Search

Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract: Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera. [ABSTRACT FROM AUTHOR]

Scanning transmission electron microscopy images can be complex to interpret on the atomic scale as the contrast is sensitive to multiple factors such as sample thickness, composition, defects and aberrations. Simulations are commonly used to validate or interpret real experimental images, but they come at a cost of either long computation times or specialist hardware such as graphics processing units. Recent works in compressive sensing for experimental STEM images have shown that it is possible to significantly reduce the amount of acquired signal and still recover the full image without significant loss of image quality, and therefore it is proposed here that similar methods can be applied to STEM simulations. In this paper, we demonstrate a method that can significantly increase the efficiency of STEM simulations through a targeted sampling strategy, along with a new approach to independently subsample each frozen phonon layer. We show the effectiveness of this method by simulating a SrTiO3 grain boundary and monolayer 2H‐MoS2 containing a sulphur vacancy using the abTEM software. We also show how this method is not limited to only traditional multislice methods, but also increases the speed of the PRISM simulation method. Furthermore, we discuss the possibility for STEM simulations to seed the acquisition of real data, to potentially lead the way to self‐driving (correcting) STEM. Lay Description: Scanning Transmission Electron Microscopy (STEM) simulation is used to validate contrast in experimental images through matching procedures. Parameter searches are performed over various combinations of aberrations and sample structures until an approximation is made with respect to the experimental image. The most common approach for calculating simulations is the multislice approach, where the sample potential is approximated as a series of two‐dimensional slices. The propagation of the beam between slices involves multiple Fast Fourier Transform calculations, which must be performed at all probe locations, hence it is computationally expensive. This means that STEM multislice simulations take a significant amount time to perform, even with specialist hardware such as graphical processing units for parallelisation of calculation. An alternative to this is a recently developed algorithm known as the plane‐wave reciprocal‐space interpolated scattering matrix (PRISM) method. PRISM uses the fact that the electron probe can also be expressed as a plane‐wave expansion, and a basis set of plane‐waves can be propagated through the sample (as in the multislice step) independently of one another. After the plane‐waves are propagated through the sample, they form what is known as a scattering matrix and can be superimposed with appropriate coefficients to approximate the exit wavefunction/probe. This makes PRISM significantly faster to compute exit probes, at a small expense of accuracy with respect to the ground truth multislice simulation. In this paper, we present a novel method for increasing STEM simulation computation time through targeted probe sub‐sampling. This method takes advantage of developments within the compressive sensing (CS) community, whereby a significant proportion of probe locations can be omitted during calculation, yet inpainted through a dictionary learning and sparse coding algorithms (such as Beta Process Factor Analysis, BPFA) with minimal loss of accuracy. The method is implemented with both the PRISM and multislice algorithms, and demonstrations of the method are applied to HAADF simulations of a low energy strontium titanate grain boundary, and the 2H phase of monolayer molybdenum disulfide with sulfur vacancies. The methods yield results which are functionally identical to the ground truth (fully sampled) multislice simulations, but can be performed up to 350× faster by using the PRISM method with sub‐sampling. Furthermore, we also demonstrate how STEM simulations can seed the recovery of experimental CS‐STEM data through (dictionary) transfer learning. This is applied to sub‐sampled HAADF images of yttrium silicide, and shows a 9% increase in PSNR versus only using BPFA. [ABSTRACT FROM AUTHOR]

Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance the stability, activity, and selectivity of heterogeneous catalysts. While charge transfer is tunable using the atomic structure and chemistry of the catalyst-support interface, direct experimental evidence is missing for three-dimensional catalyst nanoparticles, primarily due to the lack of a high-resolution method that can probe and correlate both the charge distribution and atomic structure of catalyst/support interfaces in these structures. We demonstrate a robust scanning transmission electron microscopy (STEM) method that simultaneously visualizes the atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts using a model Au-catalyst/SrTiO3-support system. Using this method, we further reveal the atomic-scale mechanisms responsible for the highly active perimeter sites and demonstrate that the charge transfer behavior can be readily controlled using post-synthesis treatments. This methodology provides a blueprint for better understanding the role of charge transfer in catalyst stability and performance and facilitates the future development of highly active advanced catalysts. Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance catalytic performance. Here the authors demonstrate a scanning transmission electron microscopy method to visualize atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts, shedding light on the atomic-scale mechanisms behind their highly active perimeter sites. [ABSTRACT FROM AUTHOR]

Forming an ultra‐thin, permeable encapsulation oxide‐support layer on a metal catalyst surface is considered an effective strategy for achieving a balance between high stability and high activity in heterogenous catalysts. The success of such a design relies not only on the thickness, ideally one to two atomic layers thick, but also on the morphology and chemistry of the encapsulation layer. Reliably identifying the presence and chemical nature of such a trace layer has been challenging. Electron energy‐loss spectroscopy (EELS) performed in a scanning transmission electron microscope (STEM), the primary technique utilized for such studies, is limited by a weak signal on overlayers when using conventional analysis methods, often leading to misinterpreted or missed information. Here, a robust, unsupervised machine learning data analysis method is developed to reveal trace encapsulation layers that are otherwise overlooked in STEM‐EELS datasets. This method provides a reliable tool for analyzing encapsulation of catalysts and is generally applicable to any spectroscopic analysis of materials and devices where revealing a trace signal and its spatial distribution is challenging. [ABSTRACT FROM AUTHOR]

Interfaces play a fundamental role in many areas of chemistry. However, their localized nature requires characterization techniques with high spatial resolution in order to fully understand their structure and properties. State‐of‐the‐art atomic resolution or in situ scanning transmission electron microscopy and electron energy‐loss spectroscopy are indispensable tools for characterizing the local structure and chemistry of materials with single‐atom resolution, but they are not able to measure many properties that dictate function, such as vibrational modes or charge transfer, and are limited to room‐temperature samples containing no liquids. Here, we outline emerging electron microscopy techniques that are allowing these limitations to be overcome and highlight several recent studies that were enabled by these techniques. We then provide a vision for how these techniques can be paired with each other and with in situ methods to deliver new insights into the static and dynamic behavior of functional interfaces. [ABSTRACT FROM AUTHOR]

Scanning transmission electron microscopy (STEM) has emerged as one of the foremost techniques to analyze materials at atomic resolution. However, two practical difficulties inherent to STEM imaging are: radiation damage imparted by the electron beam, which can potentially damage or otherwise modify the specimen and slow-scan image acquisition, which limits the ability to capture dynamic changes at high temporal resolution. Furthermore, due in part to scan flyback corrections, typical raster scan methods result in an uneven distribution of dose across the scanned area. A method to allow extremely fast scanning with a uniform residence time would enable imaging at low electron doses, ameliorating radiation damage and at the same time permitting image acquisition at higher frame-rates while maintaining atomic resolution. The practical complication is that rastering the STEM probe at higher speeds causes significant image distortions. Non-square scan patterns provide a solution to this dilemma and can be tailored for low dose imaging conditions. Here, we develop a method for imaging with alternative scan patterns and investigate their performance at very high scan speeds. A general analysis for spiral scanning is presented here for the following spiral scan functions: Archimedean, Fermat, and constant linear velocity spirals, which were tested for STEM imaging. The quality of spiral scan STEM images is generally comparable with STEM images from conventional raster scans, and the dose uniformity can be improved. [ABSTRACT FROM AUTHOR]

Li-ion-conducting solid electrolytes can simultaneously overcome two grand challenges for Li-ion batteries: the severe safety concerns that limit the large-scale application and the poor electrolyte stability that forbids the use of high-voltage cathodes. Nevertheless, the ionic conductivity of solid electrolytes is typically low, compromising the battery performances. Precisely determining the ionic transport mechanism(s) is a prerequisite for the rational design of highly conductive solid electrolytes. For decades, the research on this subject has primarily focused on the atomic and microscopic scales, where the main features of interest are unit cells and microstructures, respectively. Here, it is shown that the largely overlooked mesoscopic scale lying between these extremes could be the key to fast ionic conduction. In a prototype system, (Li0.33La0.56)TiO3, a mesoscopic framework is revealed for the first time by state-of-the-art scanning transmission electron microscopy. Corroborated by theoretical calculations and impedance measurements, it is demonstrated that such a unique configuration maximizes the number of percolation directions and thus most effectively improves the ionic conductivity. This discovery reconciles the long-standing structure-property inconsistency in (Li0.33La0.56)TiO3 and also identifies mesoscopic ordering as a promising general strategy for optimizing Li+ conduction. [ABSTRACT FROM AUTHOR]

Characterizing and controlling the interlayer orientations and stacking orders of two-dimensional (2D) bilayer crystals and van der Waals (vdW) heterostructures is crucial to optimize their electrical and optoelectronic properties. The four polymorphs of layered gallium selenide (GaSe) crystals that result from different layer stackings provide an ideal platform to study the stacking configurations in 2D bilayer crystals. Through a controllable vapor-phase deposition method, bilayer GaSe crystals were selectively grown and their two preferred 0° or 60° interlayer rotations were investigated. The commensurate stacking configurations (AA′ and AB stacking) in as-grown bilayer GaSe crystals are clearly observed at the atomic scale, and the Ga-terminated edge structure was identified using scanning transmission electron microscopy. Theoretical analysis reveals that the energies of the interlayer coupling are responsible for the preferred orientations among the bilayer GaSe crystals. [ABSTRACT FROM AUTHOR]

Machine Learning: Machine Learning Method Reveals Hidden Strong Metal-Support Interaction in Microscopy Datasets (Small Methods 5/2021) Catalysts, electron energy loss spectroscopy, encapsulation, machine learning, scanning transmission electron microscopy Keywords: catalysts; electron energy loss spectroscopy; encapsulation; machine learning; scanning transmission electron microscopy EN catalysts electron energy loss spectroscopy encapsulation machine learning scanning transmission electron microscopy 1 1 1 05/17/21 20210501 NES 210501 In article number 2100035, Ramakrishnan Kannan, Xiaoqing Pan, Miaofang Chi, and co-workers developed a robust, unsupervised machine learning data analysis method to reveal encapsulation of metal catalysts that are otherwise overlooked in microscopy datasets. [Extracted from the article]

Scanning transmission electron microscopy (STEM) excels in accessing atomic-scale structure and chemistry. Enhancing our ability to directly image the functionalities of local features in materials has become one of the most important topics in the future development of STEM. Recently, differential phase contrast (DPC) imaging has been utilized to map the internal electric and magnetic fields in materials from nanoscale features such as p-n junctions, skyrmions, and even from individual atoms. Here, we use an ultra-low noise SCMOS detector in as the diffraction plane camera to collect four-dimensional (4D) datasets. The high angular resolution, efficient high-SNR acquisition, and modifiability of the camera allow it to function as a universal detector, where STEM imaging configurations, such as DPC, bright field, annular bright field, and annular dark field can all be reconstructed from a single 4D dataset. By examining a distorted perovskite, DyScO3, which possesses projected lattice spacings as small as 0.83 Å, we demonstrate DPC spatial resolution almost reaching the information limit of a 100 keV electron beam. In addition, the perovskite has ordered O-coordinations with alternating octahedral tilts, which can be quantitatively measured with single degree accuracy by taking advantage of DPC’s sensitivity to light atoms. The results, acquired on a standard Ronchigram camera as opposed to a specialized DPC detector, open up new opportunities to understand and design functional materials and devices that involve lattice and charge coupling at nano- and atomic-scales. [ABSTRACT FROM AUTHOR]

• Three SrTiO 3 (STO) shapes were tested for isopropanol (IPA) conversion. • Reconstruction of STO depends on both the pretreatment temperature and the shape. • The step sites on STO are determined as the active centers for IPA conversion. • The STO facets control the degree of surface reconstruction and thus catalysis. Strontium titanate (SrTiO 3) is an extensively investigated perovskite for various applications due to its optical, electrical and chemical properties. To gain an in-depth understanding of the active sites involved in heterogeneous catalysis over the broadly used SrTiO 3 (STO), we studied a model reaction, isopropanol conversion, on three differently shape-controlled nanocrystals: cube, truncated cube and dodecahedra. SEM, XRD and XPS confirmed the morphology, phase and composition of STO shapes. Low energy ion scattering (LEIS) revealed the occurrence of surface reconstruction over STO shapes during O 2 pretreatment at different temperatures. Based on the catalytic activities, scanning transmission electron microscopy images and density functional theory calculations, the step sites on STO derived from surface reconstruction were proposed to be the active sites for isopropanol conversion. This was further confirmed by steady state isotopic kinetic analysis (SSITKA) which demonstrated similar intrinsic turnover frequencies (TOFs) for the differently reconstructed STO shapes. It is concluded that the crystal facets impose an indirect effect on the catalysis of STO via controlling the degrees of surface reconstruction: the less stable STO (1 1 0) facet (dodecahedra) leads to more step sites after reconstruction and hence higher overall reaction rate than the more stable (1 0 0) facet (cube). This work highlights the important interplay between the crystal facet and surface reconstruction in controlling the nature and density of active sties and thus catalysis over complex oxides. [ABSTRACT FROM AUTHOR]

Abstract: Catalysts for the dehydrogenation of light alkanes were prepared by dispersing Pt on the surface of a calcined hydrotalcite-like support containing indium, Mg(In)(Al)O. Upon reduction in H2 at temperatures above 673K, bimetallic particles of PtIn are observed by TEM, which have an average diameter of 1nm. Analysis of Pt LIII-edge extended X-ray absorption fine structure (EXAFS) data shows that the In content of the bimetallic particles increases with increasing bulk In/Pt ratio and reduction temperature. Pt LIII-edge X-ray absorption near edge structure (XANES) indicates that an increasing donation of electronic charge from In to Pt occurs with increasing In content in the PtIn particles. The activity and selectivity of the Pt/Mg(In)(Al)O catalysts for ethane and propane dehydrogenation reactions are strongly dependent on the bulk In/Pt ratio. For both reactants, maximum activity was achieved for a bulk In/Pt ratio of 0.48, and at this In/Pt ratio, the selectivity to alkene was nearly 100%. Coke deposition was observed after catalyst use for either ethane or propane dehydrogenation, and it was observed that the alloying of Pt with In greatly reduced the amount of coke deposited. Characterization of the deposit by Raman spectroscopy indicates that the coke is present as highly disordered graphite particles <30nm in diameter. While the amount of coke deposited during ethane and propane dehydrogenation are comparable, the effects on activity are dependent on reactant composition. Coke deposition had no effect on ethane dehydrogenation activity, but caused a loss in propane dehydrogenation activity. This difference is attributed to the greater ease with which coke produced on the surface of PtIn nanoparticles migrates to the support during ethane dehydrogenation versus propane dehydrogenation. [Copyright &y& Elsevier]