Investigating the earliest stages of crystallization requires the transmission electron microscope (TEM) and is particularly challenging for materials which can be affected by the electron beam. Typically, when imaging at magnifications high enough to observe local crystallinity, the electron beam's current density must be high to produce adequate image contrast. Yet, minimizing the electron dose is necessary to reduce the changes caused by the beam. With the advent of a sensitive, high-speed, direct-detection camera for a TEM that is corrected for spherical aberration, it is possible to probe the early stages of crystallization at the atomic scale. High-quality images with low contrast can now be analyzed using new computing methods. In the present paper, this approach is illustrated for crystallization in a Ge2Sb2Te5 (GST-225) phase-change material which can undergo particularly rapid phase transformations and is sensitive to the electron beam. A thin (20 nm) film of GST-225 has been directly imaged in the TEM and the low-dose images processed using Python scripting to extract details of the nanoscale nuclei. Quantitative analysis of the processed images in a video sequence also allows the growth of such nuclei to be followed.

Inorganic gunshot residue (GSR) analysis is carried out by scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS) in many forensic laboratories. Characteristic GSR often consists of lead–barium–antimony, commonly associated with sulfur. The strength of forensic GSR evidence increases when unusual elements are found in residues collected both from the suspect and from the discharged firearm. The presence of molybdenum in GSR, due to the use of MoS2 lubricants in firearms, is experimentally demonstrated here for the first time. The most intense molybdenum X-ray emissions are MoL peaks at 2.3 keV which overlap with PbM and SK families due to the poor resolution of EDS detectors. When Pb, S, and Mo are allegedly present in the same particle, the reliability of automatic EDS routines is at risk. Missing identifications or false detections and exclusions may then occur. Molybdenum should be considered as detected only if MoK emissions meet the peak-to-background ratio minimum requirements. A strategy to spot Mo-containing residues is described, based on the automated search of MoS2, using a new “Sulfur only” class added to the classification scheme, followed by careful manual review of all GSR particles at an acceleration voltage of 30 kV. Our proposal improves commonly adopted forensic procedures currently followed in casework.

Quantitative structural characterization of nanomaterials is important to tailor their functional properties. Corrosion of AgAu-alloy nanoparticles (NPs) results in porous structures, making them interesting for applications especially in the fields of catalysis and surface-enhanced Raman spectroscopy. For the present report, structures of dealloyed NPs were reconstructed three-dimensionally using scanning transmission electron microscopy tomography. These reconstructions were evaluated quantitatively, revealing structural information such as pore size, porosity, specific surface area, and tortuosity. Results show significant differences compared to the structure of dealloyed bulk samples and can be used as input for simulations of diffusion or mass transport processes, for example, in catalytic applications.

The measurement of the composition of ε-Ga2O3 and the quantification of Sn doping in ε-Ga2O3:Sn by laser-assisted atom probe tomography (APT) may be inaccurate depending on the experimental conditions. Both the role of the laser energy and surface electric field were investigated, and the results clearly indicate that deviations from stoichiometry are observed changing the electric field conditions during APT. The measured atomic fraction of Ga can change from 0.45 at low field to 0.38 at high field, to be compared with the expected 0.4. This was interpreted in terms of preferential evaporation of Ga at high field and deficit of O at low field, which was caused by the formation of neutrals. The quantification of Sn-doping is accurate at low-field conditions, with an overestimation of the detected Sn-metallic fraction at high field. This suggests that Sn has a higher evaporation field compared to Ga. Finally, multiple detection events were in-depth studied, revealing that three dissociation reactions occur during APT: GaO2+ → Ga+ + O+; Ga2O22+ → Ga+ + GaO2+; Ga3O22+ → Ga+ + Ga2O2+. Nevertheless, only 2% of the detected events are related to such dissociation reactions, too small a fraction to fully explain the observed deviation from the stoichiometric composition in ε-Ga2O3.

Atomic force microscopy (AFM) measurements of dihedral angles are conducted for the first time to characterize the ratio of the twin-boundary energy (γΤ) to the surface free energy (γS). In plane, twin morphology is measured with AFM, verified by scanning electron microscopy, optical microscopy, and found to be consistent. The chemical composition and homogeneity of annealed Cu10 wt%Zn sample are confirmed by energy-dispersive spectroscopy. AFM data indicate that the average depth and height of the grooves and peaks are 118 ± 45 and 158 ± 45 nm, respectively. Surface roughness parameters, Sq and Sa, are measured by a factor of two to four less than the depth and height of the twin boundaries. Both surface roughness parameters are less with no planar defects present compared with selected areas containing twin boundaries. The average dihedral angle is found to be 167 ± 5° for the grooves and 193 ± 4° for the peaks. The twin to surface interfacial free energy ratio, γT/γS, is 0.0018. The comparison of AFM-based results to the other method-based results obtained on pure metals is discussed.

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

Recent work has revived interest in the scattering matrix formulation of electron scattering in transmission electron microscopy as a stepping stone toward atomic-resolution structure determination in the presence of multiple scattering. We discuss ways of visualizing the scattering matrix that make its properties clear. Through a simulation-based case study incorporating shot noise, we shown how regularizing on this continuity enables the scattering matrix to be reconstructed from 4D scanning transmission electron microscopy (STEM) measurements from a single defocus value. Intriguingly, for crystalline samples, this process also yields the sample thickness to nanometer accuracy with no a priori knowledge about the sample structure. The reconstruction quality is gauged by using the reconstructed scattering matrix to simulate STEM images at defocus values different from that of the data from which it was reconstructed.

Accurate control and measurement of real-time sample temperature are critical for the understanding and interpretation of the experimental results from in situ heating experiments inside environmental transmission electron microscope (ETEM). However, quantifying the real-time sample temperature remains a challenging task for commercial in situ TEM heating devices, especially under gas conditions. In this work, we developed a home-made micro-electrical-mechanical-system (MEMS) heater with unprecedented small temperature gradient and thermal drift, which not only enables the temperature evolution caused by gas injection to be measured in real-time but also makes the key heat dissipation path easier to model to theoretically understand and predict the temperature decrease. A new parameter termed as “gas cooling ability (H)”, determined purely by the physical properties of the gas, can be used to compare and predict the gas-induced temperature decrease by different gases. Our findings can act as a reference for predicting the real temperature for in situ heating experiments without closed-loop temperature sensing capabilities in the gas environment, as well as all gas-related heating systems.

We introduce a novel composite holey gold support that prevents cryo-crinkling and reduces beam-induced motion of soft specimens, building on the previously introduced all-gold support. The composite holey gold support for high-resolution cryogenic electron microscopy of soft crystalline membranes was fabricated in two steps. In the first step, a holey gold film was transferred on top of a molybdenum grid. In the second step, a continuous thin carbon film was transferred onto the holey gold film. This support (Au/Mo grid) was used to image crystalline synthetic polymer membranes. The low thermal expansion of Mo is not only expected to avoid cryo-crinkling of the membrane when the grids are cooled to cryogenic temperatures, but it may also act to reduce whatever crinkling existed even before cooling. The Au/Mo grid exhibits excellent performance with specimens tilted to 45°. This is demonstrated by quantifying beam-induced motion and differences in local defocus values. In addition, images of specimens on the Au/Mo grids that are tilted at 45° show high-resolution information of the crystalline membranes that, after lattice-unbending, extends beyond 1.5 Å in the direction perpendicular to the tilt axis.

Methods within the domain of artificial intelligence are gaining traction for solving a range of materials science objectives, notably the use of deep neural networks for computer vision for the analysis of electron diffraction patterns. An important component of deploying these models is an understanding of the performance as experimental diffraction conditions are varied. This knowledge can inspire confidence in the classifications over a range of operating conditions and identify where performance is degraded. Elucidating the relative impact of each parameter will suggest the most important parameters to vary during the collection of future training data. Knowing which data collection efforts to prioritize is of concern given the time required to collect or simulate vast libraries of diffraction patterns for a wide variety of materials without considering varying any parameters. In this work, five parameters, frame averaging, detector tilt, sample-to-detector distance, accelerating voltage, and pattern resolution, essential to electron diffraction are individually varied during the collection of electron backscatter diffraction patterns to explore the effect on the classifications produced by a deep neural network trained from diffraction patterns captured using a fixed set of parameters. The model is shown to be resilient to nearly all the individual changes examined here.

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

Manual selection of targets in experimental or diagnostic samples by transmission electron microscopy (TEM), based on single overview and detail micrographs, has been time-consuming and susceptible to bias. Substantial information and throughput gain may now be achieved by the automated acquisition of virtually all structures in a given EM section. Resulting datasets allow the convenient pan-and-zoom examination of tissue ultrastructure with preserved microanatomical orientation. The technique is, however, critically sensitive to artifacts in sample preparation. We, therefore, established a methodology to prepare large-scale digitization samples (LDS) designed to acquire entire sections free of obscuring flaws. For evaluation, we highlight the supreme performance of scanning EM in transmission mode compared with other EM technology. The use of LDS will substantially facilitate access to EM data for a broad range of applications.

A focused ion beam (FIB) technique describing the preparation of specimens for in situ thermal and electrical transmission electron microscopy is presented in detail. The method can be applied to a wide range of materials and allows the sample to be thinned down to electron transparency while it is attached to the in situ chip. This offers the advantage that the specimen can have a quality in terms of contamination and damage due to the ion beam that is comparable to samples prepared by means of conventional FIB preparation. Additionally, our technique can be performed by most commercially available FIB devices and only requires three simple, custom stubs for the procedure. This should enable a large userbase for this type of sample fabrication. One further benefit of our technique is that the in situ chip can be reused to create another sample on the same chip. The quality of the samples is demonstrated by high-resolution transmission electron microscopy as well as electron energy loss spectroscopy.

Scanning transmission electron microscopy (STEM) is an extremely versatile method for studying materials on the atomic scale. Many STEM experiments are supported or validated with electron scattering simulations. However, using the conventional multislice algorithm to perform these simulations can require extremely large calculation times, particularly for experiments with millions of probe positions as each probe position must be simulated independently. Recently, the plane-wave reciprocal-space interpolated scattering matrix (PRISM) algorithm was developed to reduce calculation times for large STEM simulations. Here, we introduce a new method for STEM simulation: partitioning of the STEM probe into “beamlets,” given by a natural neighbor interpolation of the parent beams. This idea is compatible with PRISM simulations and can lead to even larger improvements in simulation time, as well requiring significantly less computer random access memory (RAM). We have performed various simulations to demonstrate the advantages and disadvantages of partitioned PRISM STEM simulations. We find that this new algorithm is particularly useful for 4D-STEM simulations of large fields of view. We also provide a reference implementation of the multislice, PRISM, and partitioned PRISM algorithms.

Epithelial–mesenchymal transition (EMT) is an essential biological process, also implicated in pathological settings such as cancer metastasis, in which epithelial cells transdifferentiate into mesenchymal cells. We devised an image analysis pipeline to distinguish between tissues comprised of epithelial and mesenchymal cells, based on extracted features from immunofluorescence images of differing biochemical markers. Mammary epithelial cells were cultured with 0 (control), 2, 4, or 10 ng/mL TGF-β1, a well-established EMT-inducer. Cells were fixed, stained, and imaged for E-cadherin, actin, fibronectin, and nuclei via immunofluorescence microscopy. Feature selection was performed on different combinations of individual cell markers using a Bag-of-Features extraction. Control and high-dose images comprised the training data set, and the intermediate dose images comprised the testing data set. A feature distance analysis was performed to quantify differences between the treatment groups. The pipeline was successful in distinguishing between control (epithelial) and the high-dose (mesenchymal) groups, as well as demonstrating progress along the EMT process in the intermediate dose groups. Validation using quantitative PCR (qPCR) demonstrated that biomarker expression measurements were well-correlated with the feature distance analysis. Overall, we identified image pipeline characteristics for feature extraction and quantification of immunofluorescence images to distinguish progression of EMT.

A profound characteristic of field cancerization is alterations in chromatin packing. This study aimed to quantify these alterations using electron microscopy image analysis of buccal mucosa cells of laryngeal, esophageal, and lung cancer patients. Analysis was done on normal-appearing mucosa, believed to be within the cancerization field, and not tumor itself. Large-scale electron microscopy (nanotomy) images were acquired of cancer patients and controls. Within the nuclei, the chromatin packing of euchromatin and heterochromatin was characterized. Furthermore, the chromatin organization was quantified through chromatin packing density scaling. A significant difference was found between the cancer and control groups in the chromatin packing density scaling parameter for length scales below the optical diffraction limit (200 nm) in both the euchromatin (p = 0.002) and the heterochromatin (p = 0.006). The chromatin packing scaling analysis also indicated that the chromatin organization of cancer patients deviated significantly from the control group. They might allow for novel strategies for cancer risk stratification and diagnosis with high sensitivity. This could aid clinicians in personalizing screening strategies for high-risk patients and follow-up strategies for treated cancer patients.

Gray level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computer-based algorithm that can be used for the quantification of subtle changes in a cellular structure. In this work, we use this method for the detection of discrete alterations in hepatocyte chromatin distribution after in vivo exposure to iron oxide nanoparticles (IONPs). The study was performed on 40 male, healthy C57BL/6 mice divided into four groups: three experimental groups that received different doses of IONPs and 1 control group. We describe the dose-dependent reduction of chromatin textural uniformity measured as GLCM angular second moment. Similar changes were detected for chromatin textural uniformity expressed as the value of inverse difference moment. To the best of our knowledge, this is the first study to investigate the impact of iron-based nanomaterials on hepatocyte GLCM parameters. Also, this is the first study to apply discrete wavelet transform analysis, as a supplementary method to GLCM, for the assessment of hepatocyte chromatin structure in these conditions. The results may present the useful basis for future research on the application of GLCM and DWT methods in pathology and other medical research areas.