Liquid Phase Electron Microscopy of Bacterial Ultrastructure.

Recent advances in liquid phase scanning transmission electron microscopy (LP-STEM) have enabled the study of dynamic biological processes at nanometer resolutions, paving the way for live-cell imaging using electron microscopy. However, this technique is often hampered by the inherent thickness of whole cell samples and damage from electron beam irradiation. These restrictions degrade image quality and resolution, impeding biological interpretation. Using graphene encapsulation, scanning transmission electron microscopy (STEM), and energy-dispersive X-ray (EDX) spectroscopy to mitigate these issues provides unprecedented levels of intracellular detail in aqueous specimens. This study demonstrates the potential of LP-STEM to examine and identify internal cellular structures in thick biological samples. Specifically, it highlights the use of LP-STEM to investigate the radiation resistant, gram-positive bacterium, Deinococcus radiodurans using various imaging techniques.

Diffusion distribution model for damage mitigation in scanning transmission electron microscopy.

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modelled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage is minimised.

High-speed 4-dimensional scanning transmission electron microscopy using compressive sensing techniques.

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.

The Potential of Subsampling and Inpainting for Fast Low-Dose Cryo FIB-SEM Imaging.

Traditional image acquisition for cryo focused ion-beam scanning electron microscopy (FIB-SEM) tomography often sees thousands of images being captured over a period of many hours, with immense data sets being produced. When imaging beam sensitive materials, these images are often compromised by additional constraints related to beam damage and the devitrification of the material during imaging, which renders data acquisition both costly and unreliable. Subsampling and inpainting are proposed as solutions for both of these aspects, allowing fast and low-dose imaging to take place in the Focused ion-beam scanning electron microscopy FIB-SEM without an appreciable loss in image quality. In this work, experimental data are presented which validate subsampling and inpainting as a useful tool for convenient and reliable data acquisition in a FIB-SEM, with new methods of handling three-dimensional data being employed in the context of dictionary learning and inpainting algorithms using a newly developed microscope control software and data recovery algorithm.

Mapping Nanoscale Electrostatic Field Fluctuations around Graphene Dislocation Cores Using Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM).

Defects in crystalline lattices cause modulation of the atomic density, and this leads to variations in the associated electrostatics at the nanoscale. Mapping these spatially varying charge fluctuations using transmission electron microscopy has typically been challenging due to complicated contrast transfer inherent to conventional phase contrast imaging. To overcome this, we used four-dimensional scanning transmission electron microscopy (4D-STEM) to measure electrostatic fields near point dislocations in a monolayer. The asymmetry of the atomic density in a (1,0) edge dislocation core in graphene yields a local enhancement of the electric field in part of the dislocation core. Through experiment and simulation, the increased electric field magnitude is shown to arise from "long-range" interactions from beyond the nearest atomic neighbor. These results provide insights into the use of 4D-STEM to quantify electrostatics in thin materials and map out the lateral potential variations that are important for molecular and atomic bonding through Coulombic interactions.

Towards real-time STEM simulations through targeted subsampling strategies.

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.

Direct Visualisation of the Surface Atomic Active Sites of Carbon-Supported Co3 O4 Nanocrystals via High-Resolution Phase Restoration.

The atomic arrangement of the terminating facets on spinel Co3 O4 nanocrystals is strongly linked to their catalytic performance. However, the spinel crystal structure offers multiple possible surface terminations depending on the synthesis. Thus, understanding the terminating surface atomic structure is essential in developing high-performance Co3 O4 nanocrystals. In this work, we present direct atomic-scale observation of the surface terminations of Co3 O4 nanoparticles supported on hollow carbon spheres (HCSs) using exit wavefunction reconstruction from aberration-corrected transmission electron microscopy focal-series. The restored high-resolution phases show distinct resolved oxygen and cobalt atomic columns. The data show that the structure of {100}, {110}, and {111} facets of spinel Co3 O4 exhibit characteristic active sites for carbon monoxide (CO) adsorption, in agreement with density functional theory calculations. Of these facets, the {100} and {110} surface terminations are better suited for CO adsorption than the {111}. However, the presence of oxygen on the {111} surface termination indicates this facet also plays an essential role in CO adsorption. Our results demonstrate direct evidence of the surface termination atomic structure beyond the assumed stoichiometry of the surface.

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

Measurement of the size, shape and composition of nanoparticles is routinely performed using transmission electron microscopy and related techniques. Typically, distinguishing particles from the background in an image is performed using the intensity of each pixel, creating two sets of pixels to separate particles from background. However, this separation of intensity can be difficult if the contrast in an image is low, or if the intensity of the background varies significantly. In this study, an approach that takes into account additional image features (such as boundaries and texture) was investigated to study electron microscope images of metallic nanoparticles. In this 'trainable segmentation' approach, the user labels examples of particle and background pixels in order to train a machine learning algorithm to distinguish between particles and background. The performance of different machine learning algorithms was investigated, in addition to the effect of using different features to aid the segmentation. Overall, a trainable segmentation approach was found to perform better than use of an intensity threshold to distinguish between particles and background in electron microscope images.

Oleylamine Aging of PtNi Nanoparticles Giving Enhanced Functionality for the Oxygen Reduction Reaction.

We report a rapid solution-phase strategy to synthesize alloyed PtNi nanoparticles which demonstrate outstanding functionality for the oxygen reduction reaction (ORR). This one-pot coreduction colloidal synthesis results in a monodisperse population of single-crystal nanoparticles of rhombic dodecahedral morphology with Pt-enriched edges and compositions close to Pt1Ni2. We use nanoscale 3D compositional analysis to reveal for the first time that oleylamine (OAm)-aging of the rhombic dodecahedral Pt1Ni2 particles results in Ni leaching from surface facets, producing aged particles with concave faceting, an exceptionally high surface area, and a composition of Pt2Ni1. We show that the modified atomic nanostructures catalytically outperform the original PtNi rhombic dodecahedral particles by more than two-fold and also yield improved cycling durability. Their functionality for the ORR far exceeds commercially available Pt/C nanoparticle electrocatalysts, both in terms of mass-specific activities (up to a 25-fold increase) and intrinsic area-specific activities (up to a 27-fold increase).

Simultaneous Identification of Low and High Atomic Number Atoms in Monolayer 2D Materials Using 4D Scanning Transmission Electron Microscopy.

Simultaneous imaging of individual low and high atomic number atoms using annular dark field scanning transmission electron microscopy (ADF-STEM) is often challenging due to substantial differences in their scattering cross sections. This often leads to contrast from only the high atomic number species when imaged using ADF-STEM such as the Mo and 2S sites in monolayer MoS2 crystals, without detection of lighter atoms such as C, O, or N. Here, we show that by capturing an array of convergent beam electron diffraction patterns using a 2D pixelated electron detector (2D-PED) in a 4D STEM geometry enables identification of individual low and high atomic number atoms in 2D materials by multicomponent imaging. We have used ptychographic phase reconstructions, combined with angular dependent ADF-STEM reconstructions, to image light elements at lateral (nanopores) and vertical interfaces (surface dopants) within 2D monolayer MoS2. Differential phase contrast imaging (Div(DPC)) using quadrant segmentation of the 2D pixelated direct electron detector data not only qualitatively matches the ptychographic phase reconstructions in both resolution and contrast but also offers the additional potential for real time display. Using 4D-STEM, we have identified surface adatoms on MoS2 monolayers and have separated atomic columns with similar total atomic number into their relative combinations of low and high atomic number elements. These results demonstrate the rich information present in the data obtained during 4D-STEM imaging of ultrathin 2D materials and the ability of this approach to extract unique insights beyond conventional imaging.

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.

Entrapped Single Tungstate Site in Zeolite for Cooperative Catalysis of Olefin Metathesis with Brønsted Acid Site.

Industrial olefin metathesis catalysts generally suffer from low reaction rates and require harsh reaction conditions for moderate activities. This is due to their inability to prevent metathesis active sites (MASs) from aggregation and their intrinsic poor adsorption and activation of olefin molecules. Here, isolated tungstate species as single molecular MASs are immobilized inside zeolite pores by Brønsted acid sites (BASs) on the inner surface. It is demonstrated that unoccupied BASs in atomic proximity to MASs enhance olefin adsorption and facilitate the formation of metallocycle intermediates in a stereospecific manner. Thus, effective cooperative catalysis takes place over the BAS-MAS pair inside the zeolite cavity. In consequence, for the cross-metathesis of ethene and trans-2-butene to propene, under mild reaction conditions, the propene production rate over WO x/USY is ca. 7300 times that over the industrial WO3/SiO2-based catalyst. A propene yield up to 79% (80% selectivity) without observable deactivation was obtained over WO x/USY for a wide range of reaction conditions.

Hollow Electron Ptychographic Diffractive Imaging.

We report a method for quantitative phase recovery and simultaneous electron energy loss spectroscopy analysis using ptychographic reconstruction of a data set of "hollow" diffraction patterns. This has the potential for recovering both structural and chemical information at atomic resolution with a new generation of detectors.

Atomically Flat Zigzag Edges in Monolayer MoS2 by Thermal Annealing.

The edges of 2D materials show novel electronic, magnetic, and optical properties, especially when reduced to nanoribbon widths. Therefore, methods to create atomically flat edges in 2D materials are essential for future exploitation. Atomically flat edges in 2D materials are found after brittle fracture or when electrically biasing, but a simple scalable approach for creating atomically flat periodic edges in monolayer 2D transition metal dichalcogenides has yet to be realized. Here, we show how heating monolayer MoS2 to 800 °C in vacuum produces atomically flat Mo terminated zigzag edges in nanoribbons. We study this at the atomic level using an ultrastable in situ heating holder in an aberration-corrected transmission electron microscope and discriminating Mo from S at the edge, revealing unique Mo terminations for all zigzag orientations that remain stable and atomically flat when cooling back to room temperature. Highly faceted MoS2 nanoribbon constrictions are produced with Mo rich edge structures that have theoretically predicted spin separated transport channels, which are promising for spin logic applications.

One-Pot Synthesis of Lithium-Rich Cathode Material with Hierarchical Morphology.

Lithium-rich transition metal oxides, Li1+xTM1-xO2 (TM, transition metal), have attracted much attention as potential candidate cathode materials for next generation lithium ion batteries because their high theoretical capacity. Here we present the synthesis of Li[Li0.2Ni0.2Mn0.6]O2 using a facile one-pot resorcinol-formaldehyde method. Structural characterization indicates that the material adopts a hierarchical porous morphology consisting of uniformly distributed small pores and disordered large pore structures. The material exhibits excellent electrochemical cycling stability and a good retention of capacity at high rates. The material has been shown to be both advantageous in terms of gravimetric and volumetric capacities over state of the art commercial cathode materials.