Electron Nanodiffraction

Revealing the Distribution of Lithium Compounds in Lithium Dendrites by Four-Dimensional Electron Microscopy Analysis

Characterizing the microstructure of radiation- and chemical-sensitive lithium dendrites and its solid electrolyte interphase (SEI) is an important task when investigating the performance and reliability of lithium-ion batteries. Widely used methods, such as cryogenic high-resolution transmission electron microscopy as well as related spectroscopy, are able to reveal the local structure at nanometer and atomic scale; however, these methods are unable to show the distribution of various crystal phases along the dendrite in a large field of view. In this work, two types of four-dimensional electron microscopy diffractive imaging methods, i.e., scanning electron nanodiffraction (SEND) and scanning convergent beam electron diffraction (SCBED), are employed to show a new pathway on characterizing the sensitive lithium dendrite samples at room temperature and in a large field of view. Combining with the non-negative matrix factorization (NMF) algorithm, orientations of different lithium metal grains along the lithium dendrite as well as different lithium compounds in the SEI layer are clearly identified.

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

Data-driven electron-diffraction approach reveals local short-range ordering in CrCoNi with ordering effects

The exceptional mechanical strength of medium/high-entropy alloys has been attributed to hardening in random solid solutions. Here, we evidence non-random chemical mixing in a CrCoNi alloy, resulting from short-range ordering. A data-mining approach of electron nanodiffraction enabled the study, which is assisted by neutron scattering, atom probe tomography, and diffraction simulation using first-principles theory models. Two samples, one homogenized and one heat-treated, are observed. In both samples, results reveal two types of short-range-order inside nanoclusters that minimize the Cr–Cr nearest neighbors (L1₂) or segregate Cr on alternating close-packed planes (L1₁). The L1₁ is predominant in the homogenized sample, while the L1₂ formation is promoted by heat-treatment, with the latter being accompanied by a dramatic change in dislocation-slip behavior. These findings uncover short-range order and the resulted chemical heterogeneities behind the mechanical strength in CrCoNi, providing general opportunities for atomistic-structure study in concentrated alloys for the design of strong and ductile materials.

Non-random chemical mixings that are intrinsic to medium- and high-entropy alloys are difficult to detect and quantify. Here the authors perform a diffraction data-mining analysis, revealing nanoclusters of short-range orders in a CrCoNi alloy, and their impacts on chemical homogeneity and dislocations slip.

Probing Multiscale Disorder in Pyrochlore and Related Complex Oxides in the Transmission Electron Microscope: A Review

Transmission electron microscopy (TEM), and its counterpart, scanning TEM (STEM), are powerful materials characterization tools capable of probing crystal structure, composition, charge distribution, electronic structure, and bonding down to the atomic scale. Recent (S)TEM instrumentation developments such as electron beam aberration-correction as well as faster and more efficient signal detection systems have given rise to new and more powerful experimental methods, some of which (e.g., 4D-STEM, spectrum-imaging, in situ/operando (S)TEM)) facilitate the capture of high-dimensional datasets that contain spatially-resolved structural, spectroscopic, time- and/or stimulus-dependent information across the sub-angstrom to several micrometer length scale. Thus, through the variety of analysis methods available in the modern (S)TEM and its continual development towards high-dimensional data capture, it is well-suited to the challenge of characterizing isometric mixed-metal oxides such as pyrochlores, fluorites, and other complex oxides that reside on a continuum of chemical and spatial ordering. In this review, we present a suite of imaging and diffraction (S)TEM techniques that are uniquely suited to probe the many types, length-scales, and degrees of disorder in complex oxides, with a focus on disorder common to pyrochlores, fluorites and the expansive library of intermediate structures they may adopt. The application of these techniques to various complex oxides will be reviewed to demonstrate their capabilities and limitations in resolving the continuum of structural and chemical ordering in these systems.

Open and strong-scaling tools for atom-probe crystallography: high-throughput methods for indexing crystal structure and orientation

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemical composition features and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterization tasks. For atom-probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT data sets contain substantial noise. Given that sufficiently general predictive models for such noise remain elusive, crystallography tools for APT face several limitations: their robustness to noise is limited, and therefore so too is their capability to identify and distinguish different crystal structures and orientations. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantification with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, the existing methods are reviewed and how they link to the methods currently used by the electron and X-ray diffraction communities is discussed. As a result of this, some of the APT methods are modified to yield more robust descriptors of the atomic arrangement. Also reported is how this enables the development of an open-source software tool for strong scaling and automated identification of a crystal structure, and the mapping of crystal orientation in nanocrystalline APT data sets with multiple phases.

Detecting minute amounts of nitrogen in GaNAs thin films using STEM and CBED

Nitrogen (N) is a common element added to GaAs for band gap engineering and strain compensation. However, detection of small amounts of N is difficult for electron microscopy as well as for other chemical analysis techniques. In this work, N in GaAs is examined by using different transmission electron microscopy (TEM) techniques. While both dark-field TEM imaging using the composition sensitive (002) reflections and selected area diffraction reveal a significant difference between the doped thin-film and the GaAs substrate, spectroscopy techniques such as electron energy loss and energy dispersive X-ray spectroscopy are not able to detect N. To quantify the N content, quantitative convergent beam electron diffraction (QCBED) is used, which gives a direct evidence of N substitution and As vacancies. The measurements are enabled by the electron energy-filtered scanning CBED technique. These results demonstrate a sensitive method for composition analysis based on quantitative electron diffraction.

Cepstral scanning transmission electron microscopy imaging of severe lattice distortions

The development of four-dimensional (4D) scanning transmission electron microscopy (STEM) using fast detectors has opened-up new avenues for addressing some of longstanding challenges in electron imaging. One of these challenges is how to image severely distorted crystal lattices, such as at a dislocation core. Here we develop a new 4D-STEM technique, called Cepstral STEM, for imaging disordered crystals using electron diffuse scattering. In contrast to analysis based on Bragg diffraction, which measures the average and periodic scattering potential, electron diffuse scattering can detect fluctuations caused by crystal disorder. Local fluctuations of diffuse scattering are captured by scanning electron nanodiffraction (SEND) using a coherent probe. The harmonic signals in electron diffuse scattering are detected through Cepstral analysis and used for imaging. By integrating Cepstral analysis with 4D-STEM, we demonstrate that information about the distortive part of electron scattering potential can be separated and imaged at nm spatial resolution. We apply the technique to the analysis of a dislocation core in SiGe and lattice distortions in a high entropy alloy.

Training artificial neural networks for precision orientation and strain mapping using 4D electron diffraction datasets

Techniques for training artificial neural networks (ANNs) and convolutional neural networks (CNNs) using simulated dynamical electron diffraction patterns are described. The premise is based on the following facts. First, given a suitable crystal structure model and scattering potential, electron diffraction patterns can be simulated accurately using dynamical diffraction theory. Secondly, using simulated diffraction patterns as input, ANNs can be trained for the determination of crystal structural properties, such as crystal orientation and local strain. Further, by applying the trained ANNs to four-dimensional diffraction datasets (4D-DD) collected using the scanning electron nanodiffraction (SEND) or 4D scanning transmission electron microscopy (4D-STEM) techniques, the crystal structural properties can be mapped at high spatial resolution. Here, we demonstrate the ANN-enabled possibilities for the analysis of crystal orientation and strain at high precision and benchmark the performance of ANNs and CNNs by comparing with previous methods. A factor of thirty improvement in angular resolution at 0.009˚ (0.16 mrad) for orientation mapping, sensitivity at 0.04% or less for strain mapping, and improvements in computational performance are demonstrated.

High resolution atomic scale characterization of dislocations in high entropy alloys: Critical assessment of template matching and geometric phase analysis

The paper assesses the applicability of advanced atomic resolution displacement measurement techniques to characterize dislocation character in metallic materials using simulated images derived from anisotropic elasticity and actual measurements in high entropy alloys. We draw attention to two techniques: the real space method of template matching (TeMA) and the reciprocal space method of geometric phase analysis (GPA) and provide a critical assessment. These techniques have limitations for direct evaluation of full dislocations Burgers vector or when local displacements are exceeding 50% lattice spacing. This is clearly illustrated with simulated arctangent displacement profiles reminiscent of dislocation cores. An approach for circumventing this limitation is suggested in the form of a nearest neighbor correction. Additionally, a methodology for determination of the Burgers vector is introduced on the basis of a vectorial rendering of the displacement field upon consideration of two zone axis measurements and applied to TeMA and GPA. The experimental results conform to the Burgers vector of a full lattice dislocation in the FCC crystal structure of the High-Entropy Alloy (HEA). The comparison of simulated and experimental images proves the efficacy of the HR-TEM (High Resolution Transmission Electron Microscopy) displacement mapping techniques while pointing to the need for caution in case of large displacements.