations, which are accurate but have drawbacks such as long computation times. The second one is to use analytical models, which are computationally efficient, but limited to specific geometries. The last approach is a hybrid model, which combines Monte Carlo simulations and analytical calculations. In this article, a program is developed by combining Monte Carlo simulations for X-ray depth distributions and an analytical model to calculate the secondary fluorescence. The X-ray depth distribution curves of both the characteristic and bremsstrahlung X-rays obtained from Monte Carlo program MC X-ray allow us to quickly calculate the total fluorescence X-ray intensities. The fluorescence correction program can be applied to both bulk and multilayer materials. Examples for both cases are shown. Simulated results of our program are compared with both experimental data from the literature and simulation data from PENEPMA and DTSA-II. The practical application of the hybrid model is presented by comparing with the complete Monte Carlo program.

Adipogenesis is a developmental process in which an elongated preadipocyte differentiates to a round adipocyte along with the accumulation of lipid droplets. In the present study, we focus on the study of cell motility at the single-cell level, toward expanding our knowledge regarding the cytoskeleton alteration during differentiation; since-cell motility is mediated by cytoskeletal components. We used the holographic-microscopy live imaging technique to evaluate, for the first time in the literature, differences between the motility of nondifferentiated preadipocytes and differentiated mature adipocytes in living cell cultures over time. We revealed that mean motility speed of preadipocytes was significantly higher (fourfold) than that of adipocytes, and that the movement of preadipocytes is less consistent and more extensive. Furthermore, we found that preadipocytes tend to migrate to farther distances, while mature adipocytes remain relatively close to their original location. The results presented here are in agreement with the fact that the cytoskeleton of adipocytes is altered during differentiation and similarly, points to the fact that the cell-sensing mechanisms are changing during differentiation. Our research paves the way to gain better insights of the differentiation process and its implications on larger scale systems in the context of obesity.