Unscrambling Mixed Elements using High Angle Annular Dark Field Scanning Transmission Electron Microscopy

Element specific atom counting for heterogeneous nanostructures: Combining multiple ADF STEM images for simultaneous thickness and composition determination

In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core-shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Analysis of 3D elemental distribution in nanomaterials: Towards higher throughput and dose efficiency

Many advanced nanomaterials rely on carefully designed morphology and elemental distribution to achieve their functionalities. Among the few experimental techniques that can directly visualise the 3D elemental distribution on the nanoscale are approaches based on electron tomography in combination with energy-dispersive X-ray spectroscopy (EDXS) and electron energy loss spectroscopy (EELS). Unfortunately, these highly informative methods are severely limited by the fundamentally low signal-to-noise ratio, which makes long experimental times and high electron irradiation doses necessary to obtain reliable 3D reconstructions. Addressing these limitations has been the major research question for the development of these techniques in recent years. This short review outlines the latest progress on the methods to reduce experimental time and electron irradiation dose requirements for 3D elemental distribution analysis and gives an outlook on the development of this field in the near future.

Recent Developments of Microscopic Study for Lanthanide and Manganese Doped Luminescent Materials

Luminescent materials are indispensable for applications in lighting, displays and photovoltaics, which can transfer, absorb, store and utilize light energy. Their performance is closely related with their size and morphologies, exact atomic arrangement, and local configuration about photofunctional centers. Advanced electron microscopy-based techniques have enabled the possibility to study nanostructures with atomic resolution. Especially, with the advanced micro-electro-mechanical systems, it is able to characterize the luminescent materials at the atomic scale under various environments, providing a deep understanding of the luminescent mechanism. Accordingly, this review summarizes the recent achievements of microscopic study to directly image the microstructure and local environment of activators in lanthanide and manganese (Ln/Mn²⁺ )-doped luminescent materials, including: 1) bulk materials, the typical systems are nitride/oxynitride phosphors; and 2) nanomaterials, such as nanocrystals (hexagonal-phase NaLnF₄ and perovskite) and 2D nanosheets (Ca₂ Ta₃ O₁₀ and MoS₂ ). Finally, the challenges and limitations are highlighted, and some possible solutions to facilitate the developments of advanced luminescent materials are provided.

Element Specific Atom Counting at the Atomic Scale by Combining High Angle Annular Dark Field Scanning Transmission Electron Microscopy and Energy Dispersive X-ray Spectroscopy

A new methodology is presented to count the number of atoms in multimetallic nanocrystals by combining energy dispersive X-ray spectroscopy (EDX) and high angle annular dark field scanning transmission electron microscopy (HAADF STEM). For this purpose, the existence of a linear relationship between the incoherent HAADF STEM and EDX images is exploited. Next to the number of atoms for each element in the atomic columns, the method also allows quantification of the error in the obtained number of atoms, which is of importance given the noisy nature of the acquired EDX signals. Using experimental images of an Au@Ag core-shell nanorod, it is demonstrated that 3D structural information can be extracted at the atomic scale. Furthermore, simulated data of an Au@Pt core-shell nanorod show the prospect to characterize heterogeneous nanostructures with adjacent atomic numbers.

Advances and Applications of Atomic-Resolution Scanning Transmission Electron Microscopy

Although scanning transmission electron microscopy (STEM) images of individual heavy atoms were reported 50 years ago, the applications of atomic-resolution STEM imaging became wide spread only after the practical realization of aberration correctors on field-emission STEM/TEM instruments to form sub-Ångstrom electron probes. The innovative designs and advances of electron optical systems, the fundamental understanding of electron–specimen interaction processes, and the advances in detector technology all played a major role in achieving the goal of atomic-resolution STEM imaging of practical materials. It is clear that tremendous advances in computer technology and electronics, image acquisition and processing algorithms, image simulations, and precision machining synergistically made atomic-resolution STEM imaging routinely accessible. It is anticipated that further hardware/software development is needed to achieve three-dimensional atomic-resolution STEM imaging with single-atom chemical sensitivity, even for electron-beam-sensitive materials. Artificial intelligence, machine learning, and big-data science are expected to significantly enhance the impact of STEM and associated techniques on many research fields such as materials science and engineering, quantum and nanoscale science, physics and chemistry, and biology and medicine. This review focuses on advances of STEM imaging from the invention of the field-emission electron gun to the realization of aberration-corrected and monochromated atomic-resolution STEM and its broad applications.

First-Principles Evaluation of Volatile Organic Compounds Degradation in Z-Scheme Photocatalytic Systems: MXene and Graphitic-CN Heterostructures

It is a formidable challenge to use the traditional trial-and-error method to identify suitable catalysts for the photocatalytic degradation of volatile organic compounds (VOCs). In this work, by performing density functional theory calculations, we designed three Z-scheme g-CN/M₂CO₂ (M = Hf, Zr, and Sc) heterostructures, which not only exhibit favorable structure stability but also show promising ability for photocatalytic degradation of VOCs. The enhancement of the photocatalytic activity of these three Z-scheme systems can be ascribed to the low recombination rate of electron-hole pairs because photoelectrons migrated from the g-CN layer to the M₂CO₂ layer as well as the internal electric fields in the Z-scheme heterojunction. Among the three heterostructures, only g-CN/Zr₂CO₂ presents favorable spectra utilization under photoirradiation as well as the direct band gap. As a result, in the Z-scheme g-CN/Zr₂CO₂ heterostructure, the electrons in the conduction band of g-CN migrate to the holes in the valence band of the Zr₂CO₂ layer, which improves extraction and utilization of photogenerated electrons in the g-CN sheet. Moreover, the Z-scheme g-CN/Zr₂CO₂ system shows superior performance for photocatalytic VOC degradation in comparison with individual g-CN and Zr₂CO₂, which can be attributed to the enhanced VOC adsorption capacity as well as excellent ability to photoactivate O₂ and H₂O into ^•O₂^- and ^•OH radicals. Our findings pave a new promising way to facilitate the application of MXene-based materials for VOC photocatalytic degradation.

Stabilizing Pt-Based Electrocatalysts for Oxygen Reduction Reaction: Fundamental Understanding and Design Strategies

Proton exchange membrane fuel cells (PEMFCs) with high efficiency and nonpollution characteristics have attracted massive attention from both academic and industrial communities due to their irreplaceable roles in building the future sustainable energy system. However, the stability issue of Pt-based catalysts for oxygen reduction reaction (ORR) has become a central constraint to the widespread deployment of the devices relative to the catalytic activity. This review aims to provide comprehensive insights into how to improve the stability of Pt-based catalysts for ORR. First, the basic physical chemistry behind the catalyst degradation, including the fundamental understandings of carbon corrosion, catalyst dissolution, and particle sintering, is highlighted. After a discussion of advanced characterization techniques for the catalyst degradation, the design strategies for improving the stability of Pt-based catalysts are summarized. Finally, further insights into the remaining challenges and future research directions are also provided.

Nonsymmetrical Segregation of Solutes in Periodic Misfit Dislocations Separated Tilt Grain Boundaries

Interfacial segregation is ubiquitous in mulit-component polycrystalline materials and plays a decisive role in material properties. So far, the discovered solute segregation patterns at special high-symmetry interfaces are usually located at the boundary lines or are distributed symmetrically at the boundaries. Here, in a model Mg-Nd-Mn alloy, we confirm that elastic strain minimization facilitated nonsymmetrical segregation of solutes in four types of linear tilt grain boundaries (TGBs) to generate ordered interfacial superstructures. Aberration-corrected high-angle annular dark-field scanning transmission electron microscopy observations indicate that the solutes selectively segregate at substitutional sites at the linear TGBs separated by periodic misfit dislocations to form such two-dimensional planar structures. These findings are totally different from the classical McLean-type segregation which has assumed the monolayer or submonolayer coverage of a grain boundary and refresh understanding on strain-driven interface segregation behaviors.

Optimizing Experimental Conditions for Accurate Quantitative Energy-Dispersive X-ray Analysis of Interfaces at the Atomic Scale

The invention of silicon drift detectors has resulted in an unprecedented improvement in detection efficiency for energy-dispersive X-ray (EDX) spectroscopy in the scanning transmission electron microscope. The result is numerous beautiful atomic-scale maps, which provide insights into the internal structure of a variety of materials. However, the task still remains to understand exactly where the X-ray signal comes from and how accurately it can be quantified. Unfortunately, when crystals are aligned with a low-order zone axis parallel to the incident beam direction, as is necessary for atomic-resolution imaging, the electron beam channels. When the beam becomes localized in this way, the relationship between the concentration of a particular element and its spectroscopic X-ray signal is generally nonlinear. Here, we discuss the combined effect of both spatial integration and sample tilt for ameliorating the effects of channeling and improving the accuracy of EDX quantification. Both simulations and experimental results will be presented for a perovskite-based oxide interface. We examine how the scattering and spreading of the electron beam can lead to erroneous interpretation of interface compositions, and what approaches can be made to improve our understanding of the underlying atomic structure.

Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

Warwick electron microscopy datasets

Large, carefully partitioned datasets are essential to train neural networks and standardize performance benchmarks. As a result, we have set up new repositories to make our electron microscopy datasets available to the wider community. There are three main datasets containing 19769 scanning transmission electron micrographs, 17266 transmission electron micrographs, and 98340 simulated exit wavefunctions, and multiple variants of each dataset for different applications. To visualize image datasets, we trained variational autoencoders to encode data as 64-dimensional multivariate normal distributions, which we cluster in two dimensions by t-distributed stochastic neighbor embedding. In addition, we have improved dataset visualization with variational autoencoders by introducing encoding normalization and regularization, adding an image gradient loss, and extending t-distributed stochastic neighbor embedding to account for encoded standard deviations. Our datasets, source code, pretrained models, and interactive visualizations are openly available at https://github.com/Jeffrey-Ede/datasets.

Quantitative 3D Characterization of Elemental Diffusion Dynamics in Individual Ag@Au Nanoparticles with Different Shapes

Anisotropic bimetallic nanoparticles are promising candidates for plasmonic and catalytic applications. Their catalytic performance and plasmonic properties are closely linked to the distribution of the two metals, which can change during applications in which the particles are exposed to heat. Due to this fact, correlating the thermal stability of complex heterogeneous nanoparticles to their microstructural properties is of high interest for the practical applications of such materials. Here, we employ quantitative electron tomography in high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) mode to measure the 3D elemental diffusion dynamics in individual anisotropic Au-Ag nanoparticles upon heating in situ. This approach allows us to study the elemental redistribution in complex, asymmetric nanoparticles on a single particle level, which has been inaccessible to other techniques so far. In this work, we apply the proposed method to compare the alloying dynamics of Au-Ag nanoparticles with different shapes and compositions and find that the shape of the nanoparticle does not exhibit a significant effect on the alloying speed whereas the composition does. Finally, comparing the experimental results to diffusion simulations allows us to estimate the diffusion coefficients of the metals for individual nanoparticles.

Composition determination for quaternary III-V semiconductors by aberration-corrected STEM

Quantitative scanning transmission electron microscopy (STEM) is a powerful tool for the characterization of nano-materials. Absolute composition determination for ternary III-V semiconductors by direct comparison of experiment and simulation is well established. Here, we show a method to determine the composition of quaternary III-V semiconductors with two elements on each sub lattice from the intensities of one STEM image. As an example, this is applied to (GaIn)(AsBi). The feasibility of the method is shown in a simulation study that also explores the influence of detector angles and specimen thickness. Additionally, the method is applied to an experimental STEM image of a (GaIn)(AsBi) quantum well grown by metal organic vapour phase epitaxy. The obtained concentrations are in good agreement with X-ray diffraction and photoluminescence results.

Structure-Property Relationships in Lanthanide-Doped Upconverting Nanocrystals: Recent Advances in Understanding Core-Shell Structures

The production of upconverting nanostructures with tailored optical properties is of major technological interest, and rapid progress toward the realization of such production has been made in recent years. Ultimately, accurate understanding of nanostructure organization will lead to design rules for accurately tailoring optical properties. Here, the context of open questions still of general importance to the upconversion and nanocrystal communities is presented, with a particular emphasis on the structure-property relationships of core-shell upconverting nanocrystals. Although the optical properties of the latter have been thoroughly investigated, little is known regarding their atomic-scale organization. Indeed, solving the atomic-scale structure of such nanomaterials is challenging because of their intrinsic nonperiodic nature. Familiar concepts of crystallography are no longer appropriate; chemical and structural modulation waves must be introduced. To reveal the exact core-shell structures, innovative characterization techniques need to be applied and developed, as discussed herein. The continued development and application of structural characterization techniques will be vital to consolidate the currently incomplete link between atomic-scale structure and upconversion properties. This will ultimately provide a valuable contribution to the emerging detailed guidelines on how to better design upconverting nanostructures to achieve given optical properties in terms of efficiency, absorption, spectral emission, and dynamics.

The maximum a posteriori probability rule for atom column detection from HAADF STEM images

Recently, the maximum a posteriori (MAP) probability rule has been proposed as an objective and quantitative method to detect atom columns and even single atoms from high-resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images. The method combines statistical parameter estimation and model-order selection using a Bayesian framework and has been shown to be especially useful for the analysis of the structure of beam-sensitive nanomaterials. In order to avoid beam damage, images of such materials are usually acquired using a limited incoming electron dose resulting in a low contrast-to-noise ratio (CNR) which makes visual inspection unreliable. This creates a need for an objective and quantitative approach. The present paper describes the methodology of the MAP probability rule, gives its step-by-step derivation and discusses its algorithmic implementation for atom column detection. In addition, simulation results are presented showing that the performance of the MAP probability rule to detect the correct number of atomic columns from HAADF STEM images is superior to that of other model-order selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Moreover, the MAP probability rule is used as a tool to evaluate the relation between STEM image quality measures and atom detectability resulting in the introduction of the so-called integrated CNR (ICNR) as a new image quality measure that better correlates with atom detectability than conventional measures such as signal-to-noise ratio (SNR) and CNR.

Simultaneous determination of local thickness and composition for ternary III-V semiconductors by aberration-corrected STEM

Scanning transmission electron microscopy (STEM) is a suitable method for the quantitative characterization of nanomaterials. For an absolute composition determination on an atomic scale, the thickness of the specimen has to be known locally with high accuracy. Here, we propose a method to determine both thickness and composition of ternary III-V semiconductors locally from one STEM image as shown for the example material systems Ga(AsBi) and (GaIn)As. In a simulation study, the feasibility of the method is proven and the influence of specimen thickness and detector angles used is investigated. An application to an experimental STEM image of a Ga(AsBi) quantum well grown by metal organic vapour phase epitaxy yields an excellent agreement with composition results from high resolution X-ray diffraction.

Imaging Three-Dimensional Elemental Inhomogeneity in Pt-Ni Nanoparticles Using Spectroscopic Single Particle Reconstruction

The properties of nanoparticles are known to critically depend on their local chemistry but characterizing three-dimensional (3D) elemental segregation at the nanometer scale is highly challenging. Scanning transmission electron microscope (STEM) tomographic imaging is one of the few techniques able to measure local chemistry for inorganic nanoparticles but conventional methodologies often fail due to the high electron dose imparted. Here, we demonstrate realization of a new spectroscopic single particle reconstruction approach built on a method developed by structural biologists. We apply this technique to the imaging of PtNi nanocatalysts and find new evidence of a complex inhomogeneous alloying with a Pt-rich core, a Ni-rich hollow octahedral intermediate shell and a Pt-rich rhombic dodecahedral skeleton framework with less Pt at ⟨100⟩ vertices. The ability to gain evidence of local surface enrichment that varies with the crystallographic orientation of facets and vertices is expected to provide significant insight toward the development of nanoparticles for sensing, medical imaging, and catalysis.

Self-adapted clustering of solute atoms into a confined two-dimensional prismatic platelet with an ellipse-like quasi-unit cell

This paper reports a new structured prismatic platelet, self-assembled by an ellipse-like quasi-unit cell, precipitated in Mg-In-Yb and Mg-In-Ca ternary alloys and aged isothermally at 200°C using aberration-corrected high-angle annular dark-field scanning transmission electron microscopy combined with density functional theory computations. The ordered stacking of solute atoms along the [0001]α direction based on elliptically shaped self-adapted clustering leads to the generation of the quasi-unit cell. The bonding of these ellipse-like quasi-unit-cell rods by the Mg atomic columns along the 〈〉α directions formed a two-dimensional planar structure, which has three variants with a {}α habit plane and full coherence with the α-Mg matrix. This finding is important for understanding the clustering and stacking behaviors of solute atoms in condensed matter, and is expected to guide the future design of novel high-strength Mg alloys strengthened by such high-density prismatic platelets.

Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images

Single atom detection is of key importance to solving a wide range of scientific and technological problems. The strong interaction of electrons with matter makes transmission electron microscopy one of the most promising techniques. In particular, aberration correction using scanning transmission electron microscopy has made a significant step forward toward detecting single atoms. However, to overcome radiation damage, related to the use of high-energy electrons, the incoming electron dose should be kept low enough. This results in images exhibiting a low signal-to-noise ratio and extremely weak contrast, especially for light-element nanomaterials. To overcome this problem, a combination of physics-based model fitting and the use of a model-order selection method is proposed, enabling one to detect single atoms with high reliability.

Recent Advances in Transmission Electron Microscopy for Materials Science at the EMAT Lab of the University of Antwerp

The rapid progress in materials science that enables the design of materials down to the nanoscale also demands characterization techniques able to analyze the materials down to the same scale, such as transmission electron microscopy. As Belgium's foremost electron microscopy group, among the largest in the world, EMAT is continuously contributing to the development of TEM techniques, such as high-resolution imaging, diffraction, electron tomography, and spectroscopies, with an emphasis on quantification and reproducibility, as well as employing TEM methodology at the highest level to solve real-world materials science problems. The lab's recent contributions are presented here together with specific case studies in order to highlight the usefulness of TEM to the advancement of materials science.

A streaming multi-GPU implementation of image simulation algorithms for scanning transmission electron microscopy

Simulation of atomic-resolution image formation in scanning transmission electron microscopy can require significant computation times using traditional methods. A recently developed method, termed plane-wave reciprocal-space interpolated scattering matrix (PRISM), demonstrates potential for significant acceleration of such simulations with negligible loss of accuracy. Here, we present a software package called Prismatic for parallelized simulation of image formation in scanning transmission electron microscopy (STEM) using both the PRISM and multislice methods. By distributing the workload between multiple CUDA-enabled GPUs and multicore processors, accelerations as high as 1000 × for PRISM and 15 × for multislice are achieved relative to traditional multislice implementations using a single 4-GPU machine. We demonstrate a potentially important application of Prismatic, using it to compute images for atomic electron tomography at sufficient speeds to include in the reconstruction pipeline. Prismatic is freely available both as an open-source CUDA/C++ package with a graphical user interface and as a Python package, PyPrismatic.

A quantitative method for measuring small residual beam tilts in high-resolution transmission electron microscopy

In a transmission electron microscope, electron illumination beam tilt, or the degree of deviation of electron beam from its optical axis, is an important parameter that has a significant impact on image contrast and image interpretation. Although a large beam tilt can easily be noticed and corrected by the standard alignment procedure, a small residual beam tilt is difficult to measure and, therefore, difficult to account for quantitatively. Here we report a quantitative method for measuring small residual beam tilts, including its theoretical schemes, numerical simulation testing and experimental verification. Being independent of specimen thickness and taking specimen drifts into account in measurement, the proposed method is supplementary to the existing "rotation center" and "coma-free" alignment procedures. It is shown that this method can achieve a rather good accuracy of 94% in measuring small residual beam tilts of about 0.1° or less.

Simplifying Electron Beam Channeling in Scanning Transmission Electron Microscopy (STEM)

Sub-angstrom scanning transmission electron microscopy (STEM) allows quantitative column-by-column analysis of crystalline specimens via annular dark-field images. The intensity of electrons scattered from a particular location in an atomic column depends on the intensity of the electron probe at that location. Electron beam channeling causes oscillations in the STEM probe intensity during specimen propagation, which leads to differences in the beam intensity incident at different depths. Understanding the parameters that control this complex behavior is critical for interpreting experimental STEM results. In this work, theoretical analysis of the STEM probe intensity reveals that intensity oscillations during specimen propagation are regulated by changes in the beam's angular distribution. Three distinct regimes of channeling behavior are observed: the high-atomic-number (Z) regime, in which atomic scattering leads to significant angular redistribution of the beam; the low-Z regime, in which the probe's initial angular distribution controls intensity oscillations; and the intermediate-Z regime, in which the behavior is mixed. These contrasting regimes are shown to exist for a wide range of probe parameters. These results provide a new understanding of the occurrence and consequences of channeling phenomena and conditions under which their influence is strengthened or weakened by characteristics of the electron probe and sample.

Three-dimensional atomic models from a single projection using Z-contrast imaging: verification by electron tomography and opportunities

In order to fully exploit structure-property relations of nanomaterials, three-dimensional (3D) characterization at the atomic scale is often required. In recent years, the resolution of electron tomography has reached the atomic scale. However, such tomography typically requires several projection images demanding substantial electron dose. A newly developed alternative circumvents this by counting the number of atoms across a single projection. These atom counts can be used to create an initial atomic model with which an energy minimization can be applied to obtain a relaxed 3D reconstruction of the nanoparticle. Here, we compare, at the atomic scale, this single projection reconstruction approach with tomography and find an excellent agreement. This new approach allows for the characterization of beam-sensitive materials or where the acquisition of a tilt series is impossible. As an example, the utility is illustrated by the 3D atomic scale characterization of a nanodumbbell on an in situ heating holder of limited tilt range.