Scanning Transmission Electron Microscopy Data Research Articles

Quantification of 3D atomic structures and their dynamics from scanning transmission electron microscopy data

Quantification of 3D atomic structures and their dynamics from scanning transmission electron microscopy data

Automated Crystal System Identification from Four-dimensional Scanning Transmission Electron Microscopy Data Using Brain-inspired Artificial Intelligence.

Automated Crystal System Identification from Four-dimensional Scanning Transmission Electron Microscopy Data Using Brain-inspired Artificial Intelligence.

Quantitative x-ray diffraction analysis of strain and interdiffusion in β-Ga2O3 superlattices of μ-Fe2O3 and β-(AlxGa1−x)2O3

Superlattices composed of either monoclinic μ-Fe2O3 or β-(AlxGa1−x)2O3 with β-Ga2O3 spacers are grown on (010) β-Ga2O3 substrates using plasma-assisted molecular beam epitaxy. High-resolution x-ray diffraction data are quantitatively fit using commercial dynamical x-ray diffraction software (LEPTOS) to obtain layer thicknesses, strain, and compositions. The strain state of β-(AlxGa1−x)2O3 and μ-Fe2O3 superlattices as characterized using reciprocal space maps in the symmetric (020) and asymmetric (420) diffraction conditions indicates coherent growths that are strained to the (010) β-Ga2O3 lattice. β-(AlxGa1−x)2O3 and μ-Fe2O3 superlattices grown at hotter substrate temperatures result in crystal structures with better coherency and reduced defects compared to colder growths. The growth rate of μ-Fe2O3 is ∼2.6 nm/min at Tsub = 700 °C and drops to ∼1.6 nm/min at Tsub = 800 °C due to increased Fe interdiffusion at hotter substrate temperatures. Scanning transmission electron microscopy data of a μ-Fe2O3 superlattice grown at Tsub = 700 °C confirm that there is significant diffusion of Fe atoms into β-Ga2O3 layers.

A Three-Dimensional Reconstruction Algorithm for Scanning Transmission Electron Microscopy Data from a Single Sample Orientation

Abstract 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, PbIrO, and yttrium-stabilized zirconia, YZrO, 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.

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE.

Unveiling Charge Transport and Degradation Mechanisms of Aqueous Zn|α‐MoO3 Batteries in Conventional Concentration and Water‐in‐Salt Electrolytes: A Multi‐Modal In Situ and Operando Study

AbstractHerein charge storage and transport properties are elucidated and cell degradation mechanisms of rechargeable aqueous Zn|α‐MoO3 batteries in three electrolyte systems (3 m ZnSO4, 3 m ZnCl2, and 30 m ZnCl2 [12.5 m] water‐in‐salt (WIS)) are distinguished by a combination of in situ X‐ray diffraction (XRD), in situ X‐ray absorption spectroscopy (XAS), operando optoelectrochemistry, and operando energy dispersive X‐ray diffraction (EDXRD). In conventional concentration 3 m electrolytes, in situ XRD and XAS, as well as ex situ scanning transmission electron microscopy data collectively support Zn2+ as the primary charge carrier. In addition, these systems are susceptible to cathode dissolution, Zn corrosion coupled with the hydrogen evolution reaction, and the resultant formation of basic zinc salt phases. The multi‐modal in situ and operando experimental analyses validate facile H+ intercalation and extraction in concentrated 30 m ZnCl2 WIS electrolyte. Via operando EDXRD, reaction front and charge transport limitation during discharge and charge in the viscous WIS electrolyte are spatially tracked. This work provides new insight into the stability and degradation mechanisms of aqueous zinc batteries during static storage and upon dynamic cycling, and highlights the utility of in situ and operando techniques in understanding the superior stability of WIS electrolytes.

TopoTEM: A Python Package for Quantifying and Visualizing Scanning Transmission Electron Microscopy Data of Polar Topologies.

The exotic internal structure of polar topologies in multiferroic materials offers a rich landscape for materials science research. As the spatial scale of these entities is often subatomic in nature, aberration-corrected transmission electron microscopy (TEM) is the ideal characterization technique. Software to quantify and visualize the slight shifts in atomic placement within unit cells is of paramount importance due to the now routine acquisition of images at such resolution. In the previous ~decade since the commercialization of aberration-corrected TEM, many research groups have written their own code to visualize these polar entities. More recently, open-access Python packages have been developed for the purpose of TEM atomic position quantification. Building on these packages, we introduce the TEMUL Toolkit: a Python package for analysis and visualization of atomic resolution images. Here, we focus specifically on the TopoTEM module of the toolkit where we show an easy to follow, streamlined version of calculating the atomic displacements relative to the surrounding lattice and thus plotting polarization. We hope this toolkit will benefit the rapidly expanding field of topology-based nano-electronic and quantum materials research, and we invite the electron microscopy community to contribute to this open-access project.

Sparsity and Noise Effects on the Reconstruction of Subsampled Scanning Transmission Electron Microscopy Data

Sparsity and Noise Effects on the Reconstruction of Subsampled Scanning Transmission Electron Microscopy Data

Rapid and Flexible Few Shot Learning-Based Classification of Scanning Transmission Electron Microscopy Data

Rapid and Flexible Few Shot Learning-Based Classification of Scanning Transmission Electron Microscopy Data

Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data

Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy, with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models. However, the fundamental limitation of machine learning methods is their correlative nature, leading to extreme susceptibility to confounding factors. Here, we implement the workflow for causal analysis of structural scanning transmission electron microscopy (STEM) data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions. The combinatorial library of the Sm-doped BiFeO3 is grown to cover the composition range from pure ferroelectric BFO to orthorhombic 20% Sm-doped BFO. Atomically resolved STEM images are acquired for selected compositions and are used to create a set of local compositional, structural, and polarization field descriptors. The information-geometric causal inference (IGCI) and additive noise model (ANM) analysis are used to establish the pairwise causal directions between the descriptors, ordering the data set in the causal direction. The causal chain for IGCI and ANM across the composition is compared and suggests the presence of common causal mechanisms across the composition series. Ultimately, we believe that the causal analysis of the multimodal data will allow exploring the causal links between multiple competing mechanisms that control the emergence of unique functionalities of morphotropic materials and ferroelectric relaxors.

Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data

We explore the possibility for reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy data. Here, scanning transmission electron microscopy (STEM) is used to observe the dynamic motion of Si atoms at the edge of monolayer graphene under continuous electron beam illumination. The resulting time-lapsed STEM images represent the snapshots of observed chemical states of the system. We use two approaches: potential of mean force calculation using a radial distribution function and a direct fitting of the graphene–Si interatomic pairwise potentials with force matching, to reconstruct the force fields in the materials. These studies lay the foundation for quantitative analysis of materials energetics from STEM data through the sampling of the metastable states in the chemical space of the system.

Colloidal particle aggregation in three dimensions.

Colloidal systems are of importance not only for everyday products, but also for the development of new advanced materials. In many applications, it is crucial to understand and control colloidal interaction. In this paper, we study colloidal particle aggregation of silica nanoparticles, where the data are given in a three-dimensional micrograph obtained by high-angle annular dark field scanning transmission electron microscopy tomography. We investigate whether dynamic models for particle aggregation, namely the diffusion limited cluster aggregation and the reaction limited cluster aggregation models, can be used to construct structures present in the scanning transmission electron microscopy data. We compare the experimentally obtained silica aggregate to the simulated postaggregated structures obtained by the dynamic models. In addition, we fit static Gibbs point process models, which are commonly used models for point patterns with interactions, to the silica data. We were able to simulate structures similar to the silica structures by using Gibbs point process models. By fitting Gibbs models to the simulated cluster aggregation patterns, we saw that a smaller probability of aggregation would be needed to construct structures similar to the observed silica particlestructure.

StructOpt: A modular materials structure optimization suite incorporating experimental data and simulated energies

StructOpt, an open-source structure optimization suite, applies genetic algorithm and particle swarm methods to obtain atomic structures that minimize an objective function. The objective function typically consists of the energy and the error between simulated and experimental data, which is typically applied to determine structures that minimize energy to the extent possible while also being fully consistent with available experimental data. We present example use cases including the structure of a metastable Pt nanoparticle determined from energetic and scanning transmission electron microscopy data, and the structure of an amorphous-nanocrystal composite determined from energetic and fluctuation electron microscopy data. StructOpt is modular in its construction and therefore is naturally extensible to include new materials simulation modules or new optimization methods, either written by the user or existing in other code packages. It uses the Message Passing Interface’s (MPI) dynamic process management functionality to allocate resources to computationally expensive codes on the fly, enabling StructOpt to take full advantage of the parallelization tools available in many scientific packages.

Structure Retrieval at Atomic Resolution in the Presence of Multiple Scattering of the Electron Probe.

The projected electrostatic potential of a thick crystal is reconstructed at atomic resolution from experimental scanning transmission electron microscopy data recorded using a new generation fast-readout electron camera. This practical and deterministic inversion of the equations encapsulating multiple scattering that were written down by Bethe in 1928 removes the restriction of established methods to ultrathin (≲50 Å) samples. Instruments already coming on line can overcome the remaining resolution-limiting effects in this method due to finite probe-forming aperture size, spatial incoherence, and residual lens aberrations.

Nanoscopic analysis of oxygen segregation at tilt boundaries in silicon ingots using atom probe tomography combined with TEM and ab initio calculations.

We have developed an analytical method to determine the segregation levels on the same tilt boundaries (TBs) at the same nanoscopic location by a joint use of atom probe tomography and scanning transmission electron microscopy, and discussed the mechanism of oxygen segregation at TBs in silicon ingots in terms of bond distortions around the TBs. The three-dimensional distribution of oxygen atoms was determined at the typical small- and large-angle TBs by atom probe tomography with a low impurity detection limit (0.01 at.% on a TB plane) simultaneously with high spatial resolution (about 0.4 nm). The three-dimensional distribution was correlated with the atomic stress around the TBs; the stress at large-angle TBs was estimated by ab initio calculations based on atomic resolution scanning transmission electron microscopy data and that at small-angle TBs were calculated with the elastic theory based on dark-field transmission electron microscopy data. Oxygen atoms would segregate at bond-centred sites under tensile stress above about 2 GPa, so as to attain a more stable bonding network by reducing the local stress. The number of oxygen atoms segregating in a unit TB area NGB (in atoms nm-2 ) was determined to be proportional to both the number of the atomic sites under tensile stress in a unit TB area nbc and the average concentration of oxygen atoms around the TB [Oi ] (in at.%) with NGB ∼ 50 nbc [Oi ].

Kinetically Controlled Fabrication of Single-Crystalline TiO2 Nanobrush Architectures with High Energy {001} Facets.

This study demonstrates that precise control of nonequilibrium growth conditions during pulsed laser deposition (PLD) can be exploited to produce single‐crystalline anatase TiO2 nanobrush architectures with large surface areas terminated with high energy {001} facets. The data indicate that the key to nanobrush formation is controlling the atomic surface transport processes to balance defect aggregation and surface‐smoothing processes. High‐resolution scanning transmission electron microscopy data reveal that defect‐mediated aggregation is the key to TiO2 nanobrush formation. The large concentration of defects present at the intersection of domain boundaries promotes aggregation of PLD growth species, resulting in the growth of the single‐crystalline nanobrush architecture. This study proposes a model for the relationship between defect creation and growth mode in nonequilibrium environments, which enables application of this growth method to novel nanostructure design in a broad range of materials.

Denoising time-resolved microscopy image sequences with singular value thresholding

Time-resolved imaging in microscopy is important for the direct observation of a range of dynamic processes in both the physical and life sciences. However, the image sequences are often corrupted by noise, either as a result of high frame rates or a need to limit the radiation dose received by the sample. Here we exploit both spatial and temporal correlations using low-rank matrix recovery methods to denoise microscopy image sequences. We also make use of an unbiased risk estimator to address the issue of how much thresholding to apply in a robust and automated manner. The performance of the technique is demonstrated using simulated image sequences, as well as experimental scanning transmission electron microscopy data, where surface adatom motion and nanoparticle structural dynamics are recovered at rates of up to 32 frames per second.

Hanoi tower-like multilayered ultrathin palladium nanosheets.

This paper describes the synthesis, formation mechanism, and mechanical property of multilayered ultrathin Pd nanosheets. An anisotropic, Hanoi Tower-like assembly of Pd nanosheets was identified by transmission electron microscopy and atomic force microscopy (AFM). These nanosheets may contain ultrathin Pd layers, down to single unit cell thickness. Selected area electron diffraction and scanning transmission electron microscopy data show the interconnected atomically thick layers stacking vertically with rotational mismatches, resulting in unique diffractions and Moiré patterns. Density functional theory (DFT) calculation with van der Waals correction (DFT+vdW) shows the adsorption of Pd4(CO)4(OAc)4 on Pd(110) surface (Ead = -5.68 eV) is much stronger than that on Pd(100) (Ead = -4.72 eV) or on Pd(111) (Ead = -3.80 eV). The adsorption strength of this Pd complex is significantly stronger than that of CO on the same Pd surfaces. The DFT+vdW calculation results suggest a new mechanism for the observed anisotropic growth of nanosheets with unusually high aspect ratio, in which the competitive adsorptions between Pd4(CO)4(OAc)4 complex and CO on various surfaces result in a favored growth along the ⟨110⟩ directions and inhibition along ⟨111⟩ directions. The mechanical property of these multilayered Pd nanosheets was studied using AFM and nanoindentation techniques, which indicate multilayered nanosheets show more plastic deformation than the bulk in response to an applied force.

Atom probe tomography studies of Al2O3 gate dielectrics on GaN

Atom probe tomography was used to achieve three-dimensional characterization of in situ Al2O3/GaN structures grown by metal organic chemical vapor deposition (MOCVD). Al2O3 dielectrics grown at three different temperatures of 700, 900, and 1000 °C were analyzed and compared. A low temperature GaN cap layer grown atop Al2O3 enabled a high success rate in the atom probe experiments. The Al2O3/GaN interfaces were found to be intermixed with Ga, N, and O over the distance of a few nm. Impurity measurements data showed that the 1000 °C sample contains higher amounts of C (4 × 1019/cm3) and lower amounts of H (7 × 1019/cm3), whereas the 700 °C sample exhibits lower C impurities (<1017/cm3) and higher H incorporation (2.2 × 1020/cm3). On comparing with Al2O3 grown by atomic layer deposition (ALD), it was found that the MOCVD Al2O3/GaN interface is comparatively abrupt. Scanning transmission electron microscopy data showed that the 900 °C and 1000 °C MOCVD films exhibit polycrystalline nature, while the ALD films were found to be amorphous.

Engineered Porous Carbon for High Volumetric Methane Storage

This paper covers the optimization of methane volumetric storage capacity by controlling the sub-nanometre (<1 nm) and supra-nanometre (1-5 nm) pore volumes. Nanospace engineering of KOH activated carbon generates an ideal structure for methane storage in which gas molecules are adsorbed as a high-density fluid by strong van der Waals forces into pores that are a few molecules in diameter. High specific surface areas, porosities, sub-nanometre (<1 nm) and supra-nanometre (1-5 nm) pore volumes are quantitatively selected by controlling the degree of carbon consumption and metallic potassium intercalation into the carbon lattice during the activation process. The formation of tuneable sub-nanometre and supra-nanometre pores is validated by sub-critical nitrogen adsorption. Aberration-corrected scanning transmission electron microscopy data show the atomic structure of high-surface-area activated carbon (2600 m2/g). While high surface area and high porosity are optimal for gravimetric methane storage, the re...