Ultra-fast Digital DPC Yielding High Spatio-temporal Resolution for Low-Dose Phase Characterization.

In the scanning transmission electron microscope, both phase imaging of beam-sensitive materials and characterization of a material's functional properties using in situ experiments are becoming more widely available. As the practicable scan speed of 4D-STEM detectors improves, so too does the temporal resolution achievable for both differential phase contrast (DPC) and ptychography. However, the read-out burden of pixelated detectors, and the size of the gigabyte to terabyte sized data sets, remain a challenge for both temporal resolution and their practical adoption. In this work, we combine ultra-fast scan coils and detector signal digitization to show that a high-fidelity DPC phase reconstruction can be achieved from an annular segmented detector. Unlike conventional analog data phase reconstructions from digitized DPC-segment images yield reliable data, even at the fastest scan speeds. Finally, dose fractionation by fast scanning and multi-framing allows for postprocess binning of frame streams to balance signal-to-noise ratio and temporal resolution for low-dose phase imaging for in situ experiments.

Video frame interpolation neural network for 3D tomography across different length scales.

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

On the temporal transfer function in STEM imaging from finite detector response time.

Faster scanning in scanning transmission electron microscopy has long been desired for the ability to better control dose, minimise effects of environmental distortions, and to capture the dynamics of in-situ experiments. Advances in scan controllers and scan deflection systems have enabled scanning with pixel dwell times on the order of tens of nanoseconds. At these speeds, the finite response time of the electron detector must be considered as the signal from one electron detection event can contribute to multiple pixels, blurring the features within the image. Here we introduce a temporal transfer function (TTF) to describe and model the effects of detector response time on imaging, as well as a framework for incorporating these effects into simulation.

Event-responsive scanning transmission electron microscopy.

An ever-present limitation of transmission electron microscopy is the damage caused by high-energy electrons interacting with any sample. By reconsidering the fundamentals of imaging, we demonstrate an event-responsive approach to electron microscopy that delivers more information about the sample for a given beam current. Measuring the time to achieve an electron count threshold rather than waiting a predefined constant time improves the information obtained per electron. The microscope was made to respond to these events by blanking the beam, thus reducing the overall dose required. This approach automatically apportions dose to achieve a given signal-to-noise ratio in each pixel, eliminating excess dose that is associated with diminishing returns of information. We demonstrate the wide applicability of our approach to beam-sensitive materials by imaging biological tissue and zeolite.

Liquid-Phase Exfoliation of Arsenic Trisulfide (As2S3) Nanosheets and Their Use as Anodes in Potassium-Ion Batteries.

Here, we demonstrate the production of 2D nanosheets of arsenic disulfide (As2S3) via liquid-phase exfoliation of the naturally occurring mineral, orpiment. The resultant nanosheets had mean lateral dimensions and thicknesses of 400 and 10 nm, and had structures indistinguishable from the bulk. The nanosheets were solution mixed with carbon nanotubes and cast into nanocomposite films for use as anodes in potassium-ion batteries. These anodes exhibited outstanding electrochemical performance, demonstrating an impressive discharge capacity of 619 mAh/g at a current density of 50 mA/g. Even after 1000 cycles at 500 mA/g, the anodes retained an impressive 94% of their capacity. Quantitative analysis of the rate performance yielded a capacity at a very low rate of 838 mAh/g, about two-thirds of the theoretical capacity of As2S3 (1305 mAh/g). However, this analysis also implied As2S3 to have a very small solid-state diffusion coefficient (∼10-17 m2/s), somewhat limiting its potential for high-rate applications.

New Poisson denoising method for pulse-count STEM imaging.

With the recent progress in the development of detectors in electron microscopy, it has become possible to directly count the number of electrons per pixel, even with a scintillator-type detector, by incorporating a pulse-counting module. To optimize a denoising method for electron counting imaging, in this study, we propose a Poisson denoising method for atomic-resolution scanning transmission electron microscopy images. Our method is based on the Markov random field model and Bayesian inference, and we can reduce the electron dose by a factor of about 15 times or further below. Moreover, we showed that the method of reconstruction from multiple images without integrating them performs better than that from an integrated image.

Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography.

Networks of solution-processed nanomaterials are becoming increasingly important across applications in electronics, sensing and energy storage/generation. Although the physical properties of these devices are often completely dominated by network morphology, the network structure itself remains difficult to interrogate. Here, we utilise focused ion beam - scanning electron microscopy nanotomography (FIB-SEM-NT) to quantitatively characterise the morphology of printed nanostructured networks and their devices using nanometre-resolution 3D images. The influence of nanosheet/nanowire size on network structure in printed films of graphene, WS2 and silver nanosheets (AgNSs), as well as networks of silver nanowires (AgNWs), is investigated. We present a comprehensive toolkit to extract morphological characteristics including network porosity, tortuosity, specific surface area, pore dimensions and nanosheet orientation, which we link to network resistivity. By extending this technique to interrogate the structure and interfaces within printed vertical heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation.

Electron counting detectors in scanning transmission electron microscopy via hardware signal processing.

Transmission electron microscopy is a pivotal instrument in materials and biological sciences due to its ability to provide local structural and spectroscopic information on a wide range of materials. However, the electron detectors used in scanning transmission electron microscopy are often unable to provide quantified information, that is the number of electrons impacting the detector, without exhaustive calibration and processing. This results in arbitrary signal values with slow response times that cannot be used for quantification or comparison to simulations. Here we demonstrate and optimise a hardware signal processing approach to augment electron detectors to perform single electron counting.

Interlacing in Atomic Resolution Scanning Transmission Electron Microscopy.

Fast frame rates are desirable in scanning transmission electron microscopy for a number of reasons: controlling electron beam dose, capturing in situ events, or reducing the appearance of scan distortions. While several strategies exist for increasing frame rates, many impact image quality or require investment in advanced scan hardware. Here, we present an interlaced imaging approach to achieve minimal loss of image quality with faster frame rates that can be implemented on many existing scan controllers. We further demonstrate that our interlacing approach provides the best possible strain precision for a given electron dose compared with other contemporary approaches.

How Fast is Your Detector? The Effect of Temporal Response on Image Quality.

With increasing interest in high-speed imaging, there should be an increased interest in the response times of our scanning transmission electron microscope detectors. Previous works have highlighted and contrasted the performance of various detectors for quantitative compositional or structural studies, but here, we shift the focus to detector temporal response, and the effect this has on captured images. The rise and decay times of eight detectors' single-electron response are reported, as well as measurements of their flatness, roundness, smoothness, and ellipticity. We develop and apply a methodology for incorporating the temporal detector response into simulations, showing that a loss of resolution is apparent in both the images and their Fourier transforms. We conclude that the solid-state detector outperforms the photomultiplier tube-based detectors in all areas bar a slightly less elliptical central hole and is likely the best detector to use for the majority of applications. However, using the tools introduced here, we encourage users to effectively evaluate which detector is most suitable for their experimental needs.

A Retrofittable Photoelectron Gun for Low-Voltage Imaging Applications in the Scanning Electron Microscope.

Low-voltage scanning electron microscopy is a powerful tool for examining surface features and imaging beam-sensitive materials. Improving resolution during low-voltage imaging is then an important area of development. Decreasing the effect of chromatic aberration is one solution to improving the resolution and can be achieved by reducing the energy spread of the electron source. Our approach involves retrofitting a light source onto a thermionic lanthanum hexaboride (LaB6) electron gun as a cost-effective low energy-spread photoelectron emitter. The energy spread of the emitter's photoelectrons is theorized to be between 0.11 and 0.38 eV, depending on the photon energy of the ultraviolet (UV) light source. Proof-of-principle images have been recorded using this retrofitted photoelectron gun, and an analysis of its performance is presented.

Correcting for probe wandering by precession path segmentation.

Precession electron diffraction has in the past few decades become a powerful technique for structure solving, strain analysis, and orientation mapping, to name a few. One of the benefits of precessing the electron beam, is increased reciprocal space resolution, albeit at a loss of spatial resolution due to an effect referred to as 'probe wandering'. Here, a new methodology of precession path segmentation is presented to counteract this effect and increase the resolution in reconstructed virtual images from scanning precession electron diffraction data. By utilizing fast pixelated electron detector technology, multiple frames are recorded for each azimuthal rotation of the beam, allowing for the probe wandering to be corrected in post-acquisition processing. Not only is there an apparent increase in the resolution of the reconstructed images, but probe wandering due to instrument misalignment is reduced, potentially easing an already difficult alignment procedure.

Templated Synthesis of SiO2 Nanotubes for Lithium-Ion Battery Applications: An In Situ (Scanning) Transmission Electron Microscopy Study.

One of the weaknesses of silicon-based batteries is the rapid deterioration of the charge-storage capacity with increasing cycle numbers. Pure silicon anodes tend to suffer from poor cycling ability due to the pulverization of the crystal structure after repeated charge and discharge cycles. In this work, we present the synthesis of a hollow nanostructured SiO2 material for lithium-ion anode applications to counter this drawback. To improve the understanding of the synthesis route, the crucial synthesis step of removing the ZnO template core is shown using an in situ closed gas-cell sample holder for transmission electron microscopy. A direct visual observation of the removal of the ZnO template from the SiO2 shell is yet to be reported in the literature and is a critical step in understanding the mechanism by which these hollow nanostructures form from their core-shell precursors for future electrode material design. Using this unique technique, observation of dynamic phenomena at the individual particle scale is possible with simultaneous heating in a reactive gas environment. The electrochemical benefits of the hollow morphology are demonstrated with exceptional cycling performance, with capacity increasing with subsequent charge-discharge cycles. This demonstrates the criticality of nanostructured battery materials for the development of next-generation Li+-ion batteries.

Intervention to Improve Compliance With National Guidelines on Venous Thromboembolism Chemoprophylaxis for Patients With Operatively Managed Ankle Fractures.

Background: Trauma and subsequent immobilization of the lower limb increase the risk of venous thromboembolism (VTE). Our aim was to evaluate compliance with national guidance on operatively managed ankle fractures and VTE chemoprophylaxis before and after implementation of a change in practice. Methods: We conducted an initial single-center audit of patients undergoing ankle fracture fixation. The primary outcome was quality of operation note documentation, and the secondary outcome was whether VTE chemoprophylaxis was prescribed on discharge. All stakeholders were educated on audit findings, new guidelines were synthesized, and the practice was re-audited. Results: A total of 137 patients were included in the initial audit, and 49 patients were included in the loop closure. The first audit highlighted that chemoprophylaxis prescription on discharge was significantly higher when both the agent and treatment duration were clearly stipulated in the operation note compared to when either treatment duration or both agent and treatment duration were omitted (97.2% vs 51.8% and 32.4%, respectively, P<0.001). Following our intervention, operation note documentation of agent and treatment duration improved from 29% to 90% (P<0.001). VTE chemoprophylaxis on discharge significantly improved from 57% to 98% (P<0.001). Conclusion: Our closed-loop audit identified suboptimal operation note documentation as the root cause of VTE noncompliance. The operation note is an important clinical interface between the operating theater and ward staff. We addressed these deficiencies with a basic intervention.

Cost and Capability Compromises in STEM Instrumentation for Low-Voltage Imaging.

Low-voltage transmission electron microscopy (≤80 kV) has many applications in imaging beam-sensitive samples, such as metallic nanoparticles, which may become damaged at higher voltages. To improve resolution, spherical aberration can be corrected for in a scanning transmission electron microscope (STEM); however, chromatic aberration may then dominate, limiting the ultimate resolution of the microscope. Using image simulations, we examine how a chromatic aberration corrector, different objective lenses, and different beam energy spreads each affect the image quality of a gold nanoparticle imaged at low voltages in a spherical aberration-corrected STEM. A quantitative analysis of the simulated examples can inform the choice of instrumentation for low-voltage imaging. We here demonstrate a methodology whereby the optimum energy spread to operate a specific STEM can be deduced. This methodology can then be adapted to the specific sample and instrument of the reader, enabling them to make an informed economical choice as to what would be most beneficial for their STEM in the cost-conscious landscape of scientific infrastructure.

Prismatic 2.0 - Simulation software for scanning and high resolution transmission electron microscopy (STEM and HRTEM).

Scanning transmission electron microscopy (STEM), where a converged electron probe is scanned over a sample's surface and an imaging, diffraction, or spectroscopic signal is measured as a function of probe position, is an extremely powerful tool for materials characterization. The widespread adoption of hardware aberration correction, direct electron detectors, and computational imaging methods have made STEM one of the most important tools for atomic-resolution materials science. Many of these imaging methods rely on accurate imaging and diffraction simulations in order to interpret experimental results. However, STEM simulations have traditionally required large calculation times, as modeling the electron scattering requires a separate simulation for each of the typically millions of probe positions. We have created the Prismatic simulation code for fast simulation of STEM experiments with support for multi-CPU and multi-GPU (graphics processing unit) systems, using both the conventional multislice and our recently-introduced PRISM method. In this paper, we introduce Prismatic version 2.0, which adds many new algorithmic improvements, an updated graphical user interface (GUI), post-processing of simulation data, and additional operating modes such as plane-wave TEM. We review various aspects of the simulation methods and codes in detail and provide various simulation examples. Prismatic 2.0 is freely available both as an open-source package that can be run using a C++ or Python command line interface, or GUI, as well within a Docker container environment.