A decade ago, a statistics-based method was introduced to count the number of atoms from annular dark-field scanning transmission electron microscopy (ADF STEM) images. In the past years, this method was successfully applied to nanocrystals of arbitrary shape, size, and composition (and its high accuracy and precision has been demonstrated). However, the counting results obtained from this statistical framework are so far presented without a visualization of the actual uncertainty about this estimate. In this paper, we present three approaches that can be used to represent counting results together with their statistical error, and discuss which approach is most suited for further use based on simulations and an experimental ADF STEM image.

Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.

This article introduces a training simulator for electron beam alignment using Ronchigrams. The interactive web application, www.ronchigram.com, is an advanced educational tool aimed at making scanning transmission electron microscopy (STEM) more accessible and open. For experienced microscopists, the tool offers on-hand quantification of simulated Ronchigrams and their resolution limits.

Increasing interest in three-dimensional nanostructures adds impetus to electron microscopy techniques capable of imaging at or below the nanoscale in three dimensions. We present a reconstruction algorithm that takes as input a focal series of four-dimensional scanning transmission electron microscopy (4D-STEM) data. We apply the approach to a lead iridate, Pb$_2$Ir$_2$O$_7$, and yttrium-stabilized zirconia, Y$_{0.095}$Zr$_{0.905}$O$_2$, heterostructure from data acquired with the specimen in a single plan-view orientation, with the epitaxial layers stacked along the beam direction. We demonstrate that Pb–Ir atomic columns are visible in the uppermost layers of the reconstructed volume. We compare this approach to the alternative techniques of depth sectioning using differential phase contrast scanning transmission electron microscopy (DPC-STEM) and multislice ptychographic reconstruction.

Artificial intelligence (AI) promises to reshape scientific inquiry and enable breakthrough discoveries in areas such as energy storage, quantum computing, and biomedicine. Scanning transmission electron microscopy (STEM), a cornerstone of the study of chemical and materials systems, stands to benefit greatly from AI-driven automation. However, present barriers to low-level instrument control, as well as generalizable and interpretable feature detection, make truly automated microscopy impractical. Here, we discuss the design of a closed-loop instrument control platform guided by emerging sparse data analytics. We hypothesize that a centralized controller, informed by machine learning combining limited a priori knowledge and task-based discrimination, could drive on-the-fly experimental decision-making. This platform may unlock practical, automated analysis of a variety of material features, enabling new high-throughput and statistical studies.

Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy-based technique, electron beam-induced current imaging, except that the signal in the STEM is generated by the emission of SEs, hence the name secondary electron e-beam-induced current (SEEBIC), and in this case, the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices.

The characterization of the three-dimensional arrangement of dislocations is important for many analyses in materials science. Dislocation tomography in transmission electron microscopy is conventionally accomplished through intensity-based reconstruction algorithms. Although such methods work successfully, a disadvantage is that they require many images to be collected over a large tilt range. Here, we present an alternative, semi-automated object-based approach that reduces the data collection requirements by drawing on the prior knowledge that dislocations are line objects. Our approach consists of three steps: (1) initial extraction of dislocation line objects from the individual frames, (2) alignment and matching of these objects across the frames in the tilt series, and (3) tomographic reconstruction to determine the full three-dimensional configuration of the dislocations. Drawing on innovations in graph theory, we employ a node-line segment representation for the dislocation lines and a novel arc-length mapping scheme to relate the dislocations to each other across the images in the tilt series. We demonstrate the method for a dataset collected from a dislocation network imaged by diffraction-contrast scanning transmission electron microscopy. Based on these results and a detailed uncertainty analysis for the algorithm, we discuss opportunities for optimizing data collection and further automating the method.

Recent advances in scanning transmission electron microscopy (STEM) have rekindled interest in multi-channel detectors and prompted the exploration of unconventional scan patterns. These emerging needs are not yet addressed by standard commercial hardware. The system described here incorporates a flexible scan generator that enables exploration of low-acceleration scan patterns, while data are recorded by a scalable eight-channel array of nonmultiplexed analog-to-digital converters. System integration with SerialEM provides a flexible route for automated acquisition protocols including tomography. Using a solid-state quadrant detector with additional annular rings, we explore the generation and detection of various STEM contrast modes. Through-focus bright-field scans relate to phase contrast, similarly to wide-field TEM. More strikingly, comparing images acquired from different off-axis detector elements reveals lateral shifts dependent on defocus. Compensation of this parallax effect leads to decomposition of integrated differential phase contrast (iDPC) to separable contributions relating to projected electric potential and to defocus. Thus, a single scan provides both a computationally refocused phase contrast image and a second image in which the signed intensity, bright or dark, represents the degree of defocus.

The scope of this work includes detailed microstructural investigations using high-resolution scanning transmission electron microscopy of the oxide scales and deposits formed on nozzle ring vanes made of IN713LC alloy during the operation of an auxiliary power unit under real conditions. The paper presents the differences of oxidation processes between the suction and the pressure sides of the vanes. It has been shown that in the pressure side of the vane, where a greater flow of exhaust gases from the combustion chamber is present, the oxidation process is accompanied by a deposition of among others (i.a.) Fe, Mg, Ca, Na, and P, while on the suction side of the vane, the thermal stresses and the mechanical loading most likely lead to a cracking of the oxide scale. A segregation of Cr, Ni, Ta, and Ti to the boundaries of α-Al2O3 grains is observed, which may indicate their diffusion from a metal to a gas atmosphere.

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.

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.

Determining free lime content in steelmaking slag is crucial for its safe reuse in road construction. A simple method has been recently developed to rapidly derive this value via cathodoluminescence (CL) imaging of steelmaking slag, previously quenched from 1,000°C to room temperature, according to the illuminated areas corresponding to free lime (luminescence peak at 600 nm). This quenching is required to obtain intense CL from free lime, but the mechanism of such signal enhancement is still unknown. Therefore, the present study investigated the mechanism by comparing the microstructures, CL images, and CL spectra of free lime in quenched and unquenched steelmaking slag. Large amounts of defects, including dislocations, were observed in the free lime emitting intense luminescence at 600 nm, whereas the samples without clear CL exhibited only a few defects. These results and previous studies suggest that the luminescence at 600 nm from free lime is enhanced by the CL originating from oxygen vacancies (380 nm); therefore, the enhancement of the intensity of the free lime CL peak could be attributed to the increase in the oxygen vacancies via quenching from 1,000°C to room temperature.

The development and implementation of high-stability monochromators in state-of-the-art aberration-corrected scanning transmission electron microscopes has enabled materials characterization with an energy resolution as good as 3 meV. This allows the vibrational modes, which would otherwise be obscured by the energy spread of the electron beam, to be probed with very high precision in molecular materials. Since the vibrational energies depend on the weight of the atomic nuclei, vibrational spectroscopy can distinguish isotopes whose only difference lies in their neutron content. This opens up isotopic analysis and mapping in transmission electron microscopy as two important new research areas. Here, we review the monochromated electron energy loss spectroscopy (EELS) instrumentation, discuss optimal methods for probing beam-sensitive materials without destroying them, and review key nanoscale isotope-resolved results.

When characterizing beam-sensitive materials in the scanning transmission electron microscope (STEM), low-dose techniques are essential for the reliable observation of samples in their true state. A simple route to minimize both the total electron-dose and the dose-rate is to reduce the electron beam-current and/or raster the probe at higher speeds. At the limit of these settings, and with current detectors, the resulting images suffer from unacceptable artifacts, including signal-streaking, detector-afterglow, and poor signal-to-noise ratios (SNRs). In this article, we present an alternative approach to capture dark-field STEM images by pulse-counting individual electrons as they are scattered to the annular dark-field (ADF) detector. Digital images formed in this way are immune from analog artifacts of streaking or afterglow and allow clean, high-SNR images to be obtained even at low beam-currents. We present results from both a ThermoFisher FEI Titan G2 operated at 300 kV and a Nion UltraSTEM200 operated at 200 kV, and compare the images to conventional analog recordings. ADF data are compared with analog counterparts for each instrument, a digital detector-response scan is performed on the Titan, and the overall rastering efficiency is evaluated for various scanning parameters.

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using nonrigid registration (NRR) and averaging of image series requires careful optimization of experimental conditions and the parameters of the registration algorithm. On experimental data from SrTiO3 [100], sub-pm precision requires alignment of the sample to the zone axis to within 1 mrad tilt and sample drift of less than 1 nm/min. At fixed total electron dose for the series, precision in the fast scan direction improves with shorter pixel dwell time to the limit of our microscope hardware, but the best precision along the slow scan direction occurs at 6 μs/px dwell time. Within the NRR algorithm, the “smoothness factor” that penalizes large estimated shifts is the most important parameter for sub-pm precision, but in general, the precision of NRR images is robust over a wide range of parameters.

Single-particle reconstruction can be used to perform three-dimensional (3D) imaging of homogeneous populations of nano-sized objects, in particular viruses and proteins. Here, it is demonstrated that it can also be used to obtain 3D reconstructions of heterogeneous populations of inorganic nanoparticles. An automated acquisition scheme in a scanning transmission electron microscope is used to collect images of thousands of nanoparticles. Particle images are subsequently semi-automatically clustered in terms of their properties and separate 3D reconstructions are performed from selected particle image clusters. The result is a 3D dataset that is representative of the full population. The study demonstrates a methodology that allows 3D imaging and analysis of inorganic nanoparticles in a fully automated manner that is truly representative of large particle populations.

Elevated temperature co-sputtering of immiscible elements results in a variety of self-organized morphologies due to phase separation. Cu–Ta is used as a model system to understand the evolution of phase-separated microstructural morphologies by co-sputtering thin films with nominal 50–50 at.% composition at four temperatures: 25, 400, 600, and 800 °C. Scanning/transmission electron microscopy of the film cross sections showed the microstructure morphology varied from nanocrystalline Cu–Ta at 25 °C to a wavy ribbon-like structure at 400 °C, to Cu-rich agglomerates surrounded by Ta-rich veins at 600 and 800 °C. In the agglomerate-vein morphology, microstructural features were present on two length scales, from a few nanometers to a few tens of nanometers, thus making the structures hierarchical. On the nanoscale, the Cu-rich agglomerates contained Ta precipitates, whereas the Ta-rich veins had embedded Cu nanocrystals. The various microstructures can be attributed to the highly disparate constituent element interdiffusion at the deposition temperatures with the Cu having orders of magnitude higher mobility than Ta at the deposition temperatures. This study of processing–microstructure relationship will be useful in guiding the design of hierarchical multiphase microstructures in binary or multicomponent thin films with tailored mechanical properties.

In this paper, the atomic resolution high-angle annular dark-field scanning transmission electron microscope (HAADF-STEM) was used as the main research method. Using HAADF-STEM, two types of long-period stacking ordered structure (LPSO)—14H and 18R-LPSO—were observed in Mg96Gd2Y1Ni1 alloy, and the precipitates at various stages of aging were observed. Moreover, a type of rectangular β precipitates were found, and the atomic models of β precipitates along the [0001]Mg and ${\tf="TeXGyrePagella-Bold (TrueType)"\char9001} 11\bar 20\hbox{]}_{{\rm{Mg}}}$ directions were identified. At the aging peak stage, a three-dimensional network structure composed of LPSO/γ′ precipitates and β′ precipitates and β precipitates was observed. The hardness of the unaged homogenized Mg96Gd2Y1Ni1 alloy was only 87 HV and the hardness value of aging peak was 128.4 HV. Compared with the unaged alloy, the hardness of the peak-aged alloy increased by 47.59%. The composite strengthening of the three types of precipitates induced a significant strengthening to the alloy.

This article introduces an intuitive understanding of electron Ronchigrams and how they are affected by aberrations. This is accomplished through a portable web application, http://Ronchigram.com. The history of the Ronchigram, the physics which define it, and its visual features are reviewed in the context of aberration-corrected scanning transmission electron microscopy.