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At least 577 records · Page 32

4D-STEM Mapping of Nanocrystal Reaction Dynamics and Heterogeneity in a Graphene Liquid Cell

Chemical reaction kinetics at the nanoscale are intertwined with heterogeneity in structure and composition. However, mapping such heterogeneity in a liquid environment is extremely challenging. Here, in this work, we integrate graphene liquid cell (GLC) transmission electron microscopy and four-dimensional scanning transmission electron microscopy to image the etching dynamics of gold nanorods in the reaction media. Critical to our experiment is the small liquid thickness in a GLC that allows the collection of high-quality electron diffraction patterns at low dose conditions. Machine learning-based data-mining of the diffraction patterns maps the three-dimensional nanocrystal orientation, groups spatial domains of various species in the GLC, and identifies newly generated nanocrystallites during reaction, offering a comprehensive understanding on the reaction mechanism inside a nanoenvironment. This work opens opportunities in probing the interplay of structural properties such as phase and strain with solution-phase reaction dynamics, which is important for applications in catalysis, energy storage, and self-assembly.

four-dimensional scanning transmission electron mi↗

Electrical and structural characterization of in situ MOCVD Al2O3/β-Ga2O3 and Al2O3/β-(AlxGa1−x)2O3 MOSCAPs

This study investigates the electrical and structural properties of metal–oxide–semiconductor capacitors (MOSCAPs) with in situ metal-organic chemical vapor deposition-grown Al2O3 dielectrics deposited at varying temperatures on (010) β-Ga2O3 and β-(AlxGa1−x)2O3 films with different Al compositions. The Al2O3/β-Ga2O3 MOSCAPs exhibited a strong dependence of electrical properties on Al2O3 deposition temperature. At 900 °C, reduced voltage hysteresis (∼0.3 V) with improved reverse breakdown voltage (74.5 V) was observed, corresponding to breakdown fields of 5.01 MV/cm in Al2O3 and 4.11 MV/cm in β-Ga2O3 under reverse bias. In contrast, 650 °C deposition temperature resulted in higher voltage hysteresis (∼3.44 V) and lower reverse breakdown voltage (38.8 V) with breakdown fields of 3.69 and 2.87 MV/cm in Al2O3 and β-Ga2O3, respectively, but exhibited impressive forward breakdown field, increasing from 5.62 MV/cm at 900 °C to 7.25 MV/cm at 650 °C. High-resolution scanning transmission electron microscopy (STEM) revealed improved crystallinity and sharper interfaces at 900 °C, contributing to enhanced reverse breakdown performance. For Al2O3/β-(AlxGa1−x)2O3 MOSCAPs, increasing Al composition (x) from 5.5% to 9.2% reduced net carrier concentration and improved reverse breakdown field contributions from 2.55 to 2.90 MV/cm in β-(AlxGa1−x)2O3 and 2.41 to 3.13 MV/cm in Al2O3. The electric field in Al2O3 dielectric under forward bias breakdown also improved from 5.0 to 5.4 MV/cm as Al composition increased from 5.5% to 9.2%. The STEM imaging confirmed the compositional homogeneity and excellent stoichiometry of both Al2O3 and β-(AlxGa1−x)2O3 layers. These findings demonstrate the robust electrical performance, high breakdown fields, and excellent structural quality of Al2O3/β-Ga2O3 and Al2O3/β-(AlxGa1−x)2O3 MOSCAPs, highlighting their potential for high-power electronic applications.

Bhuiyan, A. F. M. Anhar Uddin (ORCID:0000000334212↗

The X-ray Focusing System at the Time-Resolved AMO Instrument

The emergence of X-ray Free Electron Lasers (XFELs) in recent years has opened up new fields of research, with the merging of short wavelengths and ultrafast timescales. As XFEL facilities including the Linac Coherent Light Source (LCLS) have matured, there is renewed interest in pushing the performance boundaries of experimental endstations, as well as improving ease of operation for experiments. One of the major areas of improvement is that of X-ray micro- and nano-focusing. The focus quality is directly linked to the peak intensity at the interaction point with a sample, and is especially important for intensity-dependent (e.g. x-ray pump) studies and imaging efforts such as single particle imaging.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Distributed Optimization for Nonrigid Nano-Tomography

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist radiation induced deformations during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a distributed optimization solver for tomographic imaging of samples at the nanoscale. Our approach solves the tomography problem jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. We accelerated the solver on multi-GPU systems and validated the method on three nano-imaging experimental data sets.

97 MATHEMATICS AND COMPUTING↗

The Utility of Satellites and Autonomous Remote Sensing Platforms for Monitoring Offshore Aquaculture Farms: A Case Study for Canopy Forming Kelps

The emerging sector of offshore kelp aquaculture represents an opportunity to produce biofuel feedstock to help meet growing energy demand. Giant kelp represents an attractive aquaculture crop due to its rapid growth and production, however precision farming over large scales is required to make this crop economically viable. These demands necessitate high frequency monitoring to ensure outplant success, maximum production, and optimum quality of harvested biomass, while the long distance from shore and large necessary scales of production makes in person monitoring impractical. Remote sensing offers a practical monitoring solution and nascent imaging technologies could be leveraged to provide daily products of the kelp canopy and subsurface structures over unprecedented spatial scales. Here, we evaluate the efficacy of remote sensing from satellites and aerial and underwater autonomous vehicles as potential monitoring platforms for offshore kelp aquaculture farms. Decadal-scale analyses of the Southern California Bight showed that high offshore summertime cloud cover restricts the ability of satellite sensors to provide high frequency direct monitoring of these farms. By contrast, daily monitoring of offshore farms using sensors mounted to aerial and underwater drones seems promising. Small Unoccupied Aircraft Systems (sUAS) carrying lightweight optical sensors can provide estimates of canopy area, density, and tissue nitrogen content on the time and space scales necessary for observing changes in this highly dynamic species. Underwater color imagery can be rapidly classified using deep learning models to identify kelp outplants on a longline farm and high acoustic returns of kelp pneumatocysts from side scan sonar imagery signal an ability to monitor the subsurface development of kelp fronds. Current sensing technologies can be used to develop additional machine learning and spectral algorithms to monitor outplant health and canopy macromolecular content, however future developments in vehicle and infrastructure technologies are necessary to reduce costs and transcend operational limitations for continuous deployment in an offshore setting.

54 ENVIRONMENTAL SCIENCES↗

On the efficacy of post-build thermomechanical treatments to improve properties of Zirconium fabricated using ultrasonic additive manufacturing

Here, hot-isostatic pressing has been applied to Zirconium plate fabricated using the ultrasonic additive manufacturing (UAM) technique to enhance interfacial bond quality. Specimens heated to 800 C for 1 h at 100 MPa pressure showed grain growth across many prior foil-to-foil interfaces, thereby increasing foil adhesion. In addition to the material softening induced by a loss of Hall-Petch strengthening, premature failure of specimens loaded parallel to the build direction was observed. Premature specimen failure was attributed to the local delamination at prior foil-to-foil boundaries where grain growth was pinned by Ti impurities introduced during the UAM process via the interaction between a Ti-alloy buffer foil and the welded Zr-foils underneath. In addition, the presence of Ti along select foil interfaces resulted in the nucleation and growth of secondary (Zr,Ti)(Fe,Cr) laves phases during higher-temperature thermomechanical processing. Using a combination of micro-scale X-ray computed tomography, fractography, and in-situ digital image correlation, the effect of defects along prior foil-to-foil boundaries was revealed as delamination-assisted plasticity accelerated specimen failure in preferred tensile orientations. These findings underscore the importance of impurity control when optimizing weld quality of higher-strength material systems using ultrasonic welding.

36 MATERIALS SCIENCE↗

Embedding Fiber Optic Sensors in Stainless Steel using Spark Plasma Sintering for Structural Health Monitoring in Harsh Environments

Embedded fiber optic sensors such as fiber Bragg gratings (FBGs) offer a unique route for distributed real-time in-situ imaging of various engineering parameters for numerous purposes. This study advanced the current sensor embedding approaches by exploring a spark plasma sintering (SPS)-assisted technology to embed FBGs in high-temperature structural materials and demonstrated the capability of temperature measurement. In this approach, single-mode FBGs were integrated into stainless steel (SS) 316L components using SPS, followed by the evaluation of the bonding quality between the FBGs and matrix, the optical attenuation of the fibers induced by embedding, and the sensing characters of the FBGs under temperature stimuli. The results demonstrated that superior bonding was achieved between the FBGs and highly-densified SS316L. Examination of the behavior of Bragg gratings validated signal fidelity after embedding. Real-time thermal imaging under temperature cycling using the FBGs demonstrated the effectiveness of the technique for smart materials manufacturing.

36 - MATERIALS SCIENCE↗

Ontology Development Kit: a toolkit for building, maintaining and standardizing biomedical ontologies

Similar to managing software packages, managing the ontology life cycle involves multiple complex workflows such as preparing releases, continuous quality control checking and dependency management. To manage these processes, a diverse set of tools is required, from command-line utilities to powerful ontology-engineering environmentsr. Particularly in the biomedical domain, which has developed a set of highly diverse yet inter-dependent ontologies, standardizing release practices and metadata and establishing shared quality standards are crucial to enable interoperability. The Ontology Development Kit (ODK) provides a set of standardized, customizable and automatically executable workflows, and packages all required tooling in a single Docker image. In this paper, we provide an overview of how the ODK works, show how it is used in practice and describe how we envision it driving standardization efforts in our community.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Galaxy deblending using residual dense neural networks

We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic galaxy images similar to the typical arrangements of field galaxies with a finite point spread function (PSF) and realistic noise levels. The main novelty of our approach is the usage of two distinct neural networks: i) a deblending network which isolates a single galaxy postage stamp from the composite and, ii) a classifier network which counts the remaining number of galaxies. The deblending proceeds by iteratively peeling one galaxy at a time from the composite until the image contains no further objects as determined by the classifier, or by other stopping criteria. By looking at the consistency in the outputs of the two networks, we can assess the quality of the deblending. Further, we characterize the flux and shape reconstructions in different quality bins and compare our deblender with the industry standard, SExtractor. We also discuss possible future extensions for the project with variable PSFs and noise levels.

79 ASTRONOMY AND ASTROPHYSICS↗

Chatbots can guide experimentalists to avoid many common mistakes in measuring fluorescence spectra

The concepts of fluorescence spectroscopy are well established, yet the experimental collection of a spectrum is susceptible to a range of errors. A recent study showed that chatbots – derived from substantial advances in artificial intelligence – can provide practical assistance to the experimentalist during collection of absorption spectra and thereby achieve improved quality. Fluorescence, while the complement of absorption, is prone to a far richer array of experimental errors encompassing environmental factors, instrumental settings, and experimentalist mistakes. Here, 10 chatbots (ChatGPT 5, Gemini 2.5 Pro, Microsoft Copilot, Meta AI Llama 4, Claude Sonnet 4.5, X.ai Grok 4, Perplexity Pro, DeepSeek V3.1, Z.ai GLM-4.6, and KIMI K2) have been evaluated for the suitability in providing quality-improvement advice to the experimentalist during acquisition of fluorescence spectra. Significant opportunities for practical assistance are noted, although intervention by a domain expert is often required. Here, if combined with image recognition and/or signal processing, chatbots embedded in instruments may enable real-time guidance during fluorescence data acquisition.

Artificial intelligence↗

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗

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↗

Thermal tomography 3D imaging of additively manufactured metallic structures

Thermal tomography is a computational method for heat diffusion-based imaging of solids, which provides 3D visualization of data from flash thermography measurements. We investigate thermal tomography imaging and nondestructive evaluation of stainless steel and nickel super alloy metallic structures produced with the laser powder bed fusion (LPBF) additive manufacturing (AM) process. Metallic structures produced with LPBF contain defects, and there are limited capabilities to evaluate these structures non-destructively. Thermal tomography reconstruction of 3D apparent spatial effusivity provides information about AM structure geometry and internal material flaws. We study performance of thermal tomography in imaging of metallic structures through COMSOL computer simulations of transient heat transfer and through reconstruction of data obtained from experimental measurements. Thermal tomography reconstructions of structure shape and dimensions are shown for the Inconel 718 AM structure which has variations in the horizontal plane but is uniform along the depth dimension. Reconstruction of internal defects is investigated using a stainless steel 316L specimen with flat bottom hole (FBH) indentations, and the Inconel 718 plate is produced with the LPBF method, which contains imprinted hemispherical shape low density regions containing non-sintered metallic powder. The FBHs have the same sizes as the imprinted defects in the LPBF specimens but offer better imaging contrast. Thermal tomography reconstructions provide visualizations of internal defects and allow for estimation of their sizes and locations. Results of this study demonstrate that thermal tomography can be used for visualization and quality control in AM.

36 MATERIALS SCIENCE↗

Evaluation of XCT for Matrix Density Measurement of Particle Fuel Forms

Particle fuel forms generally consist of a dispersion of fuel, such as tristructural isotropic (TRISO) particles, within a refractory matrix (e.g., graphite or silicon carbide). The density of matrix materials for particle fuel forms is of interest for modeling fuel form strength and thermal properties and may be specified as a quality control parameter, depending on reactor design. Some of the uncertainty associated with traditional, manual approaches can be eliminated by performing x-ray computed tomography (XCT) on the fuel forms and applying image processing methods to generate a precise count of the number of particles. This also removes the need to include determination of particle count within each individual fuel form during fabrication. Unfortunately, reconstruction artifacts from high-Z uranium-bearing kernels prevent accurate measurement of individual particle volumes using this approach, so the use of mean particle mass and volume are still necessary for computation of average fuel form matrix density. This method of using XCT to count particles in individual fuel form for determination of average matrix density was applied to three archived compacts from the Advanced Gas Reactor Fuel Development and Qualification (AGR)-1 campaign, four archived compacts with uranium carbide/uranium oxide (UCO) TRISO from the AGR-2 campaign, and three archived UO 2 -TRISO compacts from the AGR-2 campaign. The resulting density values were compared with those previously reported, showing slight changes due to uncertainties in the previously used number of particles in each of these cylindrical, graphite matrix compacts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Morphologically-Directed Raman Spectroscopy as an Analytical Method for Subvisible Particle Characterization in Therapeutic Protein Product Quality

Abstract Subvisible particles (SVPs) are a critical quality attribute of injectable therapeutic proteins (TPs) that needs to be controlled due to potential risks associated with drug product quality. The current compendial methods routinely used to analyze SVPs for lot release provide information on particle size and count. However, chemical identification of individual particles is also important to address root-cause analysis. Herein, we introduce Morphologically-Directed Raman Spectroscopy (MDRS) for SVP characterization of TPs. The following particles were used for method development: (1) polystyrene microspheres, a traditional standard used in industry; (2) photolithographic (SU-8); and (3) ethylene tetrafluoroethylene (ETFE) particles, candidate reference materials developed by NIST. In our study, MDRS rendered high-resolution images for the ETFE particles (> 90%) ranging from 19 to 100 μm in size, covering most of SVP range, and generated comparable morphology data to flow imaging microscopy. Our method was applied to characterize particles formed in stressed TPs and was able to chemically identify individual particles using Raman spectroscopy. MDRS was able to compare morphology and transparency properties of proteinaceous particles with reference materials. The data suggests MDRS may complement the current TPs SVP analysis system and product quality characterization workflow throughout development and commercial lifecycle.

Science & Technology - Other Topics↗

Single‐Step Deformation Processing of Ultrathin Lithium Foil and Strip

Abstract Next‐generation, high‐efficiency energy storage and conversion systems require development of lithium metal batteries. But the high cost of production and constraints on thickness of lithium (anode) foils continue to limit adoption for integration into battery cell architectures. Here, a novel lithium anode manufacturing solution is demonstrated – single‐step production of ultrathin gauge foil formats directly from solid ingot. Hybrid cutting‐based deformation processes, involving large plastic strains and strain rates, produce foil to sub‐10 µm thickness, with surface quality even superior to present Li anode processing routes. Energy analysis shows the single‐stage processing is ≈50% more efficient than conventional processing by extrusion‐rolling. Through in situ force measurements and high‐speed imaging of the cutting it also characterize – for the first time – the flow stress of Li to strain rates of 800 sec −1 , revealing a power‐law relationship. The results present a paradigm shift in manufacturing and integration of solid lithium anodes for energy applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗