Scanning transmission electron microscopy (STEM) is widely used for imaging, diffraction, and spectroscopy of materials down to atomic resolution. Recent advances in detector technology and computational methods have enabled many experiments that record a full image of the STEM probe for many probe positions, either in diffraction space or real space. In this paper, we review the use of these four-dimensional STEM experiments for virtual diffraction imaging, phase, orientation and strain mapping, measurements of medium-range order, thickness and tilt of samples, and phase contrast imaging methods, including differential phase contrast, ptychography, and others.

In this study, we have examined ceramic matrix composites with silicon carbide fibers in a melt-infiltrated silicon carbide matrix (SiC/SiC). We subjected samples to tensile loads while collecting micro X-ray computed tomography images. The results showed the expected crack slowing mechanisms and lower resistance to crack propagation where the fibers ran parallel and perpendicular to the applied load respectively. Cracking was shown to initiate not only from the surface but also from silicon inclusions. Post heat-treated samples showed longer fiber pull-out than the pristine samples, which was incompatible with previously proposed mechanisms. Evidence for oxidation was identified and new mechanisms based on oxidation or an oxidation assisted boron nitride phase transformation was therefore proposed to explain the long pull-out. The role of oxidation emphasizes the necessity of applying oxidation resistant coatings on SiC/SiC.

In situ transmission electron microscope (TEM) characterization techniques provide valuable information on structure–property correlations to understand the behavior of materials at the nanoscale. However, understanding nanoscale structures and their interaction with the electron beam is pivotal for the reliable interpretation of in situ/ex situ TEM studies. Here, we report that oxides commonly used in nanoelectronic applications, such as transistor gate oxides or memristive devices, are prone to electron beam induced damage that causes small structural changes even under very low dose conditions, eventually changing their electrical properties as examined via in situ measurements. In this work, silicon, titanium, and niobium oxide thin films are used for in situ TEM electrical characterization studies. The electron beam induced reduction of the oxides turns these insulators into conductors. The conductivity change is reversible by exposure to air, supporting the idea of electron beam reduction of oxides as primary damage mechanism. Through these measurements we propose a limit for the critical dose to be considered for in situ scanning electron microscopy and TEM characterization studies.

The use of concrete-recycled aggregates to produce high-performance concrete is limited by insufficient correlation between resulting microstructure and its influence on mechanical performance reproducibility. This work addresses this issue in a sequential approach: concrete microstructure was systematically analyzed and characterized by scanning electron microscopy and results were correlated with concrete compressive strength and water absorption ability. The influence of replacing natural aggregates (NA) with recycled concrete aggregates (RCA), with different source concrete strength levels, of silica fume (SF) addition and of mixing procedure was tested. The results show that the developed microstructure depends on the concrete composition and is conditioned by the distinct nature of NA, recycled aggregates from high-strength source concrete, and recycled aggregates from low-strength source concrete. SF was only effective at concrete densification when a two-stage mixing approach was used. The highest achieved strength in concrete with 100% incorporation of RCA was 97.3 MPa, comparable to that of conventional high-strength concrete with NA. This shows that incorporation of significant amounts of RCA replacing NA in concrete is not only a realistic approach to current environmental goals, but also a viable route for the production of high-performance concrete.

Understanding and resolving discrepancies between atom probe tomography (APT) and secondary ion mass spectrometry (SIMS) measurements of B dopants in Si-based materials has long been a problem for those in the semiconductor community who wish to measure B within the source/drain SiGe of a device. APT data collection of Si-based materials is typically optimized for Si, which is logical, but perhaps not ideal for field evaporation of B. Increasing the evaporation field well beyond the typically used 28Si2+:28Si+ ratio of approximately 10:1 up to a ratio of ~200:1 is demonstrated to improve B detection while retaining well-matched Si and Ge concentrations with respect to those measured by SIMS. A range of evaporation conditions are examined from a very low field with high laser energy to an extremely high field with extremely low laser energy demonstrating problems at both far ends of the spectrum and a sweet spot when the operating conditions used produce a 28Si2+:28Si+ ratio of approximately 200:1 (in terms of total counts of each ionization state), which is more than an order of magnitude higher than normally used conditions and results in nicely matched B, Si, and Ge APT measurements with those of SIMS.

The fluorescence effect induced by Kβ photons is usually so small that it can be neglected. However, in the Fe–Mn system, omitting Kβ fluorescence correction will lead to the overestimation of the Mn content especially when Mn is the minor alloy element. In this study, the error in the Mn concentration induced by Kβ fluorescence was investigated by both Monte Carlo simulation, using the pyPENELOPE program, and systematic electron probe measurements on the Fe–0.53% Mn alloy standard by the aid of CalcZAF software. It is shown that the error caused by Kβ fluorescence exceeds 4% for the Fe–0.53% Mn alloy. The problem can be overcome by utilizing CalcZAF in which β-line fluorescence has been included, or by employing a similar standard Fe–0.85% Mn for Mn in the absence of β-line fluorescence correction. In addition, a modified calibration curve method, using k-values instead of X-ray intensity as a variable, is presented and used to measure the Mn concentration. The accuracy of this method is as good as or better than that of the conventional matrix correction method. Compared with conventional calibration curve methods, it is time-saving because the k-value is not sensitive to instrument fluctuations and the established curve remains valid for a long period.

Silver nanowire (AgNW) diameters are typically characterized by manual measurement from high magnification electron microscope images. Measurement is monotonous and has potential ergonomic hazards. Because of this, statistics regarding wire diameter distribution can be poor, costly, and low-throughput. In addition, manual measurements are of unknown uncertainty and operator bias. In this paper we report an improved microscopy method for diameter and yield measurement of nanowires in terms of speed/automation and reduction of analyst variability. Each step in the process to generate these measurements was analyzed and optimized: microscope imaging conditions, sample preparation for imaging, image acquisition, image analysis, and data processing. With the resulting method, average diameter differences between samples of just a few nanometers can be confidently and statistically distinguished, allowing the identification of subtle incremental improvements in reactor processing conditions, and insight into nucleation and growth kinetics of AgNWs.

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.

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.

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.

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.

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.

Enzyme histochemistry is a valuable histological method which provides a connection between morphology, activity, and spatial localization of investigated enzymes. Even though the method relies purely on arbitrary evaluations performed by the human eye, it is still wildly accepted and used in histo(patho)logy. Texture analysis emerged as an excellent tool for image quantification of subtle differences reflected in both spatial discrepancies and gray level values of pixels. The current study of texture analysis utilizes the gray-level co-occurrence matrix as a method for quantification of differences between ecto-5′-nucleotidase activities in healthy hippocampal tissue and tissue with marked neurodegeneration. We used the angular second moment, contrast (CON), correlation, inverse difference moment (INV), and entropy for texture analysis and receiver operating characteristic analysis with immunoblot and qualitative assessment of enzyme histochemistry as a validation. Our results strongly argue that co-occurrence matrix analysis could be used for the determination of fine differences in the enzyme activities with the possibility to ascribe those differences to regions or specific cell types. In addition, it emerged that INV and CON are especially useful parameters for this type of enzyme histochemistry analysis. We concluded that texture analysis is a reliable method for quantification of this descriptive technique, thus removing biases and adding it a quantitative dimension.

Routine system checks are essential for supervising the performance of an advanced light microscope. Recording and evaluating the point spread function (PSF) of a given system provides information about the resolution and imaging. We compared the performance of fluorescent and gold beads for PSF recordings. We then combined the open-source evaluation software PSFj with a newly developed KNIME pipeline named PSFtracker to create a standardized workflow to track a system's performance over several measurements and thus over long time periods. PSFtracker produces example images of recorded PSFs, plots full-width-half-maximum (FWHM) measurements over time and creates an html file which embeds the images and plots, together with a table of results. Changes of the PSF over time are thus easily spotted, either in FWHM plots or in the time series of bead images which allows recognition of aberrations in the shape of the PSF. The html file, viewed in a local browser or uploaded on the web, therefore provides intuitive visualization of the state of the PSF over time. In addition, uploading of the html file on the web allows other microscopists to compare such data with their own.

Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs. These unwanted, aperiodic features extend the image along a primary direction and occupy a small wedge of information in Fourier space. Deleting this wedge of data replaces stripes, scratches, or curtaining, with more complex streaking and blurring artifacts—known within the tomography community as “missing wedge” artifacts. Here, we overcome this problem by recovering the missing region using total variation minimization, which leverages image sparsity-based reconstruction techniques—colloquially referred to as compressed sensing (CS)—to reliably restore images corrupted by stripe-like features. Our approach removes beam instability, ion mill curtaining, mechanical scratches, or any stripe features and remains robust at low signal-to-noise. The success of this approach is achieved by exploiting CS's inability to recover directional structures that are highly localized and missing in Fourier Space.

Analysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.

In this paper, prediction models are proposed which allow the mineralogical characterization of particle systems observed by X-ray micro tomography (XMT). The models are calibrated using 2D image data obtained by a combination of scanning electron microscopy and energy dispersive X-ray spectroscopy in a planar cross-section of the XMT data. To reliably distinguish between different minerals the models are based on multidimensional distributions of certain particle characteristics describing, for example, their size, shape, and texture. These multidimensional distributions are modeled using parametric Archimedean copulas which are able to describe the correlation structure of complex multidimensional distributions with only a few parameters. Furthermore, dimension reduction of the multidimensional vectors of particle characteristics is utilized to make non-parametric approaches such as the computation of distributions via kernel density estimation viable. With the help of such distributions the proposed prediction models are able to distinguish between different types of particles among the entire XMT image.

Due to recent advances in modeling the production of characteristic X-rays, Monte Carlo simulation of electron–solid interactions can provide improved quantitative estimates of X-ray intensities for both homogeneous and heterogeneous interaction volumes. In the case of homogeneous materials, these modeled X-ray intensities can predict with excellent accuracy, matrix corrections for arbitrary compositions, arbitrary emission lines, and electron energies. By pre-calculating these Monte Carlo X-ray intensities for pure element standards and a range of compositions of binary systems, we can derive matrix corrections for complex compositions in real-time by parameterizing these k-ratios as the so-called alpha factors. This method allows one to perform Monte Carlo-based bulk matrix corrections in seconds for arbitrary and complex compositions (with two or more elements), by combining these binary alpha factors using the so-called beta expression. We are systematically calculating X-ray intensities for 11 compositions from 1 to 99 wt% for binary pairs of all emitters and absorbers in the periodic table, for the main emission lines (Kα, Kβ, Lα, Lβ, Mα, and Mβ) at beam energies from 5 to 50 keV, using Monte Carlo calculations based on a modified PENELOPE electron-photon transport code, although any other Monte Carlo software could also be utilized. Comparison of k-ratios calculated with the proposed method and experimental k-ratios from the Pouchou and Pichoir database suggest improvements over typical φ(ρz) methods. Additional comparisons with k-ratio measurements from more complex compositions would be ideal, but our testing of the additivity of the beta equation suggests that arbitrary compositions can be handled as well, except in cases of extreme fluorescence or absorption.

Far-field three-dimensional X-ray diffraction microscopy allows for quick measurement of the centers of mass and volumes of a large number of grains in a polycrystalline material, along with their crystal lattice orientations and internal stresses. However, the grain boundaries—and, therefore, individual grain shapes—are not observed directly. The present paper aims to overcome this shortcoming by reconstructing grain shapes based only on the incomplete morphological data described above. To this end, cross-entropy (CE) optimization is employed to find a Laguerre tessellation that minimizes the discrepancy between its centers of mass and cell sizes and those of the measured grain data. The proposed algorithm is highly parallel and is thus capable of handling many grains (>8,000). The validity and stability of the CE approach are verified on simulated and experimental datasets.

The absence of quantitative in vitro cell–extracellular matrix models represents an important bottleneck for basic research and human health. Randomness of cellular distributions provides an opportunity for the development of a quantitative in vitro model. However, quantification of the randomness of random cell distributions is still lacking. In this paper, we have imaged cellular distributions in an alginate matrix using a multiview light sheet microscope and developed quantification metrics of randomness by modeling it as a Poisson process, a process that has constant probability of occurring in space or time. We imaged fluorescently labeled human mesenchymal stem cells embedded in an alginate matrix of thickness greater than 5 mm with $\sim\! {\rm 2}{\rm. 9} \pm {\rm 0}{\rm. 4}\,\mu {\rm m}$ axial resolution, the mean full width at half maximum of the axial intensity profiles of fluorescent particles. Simulated randomness agrees well with the experiments. Quantification of distributions and validation by simulations will enable quantitative study of cell–matrix interactions in tissue models.