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At least 595 records · Page 33

Characterization of the atomic-level structure of γ-alumina and (111) Pt/γ-alumina interfaces

The atomic-level structure of platinum/γ-alumina interfaces is characterized in a model system of dense γ-alumina embedded with faceted Pt NPs produced by implantation of platinum ions into sapphire followed by thermal annealing in air at 800 °C. Aberration-corrected scanning transmission electron microscopy (STEM) was used to collect atomic-resolution images, which are compared to STEM image simulations of two experimentally-based bulk models of γ-alumina by Smrčok et al. and Zhou and Snyder. A density functional theory (DFT) based model of (111) interfaces with different chemical terminations (O, Al 1 , Al 2 ) of the γ-alumina developed by Oware Sarfo et al. is also compared to experimental STEM data from Pt/γ-alumina interfaces. Further, the Smrčok γ-alumina model provides a better fit than the Zhou structure to the bulk of the γ-alumina. The oxygen-terminated Oware Sarfo model best fits the experimental data and is a very good model close to the interface. However, the fit of the interface model to the experimental data is poorer beyond the third atomic layer in the γ-alumina. This is attributed to compromises required in the design of the model to limit the cell size and computational time for DFT calculations. Understanding the accuracy and limits of the structural models of γ-alumina and Pt/γ-alumina interfaces is important to further the understanding of the structure/property relationships in this system.

36 MATERIALS SCIENCE↗

Ecology and life history of an amoebomastigote, Paratetramitus jugosus, from a microbial mat: new evidence for multiple fission

Five microbial habitats (gypsum crust, gypsum photosynthetic community, Microcoleus mat, Thiocapsa scum, and black mud) were sampled for the presence of the euryhaline, rapidly growing amoebomastigote, Paratetramitus jugosus. Field investigations of microbial mats from Baja California Norte, Mexico, and Salina Bido near Matanzas, Cuba, reveal that P. jugosus is most frequently found in the Thiocapsa layer of microbial mats. Various stages of the life history were studied using phase-contrast, differential-interference, and transmission electron microscopy. Mastigote stages were induced and studied by electron microscopy; mastigotes that actively feed on bacteria bear two or more undulipodia. A three-dimensional drawing of the kinetid ("basal apparatus") based on electron micrographs is presented. Although promitoses were occasionally observed, it is unlikely that they can account for the rapid growth of P. jugosus populations on culture media. Dense, refractile, spherical, and irregular-shaped bodies were seen at all times in all cultures along with small mononucleate (approximately 2-7 micrometers diameter) amoebae. Cytochemical studies employing two different fluorescent stains for DNA (DAPI, mithramycin) verified the presence of DNA in these small bodies. Chromatin-like material seen in electron micrographs within the cytoplasm and blebbing off nuclei were interpreted to the chromatin bodies. Our interpretation, consistent with the data but not proven, is that propagation by multiple fission of released chromatin bodies that become small amoebae may occur in Paratetramitus jugosus. These observations are consistent with descriptions of amoeba propagules in the early literature (Hogue, 1914).

Non-NASA Center↗

Deciphering the Distribution and Crystal-Chemical Environment of Arsenic, Lead, Silica, Phosphorus, Tin, and Zinc in a Porous Ferrihydrite Grain Using Transmission Electron Microscopy and Atom Probe Tomography

Here the interaction of contaminants and nutrients with soil constituents is controlled by processes in intergranular and intragranular pore spaces of organic matter or/and common secondary minerals such as ferrihydrite, ~Fe 3+ 10 O 14 (OH) 2 . This contribution shows that distribution and clustering of the contaminants As, P, Pb, Si, Sn, and Zn in a porous ferrihydrite grain is greatly affected by the heterogeneous size distribution and chemical composition of the pores as well as the ability of their polyhedra to polymerize with the same type of polyhedron. Transmission electron microscopy (TEM) and atom probe tomography (APT) studies are conducted on focused ion beam (FIB) sections extracted from a porous ferrihydrite grain from the smelter-impacted topsoil in Sudbury, Ontario, Canada. The ferrihydrite grain has pore spaces ranging in diameter from tens to hundreds of nanometers. TEM and scanning-TEM studies indicate that the surfaces of the pore walls are enriched in Si. APT data in conjunction with First Near Neighbor (1NN) analyses indicate different degrees of clustering of Pb, As, Sn, Zn, Si, and P within the sample and selected domains. Careful evaluations of 3D atomic plots and 1NN distances indicates the occurrence of polymerized arsenite-, silica-, Sn-, and Zn-polyhedra within pore spaces of the ferrihydrite. Deciphering adsorption, polymerization, and nucleation processes in porous Fe-(hydr)oxides and other soil constituents requires multianalytical approaches and, in this regard, we discuss the advantages and disadvantages of the combination of TEM and APT for characterizing complex environmental samples at the atomic to nanometer scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Benefits of using multiple Raman laser wavelengths for characterizing defects in a UO 2 matrix

Abstract Raman spectroscopy is one of the most useful techniques for studying the structure of UO 2 and changes due to specific defects caused by doping, changes in stoichiometry, irradiation, or heating under oxidizing conditions. In this paper, we illustrate several aspects of the application of Raman techniques to the study of UO 2 , including the use of wavelength‐dependent excitation (455, 532, and 785 nm) to assess the effects of doping (Nd, Th, and Zr), ion irradiation, and in situ heating and oxidation (UO 2 to U 3 O 8 ). Additionally, we show examples of how correlative microscopy is possible using electron backscatter diffraction combined with Raman maps of specific vibration bands or of laser‐induced luminescence generated by rare‐earth dopants in the matrix. For each of these applications, we suggest optimal excitation wavelengths that vary depending on the desired data. Blue (455 nm) excitation tends to promote oxidation even at low powers, but because Raman spectra change little with doping, irradiation‐induced changes are easier to observe. Green (532 nm) excitation is optimal for observing electron–phonon resonance effects in UO 2 and offers a good compromise for high‐temperature oxidation experiments, delivering high‐quality spectra for both UO 2 and U 3 O 8 . Infrared (785 nm) excitation is best for observing “defect” bands associated with doping in UO 2 , as changes with irradiation are small. Raman spectroscopy is particularly suited for studying the stability of UO 2 towards oxidation in the presence of dopants simulating fission products, where electron–phonon resonant effects, dopant ion luminescence, and mapping can be used together to investigate structural rearrangement as a function of temperature. These techniques can offer insight into microstructural changes in UO 2 fuels at higher burnups envisioned in future reactors.

42 ENGINEERING↗

On the Operando Structure of Ruthenium Oxides during the Oxygen Evolution Reaction in Acidic Media

In the search for rational design strategies for oxygen evolution reaction (OER) catalysts, linking the catalyst structure to activity and stability is key. However, highly active catalysts such as IrO x and RuO x undergo structural changes under OER conditions, and hence, structure–activity–stability relationships need to take into account the operando structure of the catalyst. Under the highly anodic conditions of the oxygen evolution reaction (OER), electrocatalysts are often converted into an active form. Here, we studied this activation for amorphous and crystalline ruthenium oxide using X-ray absorption spectroscopy (XAS) and electrochemical scanning electron microscopy (EC-SEM). We tracked the evolution of surface oxygen species in ruthenium oxides while in parallel mapping the oxidation state of the Ru atoms to draw a complete picture of the oxidation events that lead to the OER active structure. Our data show that a large fraction of the OH groups in the oxide are deprotonated under OER conditions, leading to a highly oxidized active material. The oxidation is centered not only on the Ru atoms but also on the oxygen lattice. This oxygen lattice activation is particularly strong for amorphous RuO x . We propose that this property is key for the high activity and low stability observed for amorphous ruthenium oxide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Metal Ion Fluxes Controlling Amphibian Fertilization

Mammalian oocytes undergo major changes in zinc content and localization to be fertilized, the most striking being the rapid exocytosis of over 10 billion zinc ions in what are known as zinc sparks. Here, we report that fertilization of amphibian Xenopus laevis eggs also initiates a zinc spark that progresses across the cell surface in coordination with dynamic calcium waves. This zinc exocytosis is accompanied by a newly recognized loss of intracellular manganese. Synchrotron-based X-ray fluorescence and analytical electron microscopy reveal that zinc and manganese are sequestered in a system of cortical granules that are abundant at the animal pole. Through electron-nuclear double-resonance studies, we rule out Mn2+ complexation with phosphate or nitrogenous ligands in intact eggs, but the data are consistent with a carboxylate coordination environment. Our observations suggest that zinc and manganese fluxes are a conserved feature of fertilization in vertebrates and that they function as part of a physiological block to polyspermy.

Seeler, John F.↗

Molecular insights into photostability of fluorinated organic photovoltaic blends: role of fullerene electron affinity and donor–acceptor miscibility

In this report, the photostability of certain organic photovoltaic (OPV) active layers was demonstrated to improve by as much as a factor of five under white light illumination in air with the use of 1,7-bis-trifluoromethylfullerene (C 60 (CF 3 ) 2 ) as the acceptor in place of PC 60 BM. However, the results were highly dependent on the structure and functionality within the donor material. Twelve combinations of active layer blends were studied, comprised of six different high-performance donor polymers (two fluorinated and four non-fluorinated donors) and two fullerene acceptors (PC 60 BM and C 60 (CF 3 ) 2 ). The relative rates of irreversible photobleaching of the active layer blends were found to correlate well with the electron affinity of the fullerene when the polymer and fullerene were well blended, but a full rationalization of the photobleaching data requires consideration of both the electron affinity of the fullerene as well as the relative miscibility of the polymer–fullerene components in the blend. Miscibility of those components was probed using a combination of time-resolved photoluminescence (TRPL) measurements and scanning tunneling microscopy (STM) imaging. The presence of fluorinated aromatic units in the donor materials tend to promote more intimate mixing with C 60 (CF 3 ) 2 as compared to PC 60 BM. The full results of these photobleaching studies and measurements of donor–acceptor miscibility, considered alongside additional photoconductance measurements and preliminary device work, provide new molecular optimization insights for improving the long-term stability of OPV active layers.

36 MATERIALS SCIENCE↗

Compatability of dispersion-strengthened platinum with resistojet propellants

Resistojets for the Space Station require long life and multipropellant capability. The choice of available materials to meet these requirements is limited. Dispersion-strengthened platinum was selected. Past results indicated that it should be suffieiently inert in candidate propellant environments and should be capable of operating at moderate temperatures for extended periods. A series of propellant compatibility tests was done with platinum strengthened with either yttria or zirconia. Data presented included the results of 1000-hr tests in CO2, H2, ammonia (NH3), N2, steam, hydrazine (N2H4), and methane (CH4); and 2000-hr tests in H2 and NH3. The platinum samples were tested at 1400 C in CO2, H2, NH3, N2, steam, and N2H4; at 500 C in CH4; and at 800 C in N2H4. The mass-loss results indicated material life, exptrapolated from experimental mass-loss data, in excess of 100 000 hr in all environments except steam and N2H4, where it was greater than or =45000 hr. Generally, on the basis of mass loss, there were no compatibility concerns in any of the environments considered. Optical and scanning electron microscopy were used to determine the effect of propellants on the material surface and to evaluate material stability.

Whalen, Margaret V.↗

Tritium Aging Effects on Fracture Toughness of Stainless Steel Weldments

The long-term embrittlement effects of tritium and decay helium on the structural properties of stainless steels have been studied for years at Savannah River to provide required data for establishing safe operating conditions and lifetimes of pressure vessels used to contain tritium gas. In this study, the fracture toughness properties of the longest-aged tritium-precharged stainless-steel base metals and weldments tested at Savannah River were measured and compared to earlier results. The fracture toughness values were the lowest recorded here for tritium-exposed stainless steel. As-forged and as-welded specimens were thermally precharging with tritium gas at 34.5 MPa and 623 K, then aged for up to 17 years to buildin decay helium prior to testing. ASTM J-Integral fracture mechanics analyses, transmission electron microscopy (TEM), and small angle neutron scattering (SANS) examinations were conducted to characterize the effects of tritium and its radioactive decay product, helium-3. Results show that the fracture toughness values were reduced to less than 2-4% of the as-forged values for specimens with more than 1300 appm helium from tritium decay. The trend of decreasing fracture toughness values with increasing helium content was consistent with earlier observations, and the data show that Type 304L stainless steel is more resistant to tritium-induced cracking than Type 21-6-9 stainless at similar decay helium levels. The fracture toughness properties of long-aged weldments were also affected, but the reductions were not as severe over time because weldments did not retain as much tritium as did the base metals. TEM observations were used to characterize the effects of decay helium bubbles on the deformation substructures, but nanometer-sized helium bubbles were not easily resolved because of high dislocation densities within the forged microstructures. SANS results are presented that suggest the technique can provide information on decay helium bubble size, spacing, and distribution in these steels.

36 MATERIALS SCIENCE↗

Crystallization kinetics and thermodynamics of an Ag–In–Sb–Te phase change material using complementary in situ microscopic techniques

The crystallization of an amorphous Ag–In–Sb–Te (AIST) phase change material (PCM) is studied using multiple in situ imaging techniques to directly quantify crystal growth rates over a broad range of temperatures. The measurable growth rates span from ≈ 10 –9 to ≈ 20 m/s. Recent results using dynamic transmission electron microscopy (TEM), a photoemission TEM technique, and TEM with sub-framed imaging are reported here and placed into the context of previous growth rate measurements on AIST. Dynamic TEM experiments show a maximum observed crystal growth rate for as-deposited films to be > 20 m/s. It is shown that crystal growth above the glass transition can be imaged in a TEM through use of subframing and a high-frame-rate direct electron detection camera. Challenges associated with the determination of temperature during in situ TEM experiments are described. Finally, preliminary nanocalorimetry results demonstrate the feasibility of collecting thermodynamic data for crystallization of PCMs with simultaneous TEM imaging.

36 MATERIALS SCIENCE↗

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗

Probe microscopy is all you need *

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

47 OTHER INSTRUMENTATION↗

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

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↗

Quantitative Imaging of Cobalt Phthalocyanine Distribution on Carbon Nanotubes: A Deep Learning Approach to Catalyst Characterization

Electrochemical reduction of carbon dioxide (CO 2 ) offers a pathway to valuable products, with catalysts playing a crucial role. This study investigates the distribution of cobalt tetraaminophthalocyanine (CoPc-NH 2 ) immobilized on carbon nanotubes (CNTs), utilizing high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) to characterize CoPc-NH 2 distribution. A challenge in the quantitative HAADF-STEM analysis is the introduction of bias from manual Co atom identification. To address this, we developed and trained a convolutional neural network (CNN) using a data set generated from images of CoPc-NH 2 /CNT samples with varying Co loadings. The CNN, implemented in TensorFlow and Keras, facilitated Co atom detections. Analysis of the CNN-generated data confirmed a correlation between Co loading and surface density, consistent with findings from UV–vis spectroscopy. Furthermore, the application of Ripley’s L(d) function highlighted the presence of slight Co atom clustering. Furthermore, this work demonstrates the utility of the combined HAADF-STEM and CNN approach for providing spatially resolved information about catalyst distribution on nonplanar supports, revealing structural details that are typically lost through other characterization methods.

HAADF-STEM↗

Machine learning the microscopic form of nematic order in twisted double-bilayer graphene

Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample. Using electronic nematic order in twisted double-bilayer graphene as an example, we show that incorporating correlations between the local density of states at different energies allows convolutional neural networks not only to learn the microscopic nematic order parameter, but also to distinguish it from heterostrain. These results demonstrate that neural networks are a powerful method for investigating the microscopic details of correlated phenomena in moiré systems and beyond.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Determining biomolecular structures near room temperature using X-ray crystallography: concepts, methods and future optimization

For roughly two decades, cryocrystallography has been the overwhelmingly dominant method for determining high-resolution biomolecular structures. Competition from single-particle cryo-electron microscopy and micro-electron diffraction, increased interest in functionally relevant information that may be missing or corrupted in structures determined at cryogenic temperature, and interest in time-resolved studies of the biomolecular response to chemical and optical stimuli have driven renewed interest in data collection at room temperature and, more generally, at temperatures from the protein–solvent glass transition near 200 K to ∼350 K. Fischer has recently reviewed practical methods for room-temperature data collection and analysis [Fischer (2021), Q. Rev. Biophys. 54 , e1]. Here, the key advantages and physical principles of, and methods for, crystallographic data collection at noncryogenic temperatures and some factors relevant to interpreting the resulting data are discussed. For room-temperature data collection to realize its potential within the structural biology toolkit, streamlined and standardized methods for delivering crystals prepared in the home laboratory to the synchrotron and for automated handling and data collection, similar to those for cryocrystallography, should be implemented.

59 BASIC BIOLOGICAL SCIENCES↗

Mineralogy and Microstructures of Shock-Induced Melt Veins in Chondrites

The applicability of phase equilibrium data to the interpretation of shock-induced melt veins can only be tested by a detailed study of melt- vein mineralogy to see how high-pressure assemblages vary as a function of shock conditions inferred from other indicators. We have used transmission electron microscopy (TEM), analytical electron microscopy (AEM), scanning electron microscopy (SEM), electron microprobe analysis (EMA) and optical petrography to characterize the mineralogy, microstructures, and compositions of melt veins and associated high-pressure minerals in shocked chondrites and SNC meteorites. In the processes, we have gained a better understanding of what melt veining can tell us about shock conditions and we have discovered new mineral phases in chondritic and SNC meteorites.

Sharp, Thomas G.↗