Ultrafast Electron Microscopy Module to Upgrade Existing Commercial Electron Microscopes (Final Report)
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Here we use low-dose cryogenic transmission electron microscopy (cryo-TEM) to investigate the atomic-scale structure of antiperovskite Na 2 NH 2 BH 4 crystals by preserving the room-temperature cubic phase and carefully monitoring the electron dose. Via quantitative analysis of electron beam damage using selected area electron diffraction, we find cryogenic imaging provides 6-fold improvement in beam stability for this solid electrolyte. Cryo-TEM images obtained from flat crystals revealed the presence of a new, long-range-ordered supercell with a cubic phase. The supercell exhibits doubled unit cell dimensions of 9.4 Å × 9.4 Å as compared to the cubic lattice structure revealed by X-ray crystallography of 4.7 Å × 4.7 Å. The comparison between the experimental image and simulated potential map indicates the origin of the supercell is a vacancy ordering of sodium atoms. This work demonstrates the potential of using cryo-TEM imaging to study the atomic-scale structure of air- and electron-beam-sensitive antiperovskite-type solid electrolytes.
Abstract As a discipline, structural biology has been transformed by the three-dimensional electron microscopy (3DEM) “Resolution Revolution” made possible by convergence of robust cryo-preservation of vitrified biological materials, sample handling systems, and measurement stages operating a liquid nitrogen temperature, improvements in electron optics that preserve phase information at the atomic level, direct electron detectors (DEDs), high-speed computing with graphics processing units, and rapid advances in data acquisition and processing software. 3DEM structure information (atomic coordinates and related metadata) are archived in the open-access Protein Data Bank (PDB), which currently holds more than 11,000 3DEM structures of proteins and nucleic acids, and their complexes with one another and small-molecule ligands (~ 6% of the archive). Underlying experimental data (3DEM density maps and related metadata) are stored in the Electron Microscopy Data Bank (EMDB), which currently holds more than 21,000 3DEM density maps. After describing the history of the PDB and the Worldwide Protein Data Bank (wwPDB) partnership, which jointly manages both the PDB and EMDB archives, this review examines the origins of the resolution revolution and analyzes its impact on structural biology viewed through the lens of PDB holdings. Six areas of focus exemplifying the impact of 3DEM across the biosciences are discussed in detail (icosahedral viruses, ribosomes, integral membrane proteins, SARS-CoV-2 spike proteins, cryogenic electron tomography, and integrative structure determination combining 3DEM with complementary biophysical measurement techniques), followed by a review of 3DEM structure validation by the wwPDB that underscores the importance of community engagement.
When high-energy electrons from scanning transmission electron microscope (STEM) are interacting with the liquid, the vast majority of the chemical reactions that are observed are induced by the radiolysis breakdown of the liquid molecules.
Here, we first review the significance of resolution and contrast in electron microscopy, and the effect of the electron optics on these two quantities. We then outline the physics of the generation of secondary electrons (SE) and their transport and emission from the surface of a specimen. Contrast and resolution are discussed for different kinds of SE imaging in scanning (SEM) and scanning-transmission (STEM) instruments, with some emphasis on the observation of individual atoms and atomic columns in a thin specimen. The possibility of achieving atomic resolution from a bulk specimen at SEM energies is also considered.
The metallic U-Zr nuclear fuel alloy has garnered renewed interest as a promising candidate for next-generation sodium-cooled fast reactors. Recent studies and technology assessments have identified several areas requiring improvements, enhanced knowledge, and reliable data to strengthen the U-Zr fuel design basis for qualification and commercial applications. One of the most challenging phenomena impacting this fuel system’s performance is fuel-cladding chemical interaction (FCCI). This work aimed to harvest FCCI data by examining selected HT9/U-10Zr (wt. %) fuel samples of prototypic full-length fuel pins through an integrated approach. This approach integrated scanning electron microscopy (SEM) microstructure characterization with localized mechanical properties examination to deepen understanding of FCCI phenomenon in HT9/U-10Zr fuel system. Particularly, this study focused on MFF fuel pins irradiated at Fast Flux Test Facility (FFTF), which aimed to qualify metallic fuel as a driver fuel for FFTF and to assess its viability for larger-scale fast reactors. Electron microscopy provided high confidence in detecting and distinguishing the different FCCI layers, while small-scale mechanical testing (SSMT) probed the mechanical properties of these layers. SEM examination of a MFF-2 pin 192167, with a time averaged inner cladding temperature (TICT) slightly over 500°C, revealed minimal cladding-side FCCI (cladding wastage). In contrast, significantly thicker cladding wastage comprising two distinct sublayers was observed in samples from the thermally hot MFF-3 pin 193045 and MFF-5 pin 195011 where the TICT ranged from 610-635°C. SSMT indicated complete embrittlement in the sublayer adjacent to the fuel and a tendency toward embrittlement in the other sublayer. Additionally, a new machine learning method was developed, validated, and used to quantify cladding wastage thickness. The machine learning method reliably predicted the wastage thickness across various fuel pins and sample cross-sections. Furthermore, the available cladding wastage data from HT9/U-10Zr fuel system demonstrated a strong temperature dependency. However, the dataset remains small, and ongoing research activities are essential to further understand the FCCI phenomenon and develop a reliable FCCI model for enhanced fuel performance simulation under various conditions.
Abstract The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.
Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.
Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.
The 1144 phase (Ae1A1Fe4As4) shows a strong advantage of engineering fabrication among Fe Iron)-based superconductor (FBS) family due to the robustness of its superconducting properties with respect to chemical inhomogeneities, granted by its uniform crystalline-layered structure. This regularity is furthermore associated to crystalline defects capable of acting as efficient pinning centers, which high critical currents achieved at high fields for these superconductors. Like other FBS phases, its lossless current-carrying capability can be remarkably degraded by distractions at grain boundaries (GBs). GB oxidation is an issue of upmost importance to the realization of the practical FBS application for high field (> 20T) magnet. In this study, we explore oxidized grain boundary and intrinsic grain structural properties of 1144 polycrystalline samples by applying analytical electron microscopy such as atomic resolution scanning transmission electron microscopy and atom probe tomography. These structural properties of samples produced by a mechanochemically assisted synthesis are evaluated following the degradation of superconducting properties due to oxidation. We observe a strong correlation between the contamination at grain boundaries and the decrease of transport properties of the bulk sample, while the crystallin structure seems to be not affected by the oxidation.
This article addresses recent advances in liquid phase transmission electron microscopy (LPTEM) for studying nanoscale synthetic processes of carbon-based materials that are independent of the electron beam—those driven by nonradiolytic chemical or thermal reactions. In particular, we focus on chemical/physical formations and the assembly of nanostructures composed of organic monomers/polymers, peptides/DNA, and biominerals. The synthesis of carbon-based nanomaterials generally only occurs at specific conditions, which cannot be mimicked by aqueous solution radiolysis. Carbon-based structures themselves are also acutely sensitive to the damaging effects of the irradiating beam, which make studying their synthesis using LPTEM a unique challenge that is possible when beam effects can be quantifi ed and mitigated. With new direct sensing, high frame rate cameras, and advances in liquid-cell holder designs, combined with a growing understanding of irradiation effects and proper experimental controls, microscopists have been able to make strides in observing traditionally problematic carbon-based materials under conditions where synthesis can be controlled, and imaged free from beam effects, or with beam effects quantified and accounted for. These materials systems and LPTEM experimental techniques are discussed, focusing on nonradiolytic chemical and physical transformations relevant to materials synthesis.
Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic interactions. Here, fused multi-modal electron microscopy offers high signal-to-noise ratio (SNR) recovery of material chemistry at nano- and atomic-resolution by coupling correlated information encoded within both elastic scattering (high-angle annular dark-field (HAADF)) and inelastic spectroscopic signals (electron energy loss (EELS) or energy-dispersive x-ray (EDX)). By linking these simultaneously acquired signals, or modalities, the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector hardware. In many cases, the dose requirements can be reduced by over one order of magnitude. This high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.
Organic–inorganic perovskites are an emerging class of photovoltaic materials. Despite achieving power conversion efficiencies surpassing 26%, the challenge of perovskite stability including degradation during exposure to operational conditions such as light, heat, humidity, water, oxygen, and electric fields is well known. Related, perovskite instability has limited high-resolution electron imaging and characterization techniques that can be used for understanding degradation mechanisms. Furthermore, we demonstrate perovskite device cross-section preparation using mechanical polishing in a water-free environment with cryogenic Ar ion milling. Scanning electron microscopy was then used in both backscattered electron and secondary electron imaging modes to obtain information about layer structure, grain aggregate structure, and compositional heterogeneity. Monte Carlo CASINO simulations inform optimum beam conditions and image acquisition parameters and the effects of accelerating voltage, dwell times, and frame averaging for practical image acquisition are reported.
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Considerable inroads have recently been made on algorithms to determine the sample potential from four-dimensional scanning transmission electron microscopy data from thick samples where multiple scattering cannot be neglected. This paper further develops the scattering matrix approach to such structure determination. Through simulation, we demonstrate how this approach can be modified to better handle partial spatial coherence, unknown probe defocus, and information from the dark field region. By combining these developments we reconstruct the electrostatic potential of a monolithic SrTiO 3 crystal showing good quantitative agreement with the expected structure.
Abstract Atomic‐resolution imaging of halide perovskites (HPs) using transmission electron microscopy (TEM) is challenging because of the sensitivity of their structures to the electron beam. In this article, recent achievements in this area are reviewed, covering both all‐inorganic and organic–inorganic hybrid HPs, with an emphasis on the specific imaging conditions that have proven to be effective in avoiding electron beam‐induced structural damage. The discussion focusses on the total electron dose that HPs can bear before being damaged and the effects of different imaging modes, accelerating voltages, and temperatures. The crucial role of a direct‐detection electron‐counting camera in reducing the required electron dose is outlined, which is indispensable for imaging extremely sensitive organic–inorganic hybrid perovskites. In addition to reviewing published works, the results of initial attempts to perform atomic‐resolution elemental mapping for an all‐inorganic HP and image a hybrid HP using scanning TEM are introduced. The preparation of a TEM specimen from macroscopic crystals or devices of HPs, which is very important for practical applications but has not yet received attention, is also discussed. This article aims to provide guidance on the acquisition of atomic‐resolution TEM images of HPs and inspire the development of more imaging technologies for sensitive materials.
Imaging of optical phenomena at the sub-nanometer scale can offer fundamental insights into the electronic or vibrational states in atomic-scale defects, molecules, and nanoparticles, which are important in quantum information, heterogeneous catalysis, optoelectronics, and structural biology. Several techniques have surpassed the traditional Abbe diffraction limit and attained spatial resolutions down to a few nanometers, but sub-nanometer scale optics has remained elusive. Here, we propose an approach that combines spectrally specific photoabsorption with sub-nanometer scale resolution transmission electron microscopy (TEM) of photoexcited electrons. We first estimate the signal level and conditions required for imaging nanoscale optical phenomena in core-shell quantum dots (QDs) like CdS/CdTe. Furthermore, we show the possibility of imaging photoexcited states of atomic-scale defects in a monolayer hexagonal boron nitride (h-BN) using ab initio and high resolution (HR)TEM simulations. The ability to directly visualize photoexcited states at the sub-nanometer scale opens opportunities to study properties of individual quantum dots and atomic defects.
The nanoscale mechanisms of localized corrosion in low carbon steels have remained elusive due to the complexity of studying the degradative material behavior at nanoscale solid-liquid interfaces. We identified various steps in the nanogalvanic corrosion processes using in-situ liquid-cell scanning transmission electron microscopy (STEM) using a microfluidic holder by Hummingbird Scientific. Initial work, performed at low magnification, identified the initiation point on a 1018 low-carbon steel surface. This initiation point was determined to be a triple junction of two ferrite grains bridging a cementite grain in contact with a baseline electrolyte of 6 uM CO2 dissolved in a buffered (2.78 uM Na2SO4) aqueous solution, pH 6.1. The pre-etched low-carbon steel surface was prepared using focused ion beam lift-out procedures to extract a cross-section of the low-carbon steel surface, which then was thinned to about 150 nm and transferred to a SiN membrane microfluidic window. The transfer was made using a lift-out needle to attach the low-carbon steel lamella to the corner of the SiN window, and then Pt/C deposition held the lamella in contact with the window while it was released from the lift out needle. To identify the triple point on the low carbon steel lamella, prior to attachment on the SiN window, the sample was characterized for compositional variations with energy dispersive x-ray spectroscopy mapping, grain orientation and phase mapping with precession electron diffraction, and thickness mapping with energy filtered transmission electron microscopy. This pre-characterization prior to the in-situ experiment provided a map of the multiphase and multigrain structure, where the in-situ liquid cell imaging provided a clear understanding of the initiation point on the sample. These data were cross-correlated to paint a holistic picture of the triple junction site, enabling low electron-fluence in-situ snapshot imaging to avoid dominating the native corrosion reactions with effects from the incident electron beam. This initial result identified that localized, nanogalvanic corrosion at the phase interface was the dominant corrosion process in the low-carbon steel, so we next targeted the observation of an array of these nanogalvanic features phase boundaries in a pearlite grain. Near-surface ferrite/cementite phase interfaces that typify pearlitic low-carbon steel were extracted, pre-characterized, and imaged for the in-situ corrosion processes. The sample was a cross-section from a pearlite grain, with alternating ferrite and cementite grains that extended microns down from the pre-etched low-carbon steel pipe surface. After contact with a buffered aqueous solution, the phase boundaries between the ferrite and cementite began to dissolve, with observable material loss and thickness changes in the dark-field and bright-field STEM images. Within minutes, the corrosion front proceeded deeper into the material, claiming a thin layer of ferrite around all exposed phase boundaries before progressing laterally into the ferrite matrix, converting the ferrite to corrosion product normal to each buried cementite grain. Formation of the corrosion product causes a volumetric expansion, creating a lateral wedging force that mechanically ejects the cementite grains from their grooves and leaves behind percolation channels into the steel substructure. Rapid and deleterious, this nanogalvanic corrosion pathway represents an important target for understanding and preventing run-away degradation in this common building material. Observation of this corrosion mechanism was enabled by the combination of pre-characterization using standard structural, grain, and compositional analysis in the TEM, which provides maps for understanding the reaction propagation captured in low-dose, in-situ, liquid-cell STEM.