An experimental toolbox for the physical characterization of thermal insulating polymeric foams
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Uranium-molybdenum (U-Mo) alloys show promise as a nuclear fuel system due to their high thermal conductivity and fuel loading capability. However, U-Mo systems are susceptible to irradiation induced swelling ultimately affecting the cladding via mechanical and chemical interactions. To address these shortcomings, this research investigated the formation of uranium mononitride (UN) nanoparticles within a 90 wt% U/10 wt% Mo (U-10Mo) matrix to act as a prospective defect sink for fission products at nanometric hetero-interfaces. To promote the formation of UN, U-10Mo powders were mechanically alloyed under a high purity nitrogen atmosphere. Variations of the milling process investigated included media size, duration of milling, and number of times the milling jar was re-aerated with nitrogen gas. Characterization of the fuel microstructure was completed using light element analysis, X-ray diffraction, scanning and transmission-electron microscopy, electron energy loss spectroscopy, and atom probe tomography. UN nanoparticles measuring 1–5 nm in radius were observed in the U-Mo matrix as early as 1 h into the mechanical alloying process. Milling time in excess of 10 h was found to lead to deleterious effects induced by the stainless-steel milling media.
Contact nucleation is believed to play a role in liquid-to-solid phase transitions in the atmosphere including ice nucleation and salt efflorescence. Here, for this work, contact efflorescence of optically levitated ammonium sulfate droplets by collisions with organic particles is probed using a long working-distance optical trap. Two highly viscous water-soluble organic compounds (d-(+)-raffinose and citric acid), and two insoluble highly surface-active organic compounds (stearic acid and cis-pinonic acid) were probed for their ability to induce efflorescence upon contact. While three of the organics showed minimal effectiveness as contact nuclei, cis-pinonic acid showed a remarkable ability to initiate contact efflorescence of ammonium sulfate, occurring near ammonium sulfate’s deliquescence relative humidity. Further analysis of cis-pinonic acid using bright-field microscopy in an electrodynamic balance provided evidence that the cis-pinonic acid particles are crystalline under the laboratory conditions. We suggest that the close lattice match between crystalline ammonium sulfate and crystalline cis-pinonic acid may account for the observed effectiveness in initiating contact efflorescence of ammonium sulfate. In contrast, tests of contact nucleation between cis-pinonic acid and sodium chloride, a pair with a poor lattice match, did not result in efflorescence. These findings suggest that crystalline organic compounds in the atmosphere could act as effective nuclei for contact efflorescence of atmospherically relevant salts, provided they share a compatible lattice structure.
Lithium–sulfur batteries (LSBs) are extensively researched for their high energy densities but are hindered by the lithium polysulfide (LiPS) shuttling effect, which results in poor cyclability. A popular mitigation strategy is separator modification, where a LiPS trapping material is slurry-coated onto a conventional microporous polypropylene (PP) separator. This additional mass and volume unfortunately compromise the overall energy density of the LSB. This study aims to take a separator modification approach that avoids this issue. Nanoporous atomically thin membranes (NATMs) made of graphene are gaining attention for their scalable synthesis, tunable pore size, and negligible pore length. Herein, we apply a well-characterized graphene NATM for reasons similar to those of a size-selective interlayer in LSBs. The tailored pore size of ∼0.7–1.0 nm and atomic thinness facilitate the passage of Li + (solvated ionic diameters ∼0.54–1.26 nm) and blockage of larger LiPS (solvated ionic diameters ∼0.81–1.69 nm) without adding significant impedances or mass. The sulfur confinement is confirmed through scanning electron microscopy and energy-dispersive X-ray spectroscopy elemental analysis of the Li anode. An LSB with a NATM@PP separator shows virtually no capacity loss over 150 cycles, demonstrating efficacy of size-selective molecular sieving using NATMs in LSBs.
Nuclear materials often evolve into two-phase systems comprising a bulk matrix with dispersed inert-gas bubbles. The presence of these bubbles can have consequences to the thermomechanical response of materials and is a key life-limiting factor in some nuclear fuel forms. Understanding the behavior of these two-phase, bubble-matrix systems is, thus, important to improved predictive models and frameworks for many nuclear materials applications. While temperature excursions of these two-phase systems have been characterized, fewer studies have focused on the evolution of inert-gas bubbles under pressure. Here, in this paper, we use x-ray tools to interrogate a He-implanted gold foil to determine the pressure-dependent evolution of the individual components (Au matrix + bubbles), and we compare that total pressure dependence to theoretical equation-of-state descriptions based on mixing rules.
This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.
The US Department of Energy Microreactor Program is developing compact, high-temperature microreactors that require robust neutron moderators. Known for its high hydrogen retention and structural stability, YH x is a leading candidate for this purpose. As part of an ongoing evaluation, Oak Ridge National Laboratory conducted post-irradiation examinations on YH x specimens irradiated in the High Flux Isotope Reactor under the legacy Transformational Challenge Reactor program, focusing on their structural integrity, hydrogen retention, and thermal properties. The post-irradiation examination campaign primarily examined specimens from two irradiation campaigns, covering a range of hydrogen-to-yttrium atomic ratios (H/Y), neutron damage levels (0.1–2 displacements per atom), and targeted irradiation temperature of 600°C. The investigations included electron microscopy, high-energy x-ray diffraction, laser flash analysis, differential scanning calorimetry, and SiC thermometry analysis.
High-resolution imaging using Transmission Electron Microscopy (TEM) is essential for applications such as grain boundary analysis, microchip defect characterization, and biological imaging. However, TEM images are often compromised by electron energy spread and other factors. In TEM mode, where the objective and projector lenses are positioned downstream of the sample, electron–sample interactions cause energy loss, which adversely impacts image quality and resolution. This study introduces a simulation tool to estimate the electron energy loss spectrum (EELS) as a function of sample thickness, covering electron beam energies from 300 keV to 3 MeV. Leveraging recent advances in MeV-TEM/STEM technology, which includes a state-of-the-art electron source with 2-picometer emittance, an energy spread of 3 × 10 -5 , and optimized beam characteristics, we aim to minimize energy spread. By integrating EELS capabilities into the BNL Monte Carlo (MC) simulation code for thicker samples, we evaluate electron beam parameters to mitigate energy spread resulting from electron–sample interactions. Based on our simulations, we propose an experimental procedure for quantitively distinguishing between elastic and inelastic scattering. The findings will guide the selection of optimal beam settings, thereby enhancing resolution for nanoimaging of thick biological samples and microchips.
Geometric frustration—where geometry prevents simultaneous satisfaction of local interactions—generates pseudosymmetry and emergent behaviors across physical and biological systems. At the nanoscale, pseudosymmetric features in crystalline materials manifest as local strain and distortion, but how they depend on particle size and control structural stability remains unclear. Here, we report the first study of a size-dependent crossover in pseudosymmetry in multi-twinned gold nanoparticles (NPs), combining four-dimensional scanning transmission electron microscopy with nanoscale strain mapping grounded in continuum solid mechanics. Analysis of more than 20 decahedral NPs (20–55 nm) reveals pronounced heterogeneity in multiple modes of in-plane strain and displacement field in small NPs as five tetrahedral grains close the geometric gap, without extended defects. With increasing particle size, strain fields homogenize across grains and local phases shift from predominantly low-symmetry body-centered tetragonal motifs at small sizes to face-centered cubic character approaching the bulk limit. We identify a crossover particle size of ~35 nm, well below bulk, correlating with a transition from modified-Wulff shapes to pentagonal bipyramids, consistent with finite element predictions. This quantitative framework for mapping size-dependent strain and pseudosymmetry enables precise design and control of functional crystalline solids and phase transformation for catalysis, photonics, electronics, and energy storage.
Arabidopsis GALACTURONOSYLTRANSFERASE1 (GAUT1) synthesizes homogalacturonan (HG), the most abundant pectin in growing plant cells. GAUT1 has the greatest in vitro enzyme activity of the six confirmed Arabidopsis HG biosynthetic GAUTs, but its biological activity remains elusive. Here we show that Arabidopsis GAUT1 homozygous mutants have a severe dwarfed seedling phenotype, survive several weeks as 2 to 3 mm seedlings, and have severely reduced shoot and root growth and hypocotyl epidermal, cortex and endodermal cell size. gaut1-1 pollen tubes are shorter than WT with increased bursting. Complementation of homozygous gaut1-1 with GAUT1 coding sequence driven by the GAUT1 promoter restored WT-like growth. The extreme dwarf phenotype of homozygous gaut1-1 seedlings precluded their use for detailed cell wall analysis, thus suspensions cultures were produced from callus generated from mutant and WT seedlings. Homozygous gaut1-1 suspension cells were smaller than WT with ∼30% reduced wall GalA content compared to WT. Sequential extraction of the walls with increasingly harsh solvents and sugar composition analysis revealed reduced GalA content in only the 4M KOH post-chlorite fraction, indicating that GAUT1-synthesized HG was held tightly in the wall by direct or indirect hydrogen bonding and/or oxidation-sensitive linkages. Treatment of wall fractions with endopolygalacturonase to hydrolyze HG and gel electrophoretic separation of hydrolysates exposed an HG-associated doublet band markedly downregulated in the homozygous gaut1-1 4M KOH post-chlorite fraction and to a lesser extent in 4M KOH and sodium chlorite fractions. NMR analysis identified the band as rhamnogalacturonan (RG)-II. Super resolution microscopy using anti-HG antibodies showed that, compared to WT, the homozygous gaut1-1 hypocotyl epidermal and callus cells had reduced content and length of HG nanofilaments, HG fibers associated with cell expansion in Arabidopsis. The results demonstrate that GAUT1-synthesized HG resides in a tightly-cell-wall-bound, RG-II-containing polymer required for HG nanofilament formation and seedling cell expansion.
This dataset contains atomic force microscopy images associated with the manuscript "Nanometer Scale Imaging to Develop Quantitative Descriptors of Bipolar Membrane Junction Structure" by Maria Kelly, Emily R. Dunn, Ellis A. Spickermann, Josephine N. Gruber, César A. Lasalde-Ramírez, P. N. Romero Zavala, Éowyn Lucas, Ankur Gupta, Harry A. Atwater, and Wilson A. Smith. The dataset contains both raw images as well as segmented images produced by the image processing workflow described in the manuscript. A readme file and meta data file are included to provide additional details regarding the sample identity, image acquisition parameters, and file naming scheme.
Silicon carbide (SiC) is a widely preferred material within many industries due to its favorable properties, most notably its low electrical resistivity at high temperatures, excellent thermal conductivity, and sturdy mechanical properties. Doping, particularly n-type doping, is shown to extremely reduce electrical resistivity, but due to structural changes within the lattice caused by interaction with thermal neutrons, it is possible that other attributes of SiC may also be affected. In addition to review of past literature and data, four-point probe testing, scanning electron microscopy, nanoindentation, strength tests, differential scanning calorimetry and laser flash analysis were used to investigate the effects of n-doping 3C ß-phase SiC. According to temperature dependent measurements, electric resistivity and thermal conductivity both declined as dopant levels increases. Dopant levels are shown to have a significant effect on the mechanical performance of SiC, with the highest dopant levels (4 x 1018 cm-3) providing a 40% decrease in elastic modulus from 420 GPa to 258 GPa and a 30% decrease in hardness from 40 GPa to 27.7 GPa. These values are still above average and doped SiC may prove valuable for nuclear applications.
The transmission X-ray microscopy (TXM) based X-ray absorption near-edge structure (XANES) technique provides three-dimensional mapping of element-specific chemical states at nanometer-scale spatial resolution and micrometer-scale fields of view. However, compared to conventional volume-averaged XANES (VA-XANES) measurements, the inherently small voxel size in TXM-XANES leads to a lower signal-to-noise ratio, making full-spectrum analysis computationally demanding and less robust. Here, we present the structural and compositional conditions for a statistical white-line analysis framework under which chemical state information can be directly extracted from the white-line peak position in voxel spectra without the need for voxel-wise background subtraction or normalization, under well-defined structural and compositional conditions. The method is validated on layered oxide cathode materials, where low-order polynomial fitting accurately reproduces white-line features, and the extracted energy distributions correlate strongly with VA-XANES results. This statistical approach enables high-throughput, dose-efficient, and noise-robust chemical state quantification in TXM-XANES, offering broad applicability to functional materials requiring nanoscale oxidation-state mapping.
Rubisco activase (Rca) is a critical AAA+ ATPase protein complex that remodels and promotes the Rubisco enzyme, a key player in photosynthetic performance and carbon fixation. The assembly and function of the Rca protein complex are regulated by a range of factors, including subunit concentration, nucleotide-binding states, thermal conditions, metal-ion coordination, and post-translational modifications, such as phosphorylation. Despite its importance in photosynthesis, the detailed molecular mechanisms underlying the regulation of plant Rca and how it activates Rubisco remain elusive. This project aims to bridge this knowledge gap by integrating sophisticated enzymology tools with single-molecule methods and high-resolution electron microscopy to elucidate the structure and function of plant Rca. Through these multiple approaches, we have systematically investigated how the activity of plant Rca is impacted by various factors, such as phosphorylation and metal-ion coordination. The Rca complex assembly/disassembly dynamics were captured using anti-Brownian electrokinetic (ABEL) trap-based measurements, providing unprecedented insight into its structural flexibility and diverse assembly states. Furthermore, the structural analysis of Rca through electron crystallography and single-particle cryogenic electron microscopy (cryo-EM) reveals novel assembly states of the spinach Rca, providing insight into the mechanistic action for Rubisco remodeling. By combining cutting-edge tools and approaches, this work uncovers critical aspects of Rca’s regulation and assembly, paving the way for a deeper understanding of its role in photosynthetic efficiency and the potential for enhancing carbon fixation in crops.
Manual labeling for machine learning tasks such as image classification is tedious and labor-intensive; as a result, scientific datasets suitable for deep learning applications are scarce and limited. While data augmentation techniques have shown promise for extending image datasets, very little work has been done to understand the impact of combining multiple augmentation methods sequentially or the limits of their effectiveness when combined. Our work addresses this gap by examining how standard and combinatorial data augmentation affects the performance of machine learning models when trained on small datasets for label classification tasks. For our analysis, we generate single, double and quadruple-augmented datasets for a microscopy image classification task using six standard augmentation methods, and compare the resultant improvements observed in binary classification accuracy with three standard image classification models (DenseNet169, MobileNetV2, ResNet101V2). Our experiments show a non-monotonic relationship between the number of simultaneous augmentation methods and classification accuracy, indicating that there is a trade-off between the degree of augmentation and the model performance. These findings suggest that the optimal number of augmentation methods will vary by domain and use case. We also find that the order in which augmentation methods are applied to a limited dataset matters when combining augmentation schemes, with our use case showing performance differences up to 2.6% when the augmentation order is reversed for double-augmented datasets. Our work offers insights to the limits of data augmentation when working on image classification tasks with limited datasets.
Shales are a central component of petroleum systems, as source, seal, and unconventional reservoir rocks. Unlike traditional unconventional shale, a newly emerging Caney Shale play is regarded as an unconventional of unconventional shales (UUS), due to its high content of fine-grained materials, lower reservoir porosity, dominance of nanopores, and a scarcity of visible natural fractures. Other developed unconventional shales such as the Woodford and Barnett have larger average pore size, higher porosity, and extensive natural fractures at core scale that contribute to reservoir quality. In this work, the Caney Shale was cored in entirety to characterize its interbedded ductile and reservoir intervals and establish criteria for their recognition. Representative ductile and reservoir intervals known as D2 and R3, respectively, were selected for detailed analysis including variations in elemental composition, mineralogy, facies, and the presence of natural fractures at well and core scales using X-ray fluorescence, X-ray diffraction, and thin section and core description. Characteristics of microstructure and microgeochemistry at nano- and microscales are compared using field emission scanning electron microscopy with energy dispersive spectroscopy and lowpressure nitrogen adsorption isotherms with fractal dimension analysis. The ductile (detrital clay-rich) member D2 is characterized by higher concentrations of Ti and Al and lower Si, while the reservoir R3 (possibly biogenic silica-accumulated) is characterized by lower Ti and Al and higher Si. Eight mixed carbonate–siliciclastic facies are recognized, and R3 shows a higher heterogeneity of facies stacking and average fracture abundance than D2. Further, R3 shows a more microscopically heterogeneous fabric/texture of matrix and a higher microporosity than D2 that has a higher pore surface heterogeneity. A fundamental understanding of the compositional and microstructural characteristics of UUS and further ductile/reservoir intervals will allow for a better assessment of reservoir quality, more effective production of hydrocarbons, and optimized selection of safe caprocks in carbon sequestration and subsurface hydrogen storage.
Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT’s hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.
An electrode in cylindrical or pouch cell batteries contains millions of active particles embedded in a conductive network. Battery performance, such as voltage, capacity, and cyclic efficiency, is a collective response of the particle network. We use optical microscopy to measure the local, heterogeneous state of charge of individual particles upon charging and discharging. The optical reflectivity is proportional to Li composition in the ternary oxide LiNi x Mn y Co z O 2 (NMC) cathode. Through clustering analysis, we determine the scale of heterogeneity where a representative volume in the composite electrode contains 100 to 1,000 particles. The heterogeneous activity in the particle network can be described by Weibull defect population at the particle interface with the conductive matrix.