pnnl/Temwizard
TEMWizard is used to visualize results from Atomap, an open-source software tool for determining the position and other features of atomic columns in transmission electron microscopy images.
Oostrom, Marjolein↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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NSUF Rad-AFM: Nanoscale Material Property Measurements of Radioactive Materials
Many advances in our understanding of radiation induced damage mechanisms are due to imaging techniques including transmission electron microscopy and scanning electron microscopy. However, the effect of these radiation induced defects characterized on the atomic to micrometer scale are often interpreted by changes in bulk material properties measured in the hundreds of microns to cm scale. In order to bridge this gap, additional tools need to be developed for use with radioactive materials. To this end a new Asylum Infinity multimodal atomic force microscope (AFM) has been added to the library of Nuclear Science User Facility (NSUF) instruments for use on radioactive materials. This instrument is housed in the Radiochemical Processing Laboratory (RPL) which is a Department of Energy Hazard Category II Non-Reactor Nuclear Facility operated by Battelle at Pacific Northwest National Laboratory. This allows for the handling, sample preparation, and characterization of highly radioactive materials providing a unique capability to understand fundamental properties of nuclear materials and the effects of radiation induced damage.
Riechers, Shawn L.↗
MOCVD Growth and Characterization of Wide Bandgap ZnGeN 2 Thin Films
ZnGeN 2 is a wide bandgap material having less than 0.1% lattice mismatch with GaN and bandgap within few percent of that of GaN. Based on the first principle calculations, the valence band of ZnGeN 2 is situated ~1.4 eV above that of GaN at the heterointerface. Such a staggered band alignment between two closely lattice matched materials has already been demonstrated promising for novel optoelectronic device designs, for example, high efficiency blue and green light emitting diodes. However, the research on ZnGeN 2 is yet to pass the inceptive period. We report on the growth of single crystalline ZnGeN 2 films using metalorganic chemical vapor deposition and investigation on the crystalline, optical, electrical properties. Diethylzinc (DEZn), germane (GeH 4 ) and ammonia were used as the precursors for Zn, Ge and N, respectively. Films were grown on GaN and sapphire substrates. The Zn/Ge atomic ratio in the films determined from energy dispersive X-ray spectroscopy was found to decrease with increase in growth temperature but to increase with increase in pressure and DEZn/GeH 4 molar flow rate ratio. The X-ray diffraction (XRD) 2θ-ω spectra of the single crystalline ZnGeN 2 films resembled those of orthorhombic (perfectly ordered cations) or distorted wurtzite (disordered cations) polymorphs. High resolution scanning transmission electron microscopy imaging was used to further investigate the crystalline quality and crystalline structure of the films. The single crystalline films on c-sapphire or GaN substrates were found to have planar surface from scanning electron micrographs while those on r-sapphire substrate had stepped surface. No near band edge peak but a broad peak at ~2.05 eV was observed in the photoluminescence (PL) spectra, which was attributed to the transitions involving deep level defects. The PL excitation spectra peaked around 3.4 eV, which is due to the enhanced absorption of photons having energy close to the band gap. The as grown films were found to be n-type with 10 18 – 10 19 cm -3 carrier densities and mobility up to 17 cm 2 /V·s. In the end, the advancement made in this work regarding the growth of ZnGeN 2 thin films is a footstep towards the understanding of the material itself and in turn, towards the implementation of ZnGeN 2 based novel device designs.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Hardness Measurements and Interface Behavior of SiC-B 4 C-Si Multiple Phase Particulate Composites Made with Melt Infiltration and Additive Manufacturing
Reaction-bonded SiC-B 4 C-Si ceramic composites were binder jet 3D-printed and subsequently pressureless-melt-infiltrated with molten Si. The addition of B 4 C aided the Si infiltration to produce a highly dense composite. The microstructures and phases of these composites were examined. The measured hardness values of each constituent with Vickers and nanoindentation matched the bulk values, and the macro-hardness values with Knoop and spherical indentation represented the bulk, composite hardness values of all three phases together, which was close to a rule of mixtures value. For particulate-based composites, this is a significant finding. The interfacial bonds of SiC and Si were imaged using scanning transmission electron microscopy to view intimacy, whereas the crack propagation was examined with carefully placed indents. This work demonstrated that pressureless melt infiltration with a reactive particle provides a method to shape non-wetting reaction-bonded ceramic composites with limited shrinkage and high density and provides insights into the mechanical behavior with numerous indentation techniques.
36 MATERIALS SCIENCE↗
In Situ Ultra-Small- and Small-Angle X-ray Scattering Study of ZnO Nanoparticle Formation and Growth through Chemical Bath Deposition in the Presence of Polyvinylpyrrolidone
ZnO inverse opals combine the outstanding properties of the semiconductor ZnO with the high surface area of the open-porous framework, making them valuable photonic and catalysis support materials. One route to produce inverse opals is to mineralize the voids of close-packed polymer nanoparticle templates by chemical bath deposition (CBD) using a ZnO precursor solution, followed by template removal. To ensure synthesis control, the formation and growth of ZnO nanoparticles in a precursor solution containing the organic additive polyvinylpyrrolidone (PVP) was investigated by in situ ultra-small- and small-angle X-ray scattering (USAXS/SAXS). Before that, we studied the precursor solution by in-house SAXS at T = 25 °C, revealing the presence of a PVP network with semiflexible chain behavior. Heating the precursor solution to 58 °C or 63 °C initiates the formation of small ZnO nanoparticles that cluster together, as shown by complementary transmission electron microscopy images (TEM) taken after synthesis. The underlying kinetics of this process could be deciphered by quantitatively analyzing the USAXS/SAXS data considering the scattering contributions of particles, clusters, and the PVP network. A nearly quantitative description of both the nucleation and growth period could be achieved using the two-step Finke–Watzky model with slow, continuous nucleation followed by autocatalytic growth.
36 MATERIALS SCIENCE↗
Coalescence of GaP on V-Groove Si
In recent years, better understanding and control over the formation of crystalline defects during the direct epitaxy of III-V semiconductors on Si substrates via metal organic vapor phase epitaxy (MOVPE) has enabled large gains in III-V-on-Si solar cell efficiency, pointing to pathway to lower-cost, high-performance III-V solar cells. However, such results have only been achieved on costly chemo-mechanically-polished (CMP) Si wafers. The use of V-groove nanopatterned Si substrates has demonstrated similarly high-crystalline-quality III-V-on-Si epitaxy, but also can be combined with lower-cost polishing techniques. Although they offer a potential cost advantage, growth on V-groove substrates adds challenges not present for epitaxy on planar wafers (the III-V material must be coalesced into a thin film after an initial nucleation stage). MOVPE growth conditions that promote highly facet-selective lateral growth needed for coalescence are generally actively avoided for conventional thin fim growth, so growth conditions need to be re-developed for V-groove-based epitaxy. Additionally, coalescence add complexity to the dislocation dynamics related to lattice relaxation, so strategies used to keep threading dislocation density (TDD) low on planar substrates need to be re-tuned for V-grooves. We have studied the morphological evolution and dislocation dynamics of GaP grown on V-groove Si by MOVPE. Growth conditions of V/III=5,000 and Tg=800 degrees C were uniquely found to produce extremely smooth coalesced thin films, with an RMS roughness of 0.2 nm measured by AFM. Additionally, at this growth condition, we identified two regimes of growth determined by the width of the SiNx cap (a remnant of the nanopatterning process) at the top of the V-grooves. For narrow caps, the GaP coalesces into a thin film, and for wide caps, the GaP evolves into {1 1 1}-faceted diamonds that do not coalesce. We suggest the influence of Si from the sidewalls of the V-grooves on the surface reconstruction of the GaP as the mechanism for this effect, with reflection difference spectroscopy (RDS) and Si doping experiments supporting this theory. In addition to morphology, the dislocation dynamics of the system were studied with electron channeling contrast imaging (ECCI) and transmission electron microscopy (TEM). The TDD of the coalesced GaP films was found to be 5 x 10^7 cm^-2 after coalescence via ECCI, a level still too high for high-quality solar cells. However, misfit dislocations crossing multiple grooves greater than 20 micrometers long were observed in ECCI, suggesting that the V-grooves do not block dislocation glide. TEM prior to and after coalescence was used to distinguish between dislocation creation driven by growth conditions and coalescence. Finally, strategies to reduce the dislocation density to levels acceptable for solar cells will be discussed.
GaP↗
Coalescence of GaP on V-Groove Si
With an increase of control over crystalline defects, metallorganic vapor phase epitaxy (MOVPE)-grown III-V-on-Si multijunction solar cells have seen rapid increases in efficiency in recent years, pointing to a promising path to lower cost III-V solar cells. However, the cost of chemo-mechanical polishing the Si wafers to prepare them for epitaxy is high. The use of V-groove nanopatterns enables similar defect reduction to that achieved on planar wafers, but the nanopatterns can be fabricated with a low-cost process. While V-grooves offer advantages over planar Si, they add complexity to the growth process. In particular, coalescence can cause the formation of threading dislocations, and the highly-directional growth conditions required for coalescence are unusual for MOVPE. We have studied the coalescence of GaP films nucleated directly on V-groove Si by MOVPE. We observed that for optimized growth conditions (V/III=5,000 and T=800 C) two growth modes were possible, and the resulting morphology depended on the exact geometry of the SiNx cap used to cover the (0 0 1)-oriented Si at the tops of the grooves. For caps with a width >100 nm, noncoalescing, nano ridge-like growth terminating in f1 1 1g facets was observed. For narrower caps, coalescence with an RMS roughness of 0.2 nm as measured by atomic force microscopy was observed. We will discuss mechanisms responsible for this phenomenon, including the role of Si from the substrate surface. The dislocation dynamics of this system were studied with electron channeling contrast imaging (ECCI) and transmission electron microscopy (TEM). We find that V-grooves do not block dislocation glide; ECCI measurements show misfit dislocations greater than 10 micrometers long observed to continue perpendicularly across neighboring V-grooves. In addition, all threading dislocations appear to lie on f1 1 1g planes, consistent with the GaP relaxing via glided-in glissile dislocations. The dislocation dynamics and morphological evolution of the coalescence of GaP on Si, possible mechanisms behind the observed phenomenons, and further dislocation mitigation strategies for these materials will be presented.
dislocation dynamics↗
In vitro investigation of protein assembly by combined microscopy and infrared spectroscopy at the nanometer scale
The nanoscale structure and dynamics of proteins on surfaces has been extensively studied using various imaging techniques, such as transmission electron microscopy and atomic force microscopy (AFM) in liquid environments. These powerful imaging techniques, however, can potentially damage or perturb delicate biological material and do not provide chemical information, which prevents a fundamental understanding of the dynamic processes underlying their evolution under physiological conditions. Here, we use a platform developed in our laboratory that enables acquisition of infrared (IR) spectroscopy and AFM images of biological material in physiological liquids with nanometer resolution in a cell closed by atomically thin graphene membranes transparent to IR photons. In this work, we studied the self-assembly process of S-layer proteins at the graphene-aqueous solution interface. The graphene acts also as the membrane separating the solution containing the proteins and Ca 2+ ions from the AFM tip, thus eliminating sample damage and contamination effects. The formation of S-layer protein lattices and their structural evolution was monitored by AFM and by recording the amide I and II IR absorption bands, which reveal the noncovalent interaction between proteins and their response to the environment, including ionic strength and solvation. Our measurement platform opens unique opportunities to study biological material and soft materials in general.
59 BASIC BIOLOGICAL SCIENCES↗
Contrast Mechanisms in Secondary Electron e-Beam-Induced Current (SEEBIC) Imaging
Abstract Over the last few years, a new mode for imaging in the scanning transmission electron microscope (STEM) has gained attention as it permits the direct visualization of sample conductivity and electrical connectivity. When the electron beam (e-beam) is focused on the sample in the STEM, secondary electrons (SEs) are generated. If the sample is conductive and electrically connected to an amplifier, the SE current can be measured as a function of the e-beam position. This scenario is similar to the better-known scanning electron microscopy-based technique, electron beam-induced current imaging, except that the signal in the STEM is generated by the emission of SEs, hence the name secondary electron e-beam-induced current (SEEBIC), and in this case, the current flows in the opposite direction. Here, we provide a brief review of recent work in this area, examine the various contrast generation mechanisms associated with SEEBIC, and illustrate its use for the characterization of graphene nanoribbon devices.
47 OTHER INSTRUMENTATION↗
Automated phase segmentation and quantification of high-resolution TEM image for alloy design
In the alloy design and development process, a wealth of atomically resolved structural high-resolution transmission electron microscopy (HRTEM) images are produced. Identifying the different nano-precipitate phases and tracking their evolution under various compositions and during manufacturing or post-processing requires hundreds of HRTEM images and thousands of precipitates. The nanoscopic phase information labeling and analysis purely relies on humans are prohibitively costly and time-consuming, sometimes not reliable because of the lack of authoritative knowledge. Here, in this work, we develop a novel unsupervised machine learning approach coupled with adaptive computer vision techniques with features in the Fourier space to automatically determine the number of phases and segment/quantify the phases with nanoscale resolution, allowing for quantitative correlation between nanostructure formation, processing and functional properties. To automate the phase extraction/quantification and ascertain its applicability, we have applied the developed framework to the HRTEM images from several alloy systems, processing conditions, image magnifications, and phase types and morphologies (precipitates, nano-twins, stacking faults, crystalline matrix, and amorphous structures) for verification. This study paves the road for compression, visualization, and translation of raw image structural data into physically relevant information in real-time with minimal human supervision. It shows the promise of enabling high-throughput materials characterization for the acceleration of alloy manufacturing and design.
36 MATERIALS SCIENCE↗
Capturing the softening in T91 steel annealed with molten LBE through interfacial gradient plasticity
In order to study the effect of annealing with molten lead–bismuth eutectic alloy (mLBE) on the mechanical properties of T91 steel, micropillar compression tests were analyzed through the consideration of mechanical interface energy terms within gradient plasticity. The stress–strain curves of the micropillars showed significant stochastic effects, characterized by a different elastic and plastic behavior response for each pillar. Among the various pillars, some exhibited a similar trilinear behavior. Scanning electron microscopy (SEM) images of these specimens demonstrated localized slip deformation and slip planes after compression, while transmission electron microscopy (TEM) images showed the presence of a severe slip zone along the random grain boundaries (RGBs), indicating that the GBs played a dominant role in the deformation/slip. By employing interfacial gradient plasticity that can explicitly account for the presence of GBs, the trilinear response was captured, allowing for the determination of the mechanical interface parameter. As anticipated the pillars which underwent severe slip at the GBs had a lower value for the mechanical interface parameter. Finally, annealing with mLBE can, therefore, in some cases result in softening in the overall stress–strain, as it lowers the mechanical interface energy of GBs.
36 MATERIALS SCIENCE↗
STEM Imaging of Materials: Final Report for DE-FG02-08ER46547
This project supported research advancing the state of the art in scanning transmission electron microscopy (STEM) imaging and characterization of materials, especially electronic materials. The project began with studies of point defects in materials using high-resolution Z-contrast STEM, then evolved to including methods in four-dimensional (4D) STEM and machine learning. Highlights include: (1) the discovery of stable, p-type ZnO via Sb doping, (2) demonstration that the high performance of InGaN LEDs is not due to In composition fluctuations as previously proposed, (3) the demonstration of the first sub-pm precision high-resolution STEM images using non-rigid registration of a series of STEM images, (4) the extension of high-precision STEM to low dose imaging and spectrum imaging, (5) discovery of a synthesis method for twisted spiral growth of transition-metal dichalcogenide materials via chemical vapor deposition, and (6) development of machine-learning methods for analysis and denoising of enormous 4D STEM data sets.
36 MATERIALS SCIENCE↗
Atomic Resolution Cryogenic 4D-STEM Imaging via Robust Distortion Correction
Cryogenic four-dimensional scanning transmission electron microscopy (4D-STEM) imaging is a useful technique for studying quantum materials and their interfaces by simultaneously probing charge, lattice, spin, and chemistry on the atomic scale with the sample held at temperatures ranging from room to cryogenic. However, its applications are currently limited by the instabilities of cryo-stages and electronics. To overcome this challenge, we develop an algorithm to effectively correct the complex distortions present in atomic resolution cryogenic 4D-STEM data sets. This method uses nonrigid registration to identify localized distortions in a 4D-STEM and relate them to an undistorted experimental STEM image, followed by a series of affine transformations for distortion corrections. This method allows a minimum loss of information in both reciprocal and real spaces, enabling the reconstruction of sample information from 4D-STEM data sets. This method is computationally cheap, fast, and applicable for on-the-fly data analysis in future in situ cryogenic 4D-STEM experiments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Massive all-atom analysis of 2D materials with quantum properties (Final report)
Improvements in microscopy have enabled the acquisition of data at a scale that is difficult to process manually, making automated machine learning approaches to analyzing experimental images essential. In this project, we developed and applied machine learning (ML) workflows for atomic resolution scanning transmission electron microscopy (STEM) images. This development included improving both methodology as well as generating user-friendly codes. We developed machine learning architectures which, after training, automatically identify the location and types of defects throughout a material. We used these data to produce class-averaged images of 2D atomic coordinates with up to 0.3 pm precision, uncovering the structure and oscillations of long-range strain fields around point defects in WSe 2-2x Te 2x . We also resolved a long-standing problem in this field in the training of ML models, a lack of labeled experimental data, by developing a cycle-GAN that transformed simulated-generated labeled data into labeled data indistinguishable from experiment and therefore suitable for training. This removed the remaining parts of the ML data processing workflow where human intervention was still critical and therefore a bottleneck to working at scale. Codes have been developed and released for this full machine learning workflow. ML approaches to partially automate STEM acquisition were also developed. Finally we applied ML and other advanced data processing methods to several materials science problems in two-dimensional materials, including studying the evolution of hyperuniformity with defect concentration in WSe2, understanding phase transformations in transition metal dichalcogenides during in-situ heating in the STEM, and exploring how 2D interfaces transform from twisted into aligned structures.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Electron image contrast analysis of mosaicity in rutile nanocrystals using direct electron detection
Direct electron detection provides high detective quantum efficiency, significantly improved point spread function and fast read-out which have revolutionized the field of cryogenic electron microscopy. However, these benefits for high-resolution electron microscopy (HREM) are much less exploited, especially for in situ study where major impacts on crystallographic structural studies could be made. By using direct detection in electron counting mode, rutile nanocrystals have been imaged at high temperature inside an environmental transmission electron microscope. The improvements in image contrast are quantified by comparison with a charge-coupled device (CCD) camera and by image matching with simulations using an automated approach based on template matching. Together, these approaches enable a direct measurement of 3D shape and mosaicity (~1°) of a vacuum-reduced TiO 2 nanocrystal about 50 nm in size. Thus, this work demonstrates the possibility of quantitative HREM image analysis based on direct electron detection.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis
Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.
42 ENGINEERING↗
Detection of defects in atomic-resolution images of materials using cycle analysis
The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmission-electron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material’s local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., “2D materials”) and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.
36 MATERIALS SCIENCE↗
Automation of Laser Plasma Focused Ion Beam Microscopy for Next-Gen Energy Materials
Automation can revolutionize the use of ultrafast laser ablation and plasma-focused ion beam (PFIB) techniques for high-throughput, reproducible cross-sectioning and various sample preparation in materials characterization. As these methods become essential for analyzing complex energy materials and next-generation devices, efficient, standardized workflows are needed to minimize variability and enhance precision. This work highlights our advancements in developing automated processes for sample preparation that integrates machine learning, workflow optimization, and large-scale data acquisition to improve efficiency and scalability in applications such as electrolyzers, photovoltaic cells, and microelectronics. To streamline cross-sectioning and lamella fabrication, we have implemented fully automated workflows that standardize laser ablation and PFIB milling sequences. These workflows incorporate pre-programmed protocols for material removal, alignment, and thinning, reducing user intervention and ensuring consistency across different sample types. Machine learning algorithms further enhance automation by predicting optimal milling strategies and adapting parameters based on material properties and sectioning requirements. This approach significantly improves throughput while maintaining the structural integrity of prepared samples for high-resolution imaging and analysis, including transmission electron microscopy. Beyond sample preparation, our automation platform enables the acquisition of large, high-resolution datasets through serial sectioning, image alignment, and 3D reconstruction. These automated routines facilitate multi-scale characterization, capturing structural and compositional details from the nanoscale to the device level. By reducing variability and increasing efficiency, our automated approach enhances defect analysis, failure diagnostics, and process optimization, accelerating advancements in materials research and device engineering.
36 MATERIALS SCIENCE↗