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Composite solid propellants surface structure and profile characteristics burning with various oxidizers and polyurethane binder by scanning electron microscopy
Understanding the limits of spatio-temporal carrier dynamics, especially in III-V semiconductors, is key to designing ultrafast and ultra-small optoelectronic components. However, identifying such limits and the properties controlling them has been elusive. Here, using scanning ultrafast electron microscopy (SUEM), in bulk n-GaAs and p-InAs, we simultaneously measure picosecond carrier dynamics along with three related quantities: sub-surface band bending, above-surface vacuum potentials, and surface trap densities. We make two surprising observations. First, we uncover a negative-time contrast in secondary electrons resulting from an interplay among these quantities. Second, despite dopant concentrations and surface state densities differing by many orders of magnitude between the two materials, their carrier dynamics, measured by photo-excited band bending and filling of surface states, occur at a seemingly common timescale of about 100 ps. This observation may indicate fundamental kinetic limits tied to a multitude of material and surface properties of optoelectronic III-V semiconductors, and highlights the need for techniques that simultaneously measure electro-optical kinetic properties.
Materials with tailored quantum properties can be engineered from atomic-scale assembly techniques, but existing methods often lack the agility and accuracy to precisely and intelligently control the manufacturing process. Here, we demonstrate a fully autonomous approach for fabricating atomic-level defects using electron beams in scanning transmission electron microscopy (STEM) that combines advanced machine learning and automated beam control. As a proof of concept, we achieved controlled fabrication of MoS-nanowire (MoS-NW) edge structures by iterative and targeted exposure of MoS 2 monolayer to a focused electron beam to selectively eject sulfur atoms, utilizing high-angle annular dark-field (HAADF) imaging for feedback-controlled monitoring of structural evolution of defects. A machine learning framework combining a random forest model and a convolutional neural network (CNN) was developed to decode the HAADF image and accurately identify atomic positions and species. This atomic-level information was then integrated into an autonomous decision-making platform, which applied predefined fabrication strategies to instruct beam control about atomic sites to be ejected. The selected sites were subsequently exposed to a localized electron beam using an FPGA-controlled scan routine with precise control over beam positioning and duration. While the MoS-NW edge structures produced exhibit promising mechanical and electronic properties, the proposed methods to build the autonomous fabrication framework is material-agnostic and can be extended to other 2D materials for the creation of diverse defect structures and heterostructures beyond Mo S2 .
We report scalable production of carbon nanotubes (CNTs) requires catalysts and reaction conditions that provide high nucleation efficiency. In situ characterization methods such as environmental transmission electron microscopy (ETEM) can reveal fundamental mechanisms of synthesis, but to date have primarily provided qualitative observations on small sample sizes. Here, quantitative analysis is performed using high-resolution, high-rate video capture of ETEM experimentation coupled with automated image processing, involving computer vision algorithms and convolutional neural networks. By this approach, we detect distinct nanoparticle formation from an alumina-supported iron thin film and subsequent CNT nucleation from the nanoparticles. The statistical summary of particles in each video shows that, compared to a H 2 -only atmosphere, pretreatment of the catalyst with carbon added to the H 2 atmosphere results in a smaller average particle diameter, a 2-fold increase in particle density (to 5300 particles/μm 2 ), a 3-fold increase in CNT nucleation efficiency (to 92%), and more than a 5-fold increase in CNT density (to 4800/μm 2 ). Addition of carbon during exposure to H 2 is also more effective than NH 3 at dewetting the catalyst film and increasing the CNT nucleation efficiency, in spite of NH 3 being a stronger reducing agent for iron. Insights from this study are applicable to improving CNT yield and productivity in both batch-style and continuous processes.
Matrix material from the Murchison C2(M) carbonaceous chondrite and less than micrometer-size pyroxene inclusions in an olivine from the Kenna ureilite have been characterized using high resolution transmission electron microscopy. A diversity of textures and mineral associations, including serpentine-type and brucite-type mixed-layering, occur in the Murchison matrix. Such mixed layer structures are new types of layer silicate. Their presence in the matrix may prove to be genetically significant. The Kenna inclusions, formed at the olivine grain margins, are of two types: (1) a disordered orthopyroxene-clinopyroxene which may have inverted from protopyroxene and (2) a pigeonite with narrow anti-phase domains (80 A to 250 A) and boundaries parallel to (100).
A commercial transmission electron microscope (TEM), with flat-plate upper pole piece configuration of the objective lens, and top-entry specimen introduction was modified by introducing an ultrahigh vacuum (UHV) specimen chamber for in situ TEM experimentation. The pumping and design principles and special features of this UHV chamber, which makes it possible to obtain 5 x 10 to the -10th mbar pressure at the site of the specimen, while maintaining the airlock system that allows operation in the 10 to the -10th mbar range within 15 min after specimen change, are described. Design operating pressures and image quality (resolution of metal particles smaller than 1 nm in size) were achieved. Schematic drawings and design dimensions are included.
The dimensions of graphitic layer planes directly affect the reactivity of soot towards oxidation and growth. Quantification of graphitic structure could be used to develop and test correlations between the soot nanostructure and its reactivity. Based upon transmission electron microscopy images, this paper provides a demonstration of the robustness of a fringe image analysis code for determining the level of graphitic structure within nanoscale carbon, i.e. soot. Results, in the form of histograms of graphitic layer plane lengths, are compared to their determination through Raman analysis.
The dimensions of graphitic layer planes directly affect the reactivity of soot towards oxidation and growth. Quantification of graphitic structure could be used to develop and test correlations between the soot nanostructure and its reactivity. Based upon transmission electron microscopy images, this paper provides a demonstration of the robustness of a fringe image analysis code for determining the level of graphitic structure within nanoscale carbon, i.e., soot. Results, in the form of histograms of graphitic layer plane lengths, are compared to their determination through Raman analysis.
The evolution of rapid solidification (RS) microstructure and solidification interface velocity has been studied experimentally by in-situ transmission electron microscopy and postmortem characterization for hypereutectic Al-20at.%Cu (37 wt.%Cu) after laser melting. Four morphologically distinct regions formed via growth modes changing from θ-Al 2 Cu phase dendrites to eutectic cell growth, possibly α-Al dendrite growth (α-cell), banded growth, and α-Al plane front growth. Tendency for faceting and low capacity for solute trapping limited the θ-phase dendrite growth to solidification interface velocity v < 0.07 m/s. Consequently, formation of pro-eutectic micro-constituent was suppressed, and RS microstructure formation was dominated by eutectic, α-cell, and banded morphology grains for the Al-20at.%Cu alloy. Eutectic growth operated for interface velocity of 0.1 m/s ≤ v < 0.3 m/s, with a transition from regular lamellar, 2-λ and 1-λ mode to a dense irregular morphology dominated by α-phase at v = 0.3 m/s. Interface temperature calculations indicated feasibility of α-cell growth mode for 0.3 m/s ≤ v < 0.7 m/s. Banded growth occurred for 0.7 m/s ≤ v < 1.3 m/s. Plane front α-phase growth was evident for interface velocities v ≥ 1.3 m/s. Previous work on rapid solidification microstructure development in hypereutectic Al-Cu alloys reported a regime of α-cell growth subsequently to eutectic and prior to transition to banded growth for Al-19at.%Cu (36 wt.%Cu), while for Al-22at.%Cu (40 wt.%Cu) eutectic growth transitioned directly to α-plane front growth without emergence of a banded regime. Finally, based on the current study the disappearance of the banded growth regime occurs for a composition larger than 20at.%Cu.
Grain identification in polycrystalline nanoparticles, for example, determining which crystal phases are present at each spatial location, is fundamental to materials characterisation. This is particularly challenging when grains overlap extensively, as commonly occurs in four-dimensional scanning transmission electron microscopy (4D-STEM) datasets. We propose a simplex-based model (SBM) in which each simplex vertex represents the diffraction pattern (DP) of a pure grain, and the simplex edges and interior represent overlapping grains. Our SBM grain identification algorithm operates on the Bragg disk (BD) data matrix distilled from the 4D-STEM data to identify the grain membership at each scan position, together with a BD feature matrix whose columns represent the DPs for each constituent grain, which is important for identifying the crystal structure of each grain. We solve the model using a two-stage algorithm. In Stage 1, we adapt a linear mixing algorithm to estimate an initial BD feature matrix whose columns represent DPs of potentially overlapping grains. Our Stage 2 algorithm incorporates sparsity considerations to transform the initial BD feature matrix so that its columns represent DPs of pure grains. Using simulated datasets with various grain configurations, we demonstrate that SBM recovers both the BD feature matrix and membership maps more accurately than existing methods, even when a grain lacks any pure region and completely overlaps with other grains.
Accurate evaluations of diffusion lengths for heavily to moderately doped III-V semiconductors and/or radiation damaged solar cells have been made possible by using experimental and numerical techniques. The techniques employed were electron beam induced current and low voltage electron microscopy.
We introduce a novel composite holey gold support that prevents cryo-crinkling and reduces beam-induced motion of soft specimens, building on the previously introduced all-gold support. The composite holey gold support for high-resolution cryogenic electron microscopy of soft crystalline membranes was fabricated in two steps. In the first step, a holey gold film was transferred on top of a molybdenum grid. In the second step, a continuous thin carbon film was transferred onto the holey gold film. This support (Au/Mo grid) was used to image crystalline synthetic polymer membranes. The low thermal expansion of Mo is not only expected to avoid cryo-crinkling of the membrane when the grids are cooled to cryogenic temperatures, but it may also act to reduce whatever crinkling existed even before cooling. Here, the Au/Mo grid exhibits excellent performance with specimens tilted to 45°. This is demonstrated by quantifying beam-induced motion and differences in local defocus values. In addition, images of specimens on the Au/Mo grids that are tilted at 45° show high-resolution information of the crystalline membranes that, after lattice-unbending, extends beyond 1.5 Å in the direction perpendicular to the tilt axis.
Electron microscopic study of micrometeoritic material collected in upper atmosphere
The stability of lithium batteries is tied to the physicochemical properties of the solid-electrolyte interphase (SEI). Owing to the difficulty in characterizing this sensitive interphase, the nanoscale distribution of SEI components is poorly understood. Here, we use cryogenic scanning transmission electron microscopy (cryo-STEM) to map the spatial distribution of SEI components across the metallic Li anode. We reveal that LiF, an SEI component widely believed to play an important role in battery passivation, is absent within the compact SEI film (~15 nm); instead, LiF particles (100–400 nm) precipitate across the electrode surface. We term this larger length scale as the indirect SEI regime. On the basis of these observations, we conclude that LiF cannot be a dominant contribution to anode passivation nor does it influence Li+ transport across the compact SEI film. We refine the traditional SEI structure derived from ensemble-averaged characterizations and nuance the role of SEI components on battery performance.
Solute-intercalation-induced phase separation creates spatial heterogeneities in host materials, a phenomenon ubiquitous in batteries, hydrogen storage, and other energy devices. Despite many efforts, probing intercalation processes at the atomic scale has been a significant challenge. By utilizing liquid-phase transmission electron microscopy (TEM), we study hydrogen (de)intercalation in palladium nanocrystals as a model system and have achieved unprecedented atomic-resolution imaging of hydrogen intercalation wave dynamics. Our observations reveal that intercalation wave mechanisms, instead of shrinking-core mechanisms, prevail at ambient temperature for palladium nanocubes ranging from ∼60 nm down to ∼10 nm. Systematic image analysis uncovers the atomic evolution of the hydrogen intercalation wave, transitioning from nonplanar and inclined boundaries to those closely aligned with {100} planes. Our kinetic Monte Carlo simulations demonstrate that the observed intercalation wave dynamics correspond to sorption pathways minimizing the lattice mismatch strain at the phase boundary. In conclusion, unveiling the atomic intercalation pathways holds profound implications for engineering intercalation-mediated devices and advancements in energy sciences.
SUMMARY Leaf plastids harbor a plethora of biochemical reactions including photosynthesis, one of the most important metabolic pathways on Earth. Scientists are eager to unveil the physiological processes within the organelle but also their interconnection with the rest of the plant cell. An increasingly important feature of this venture is to use experimental data in the design of metabolic models. A remaining obstacle has been the limited in situ volume information of plastids and other cell organelles. To fill this gap for chloroplasts, we established three microscopy protocols delivering in situ volumes based on: (i) chlorophyll fluorescence emerging from the thylakoid membrane, (ii) a CFP marker embedded in the envelope, and (iii) calculations from serial block‐face scanning electron microscopy (SBFSEM). The obtained data were corroborated by comparing wild‐type data with two mutant lines affected in the plastid division machinery known to produce small and large mesophyll chloroplasts, respectively. Furthermore, we also determined the volume of the much smaller guard cell plastids. Interestingly, their volume is not governed by the same components of the division machinery which defines mesophyll plastid size. Based on our three approaches, the average volume of a mature Col‐0 wild‐type mesophyll chloroplasts is 93 μm 3 . Wild‐type guard cell plastids are approximately 18 μm 3 . Lastly, our comparative analysis shows that the chlorophyll fluorescence analysis can accurately determine chloroplast volumes, providing an important tool to research groups without access to transgenic marker lines expressing genetically encoded fluorescence proteins or costly SBFSEM equipment.
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.
The potential of wide-band-gap III-V nitrides as solar-blind ultraviolet sensors and light emitters has prompted an increasing amount of work recently, including the fabrication of the first UV sensors from as-deposited single crystal GaN. We have used high resolution transmission electron microscopy (TEM) to study the microstructure of two novel developments of wide-band-gap III-V nitrides: the growth of ultra-short period GaN/AlN super lattices (digital alloys); and the incorporation of SiC layers into Al(sub x)Ga(sub 1-x)N structures. By varying the relative periods in a GaN/AlN superlattice, it should be possible to tailor the band gap of the composite to lie between the elemental values of 365 nm for GaN and 200 nm for AlN. The group IV semiconductor, SiC, has a close lattice match (< 3%) to Al(sub x)Ga(sub 1-x)N for growth on the basal plane. Demonstration of epitaxial growth for Al(sub x)Ga(sub 1-x)N/SiC multilayers could enable an extension of direct band-gap material towards the visible.