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At least 343 records · Page 19

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DOE OSTI2024

Small, solubilized platinum nanocrystals consist of an ordered core surrounded by mobile surface atoms

In situ structures of Platinum (Pt) nanoparticles (NPs) can be determined with graphene liquid cell transmission electron microscopy. Atomic-scale three-dimensional structural information about their physiochemical properties in solution is critical for understanding their chemical function. We here analyze eight atomic-resolution maps of small (<3 nm) colloidal Pt NPs. Their structures are composed of an ordered crystalline core surrounded by surface atoms with comparatively high mobility. 3D reconstructions calculated from cumulative doses of 8500 and 17,000 electrons/pixel, respectively, are characterized in terms of loss of atomic densities and atomic displacements. Less than 5% of the total number of atoms are lost due to dissolution or knock-on damage in five of the structures analyzed, whereas 10–16% are lost in the remaining three. Less than 5% of the atomic positions are displaced due to the increased electron irradiation in all structures. The surface dynamics will play a critical role in the diverse catalytic function of Pt NPs and must be considered in efforts to model Pt NP function computationally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Redox Mechanism in Na-Ion Battery Cathodes Probed by Advanced Soft X-Ray Spectroscopy

A Na-ion battery (NIB) device is a promising solution for mid-/large-scale energy storage, with the advantages of material abundance, low cost, and environmental benignity. To improve the NIB capacity and retainability, extensive efforts have been put into the developments of NIB electrode materials. The redox activities of the transition metal (TM)-based NIB electrodes are critical in defining the capacity and stability. Here, we provide a comprehensive review on recent studies of the redox mechanisms of NIB cathodes through synchrotron-based soft X-ray absorption spectroscopy (sXAS) and mapping of resonant inelastic X-ray scattering (mRIXS). These soft X-ray techniques are direct and effective tools to fingerprint the TM-3d and O-p states with both bulk and surface sensitivities. Particularly, 3d TM L-edge sXAS has been used to quantify the cationic redox contributions to the electrochemical property; however, it suffers from lineshape distortion for the bulk sensitive signals in some scenarios. With the new dimension of information along the emitted photon energy, mRIXS can address the distortion issue of in TM-L sXAS; moreover, it also breaks through the limitation of conventional sXAS on detecting unconventional TM and O states, e.g., Mn(I) in NIB anode and oxidized oxygen in NIB cathodes. The mRIXS fingerprint of the oxidized oxygen state enables the detection of the reversibility of the oxygen redox reaction through the evolution of feature intensity upon electrochemical cycling and thus clarifies various misunderstandings in our conventional wisdom. We conclude that, with mRIXS established as a powerful tool, its potential and power will continue to be explored for characterizing novel chemical states in NIB electrodes.

25 ENERGY STORAGE↗

DOE OSTI2023

The high intensity diffractometer for residual stress analysis (HIDRA), a third generation residual stress mapping neutron diffractometer at the high flux isotope reactor

This paper describes the hardware and software upgrades, operation, and performance of the high intensity diffractometer for residual stress analysis (HIDRA) instrument, a residual stress mapping neutron diffractometer located at the High Flux Isotope Reactor at Oak Ridge National Laboratory in Oak Ridge Tennessee, USA. Following a major upgrade in 2018, the new instrument has a single 3 He multiwire 2D 30 × 30 cm 2 position sensitive detector, yielding a field of view of 17° 2θ. Further, the increase in the field of view (from 4° 2θ) from the previous model instrument has contributed to the tremendous improvement in the out of plane solid angle such that the 3D count rate could be obtained easily. Accordingly, the hardware, software, Data Acquisition System (DAS), and so on have also been updated. Finally, all these enhanced features of HIDRA have been ably demonstrated by conducting multi directional diffraction measurements in the quenched 750-T74 aluminum, and the evolved and improved strain/stress mappings are presented.

3D printing↗

DOE OSTI2026

Magnetic flux imaging in a 3D superconductor integrated circuit

We report on imaging magnetic flux distributions in a multilayered superconductor integrated circuit which emerge during magnetization and after field cooling of the circuit in the DC magnetic field. The obtained complicated field maps expose the flux propagation across the patterned superconducting ground planes sandwiching layers with Josephson junction-based logic cells, fine wire grid around the functional units, and multiple superconducting fill structures located in different inner layers. The observed intricate flux distributions are explained by specific patterns of Meissner screening currents and superconducting critical currents in different mutually interacting parts of the integrated circuit. Our results provide important insights into possible ways of improving the protection of superconductor integrated circuits from magnetic fields and their resilience against flux trapping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Local Lattice Deformation of Tellurene Grain Boundaries by Four-Dimensional Electron Microscopy

Two-dimensional (2D) tellurene is a promising competitor for the fabrication of ultrathin optoelectronic devices belonging to a new family of monoelemental 2D materials. Furthermore, the precise fabrication and characterization of tellurene and its lattice defects is of utmost importance to determine device reliability and predict functionality, yet it remains experimentally challenging. The rapid growth of the four-dimensional scanning transmission electron microscopy (4D-STEM) technique as well as postprocessing tools now allows for structural analysis with nanometer-scale resolution over a broad range of length scales. Here, we use 4D-STEM to reveal the three-dimensional (3D) atomic structure of the characteristic grain boundary in 2D tellurium formed through a new microwave-enabled chemical self-assembly. Strain and lattice parameter maps permit the reconstruction of the roughness of the grain boundary and suggest that its formation is promoted by a wedge of helical atomic chains along the [0001] crystallographic direction. The observation of wrinkles at the grain boundary is found to be the dominant relaxation mechanism. Insights into the formation mechanism are elucidated by mapping the lattice parameters as the first demonstration of local in-plane and out-of-plane unit cell variation in a nanometer-by-nanometer real-space array.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Radiation Mapping UAV [Poster]

The NuMI (Neutrinos at the Main Injector) target system delivers a high-energy flux of muon-neutrinos towards a detector more than 800 km away with the goal of better understanding the origin of the universe and its evolution. The target system becomes radioactive because it is irradiated by a sub-Mega-Watt power proton beam. Radiological hazard is one of critical issues for a radiological worker which limits the iractivities near the target system; however, they often need to quantify the radiation level of the target system manually. Thus, the objective of this project is to reduce their exposure to radiation while improving the accuracy of the measurements. A quadcopter UAV fitted with a radiation dosimeter is ideal to traverse the irradiated environment as it can be deployed in any space and requires minimal hardware integration to the test room. Due to the lack of indoor GPS signal, this UAV utilizes a series of ultrasonic beacons that release several varied-frequency pulses simultaneously in order to localize the drone. The flight controller returns a 3D coordinate tied to the radiation data to understand precisely where radiation spikes are being emitted from the horn. The flight controller stores log data that can be processed into an easily interpretable map of radiation intensities. The radiation map serves as one of the primary references for planning the transportation of the NuMI horn.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images

Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A simple, validated approach for design of two-dimensional periodic particle patterns via acoustophoresis

Two-dimensional patterning of microparticles enables a wide range of functional materials, including patterned energy storage electrodes, flexible electronics, and sensor arrays. Particle patterning via acoustics offers an attractive path to generate a wide variety of 2D periodic patterns that introduce tailorable hierarchical porosity, useful for controlling surface area, transport distances, and other properties. This method is most effective with micron scale particles and patterns of tens to hundreds of microns. To enable systematic exploration of the broad design space for such patterns, this work develops a model of 2D and 3D assembly of particles at high loadings and validates the obtained patterns against both experiments and more computationally intensive modeling techniques. Using this simple model, connections are mapped between input parameters (like actuation conditions, particle volume fraction, material properties) and output geometrical features (like void size and shape, pattern connectivity, and surface area) so that they can be tailored to given applications. The utility of this simple model is illustrated by predicting and then experimentally demonstrating new hierarchical patterns resulting from multiple waves of different frequencies interacting. These multiscale patterns offer the potential to lift the limits on surface area, diffusion distances, and other features.

42 ENGINEERING↗

DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗

DOE OSTI2021

Neutron Irradiation Induced Changes in Isotopic Abundance of 6Li and 3D Nanoscale Distribution of Tritium in LiAlO2 Pellets Analyzed by Atom Probe Tomography

Tritium, a radioactive isotope of hydrogen is produced by neutron irradiation of 6Li enriched LiAlO2 pellets in Tritium producing burnable absorber rods (TPBARs). Three-dimensional nanoscale mapping of Tritium in irradiated pellets is critical to understand its spatial variation and its association with various microstructural features in the pellets. Spatially resolved analysis of 6Li isotopic enrichment in such ceramic pellets before and after irradiation can provide insights to heterogeneities in Li isotopic distribution. However, Li being a light isotope, its spatially resolved compositional or isotopic analysis is a challenging task to most analytical methods. Here we used atom probe tomography, to evaluate the Li isotopic enrichment in both the LiAlO¬2 matrix and the Li deficient secondary phase LiAl5O8 before irradiation. These results were then compared with 6Li isotopic ratio measured by APT analysis of neutron irradiated pellets to clearly demonstrate the unique capability of APT to quantitatively analyze the isotopic enrichment of 6Li as well as for other light elements present in these materials. Evidence for heterogenous nanoscale distribution of 3H and O3H within irradiated pellet microstructure is also provided, which is attributed to 3H trapping in irradiation induced vacancy clusters or voids within the LiAlO2 matrix. The characterization results given here, now prove the feasibility for using APT for analyzing nanoscale spatially resolved isotopic enrichment of Li as well as other light elements such as Tritium in TPBARs. This work also paves way to adopt APT for analyzing light elements such as Li and tritium in materials used for nuclear fusion reactor applications, geochemistry, astrophysics as well as for energy storage applications.

Devaraj, Arun↗

DOE OSTI2024

Understanding Formation of Irradiation-Induced Defects through 4D-STEM, Electron Tomography, and WBDF-STEM

A major challenge in advancing nuclear materials for next-generation fission and proposed fusion reactors is to comprehensively understand the formation of irradiation-induced defects. Here it is essential to correlate the evolution of irradiation-induced defects and the degradation of mechanical properties, as they collectively dictate the material's lifespan and ensure nuclear safety. Scanning transmission electron microscopy (STEM) based techniques have emerged as indispensable tools for irradiation-induced defect characterization, offering high spatial resolution imaging and chemical analysis, such as electron energy loss spectroscopy (EELS) and energy dispersive X-ray spectroscopy (EDXS). These techniques have been effectively used to obtain an atomic-scale view of the defect structure. Recent advances in electron microscopy, particularly in 4D-STEM, offer detailed insight into microstructural evolution by capturing full 2D diffraction patterns at every pixel position. Using high-speed direct electron detectors, this technology generates a four-dimensional dataset, overcoming the limitations of traditional STEM imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Denoising diffusion models with geometry adaptation for high fidelity calorimeter simulation

Simulation is crucial for all aspects of collider data analysis, but the available computing budget in the High Luminosity LHC era will be severely constrained. Generative machine learning models may act as surrogates to replace physics-based full simulation of particle detectors, and diffusion models have recently emerged as the state of the art for other generative tasks. Here, we introduce CaloDiffusion, a denoising diffusion model trained on the public CaloChallenge datasets to generate calorimeter showers. Our algorithm employs 3D cylindrical convolutions, which take advantage of symmetries of the underlying data representation. To handle irregular detector geometries, we augment the diffusion model with a new geometry latent mapping (GLaM) layer to learn forward and reverse transformations to a regular geometry that is suitable for cylindrical convolutions. The showers generated by our approach are nearly indistinguishable from the full simulation, as measured by several different metrics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

From 2D to 4D: a containerized workflow and browser to explore dynamic chromatin architecture

Background Characterizing the physical organization of the genome is essential for understanding long-range gene regulation, chromatin compartmentalization, and epigenetic accessibility. Hi-C experiments generate two-dimensional (2D) genome-wide contact maps of chromatin interactions by capturing the spatial proximity between genomic loci, which reveal interaction frequencies but lack the spatial resolution needed to interpret the three-dimensional (3D) genome structure(s). Emerging evidence suggests that epigenetic regulation is closely linked to 3D genome architecture, and that structural changes over time (4D) drive key biological processes in development, disease, and environmental response. Thus, integrating 3D structure with functional data is critical for a more complete understanding of genome regulation. Previous work, most notably the 4DHiC chromosome modeling framework, has shown that physical multi-dimensional modeling approaches rooted in polymer physics and molecular dynamics can resolve these structures at biologically meaningful resolutions by integrating temporal Hi-C data with physical constraints to uncover dynamic chromosome reorganization. Thus, molecular dynamics simulations, constrained by Hi-C contact matrices, can resolve fine-scale structural changes and reveal functionally significant transitions in chromatin conformation. Results Herein, we present the 4D Genome Browser Workflow (4DGBWorkflow) and the 4D Genome Browser (4DGB). The algorithm is based on the 4DHiC method, and the containerized tool is an end-to-end workflow that can transform, filter, and view 4D epigenomics and chromatin datasets, allowing non-specialists to apply three-dimensional modeling principles to diverse datasets and experimental conditions. The software executes on a laptop running macOS, Linux or Windows. From input Hi-C files (.hic), the 4DGBWorkflow produces 3D reconstructions of chromosomes, integrates the reconstruction with track data (e.g., epigenetic marks, transcriptome profiles), and provides comparative visualization of the results in a single workflow. Conclusions The 4DGBWorkflow and 4D Genome Browser are open-source tools for comparative analysis and visualization of 4D chromosome datasets, including chromatin architecture and epigenomic signals. Automatic integration of Hi-C data with molecular dynamics democratizes the construction of time resolved 3D genome structures, simplifying complex simulations and data integration schemes.

3D Genome Browser↗

DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗

DOE OSTI2022

The Latent Dirichlet Allocation model applied to airborne LiDAR data: A case study on mapping forest degradation associated with fragmentation and fire in the Amazon region

1. LiDAR data are being increasingly used to provide a detailed characterization of the vertical profile of forests. This characterization enables the generation of new insights on the influence of environmental drivers and anthropogenic disturbances on forest structure as well as on how forest structure influences important ecosystem functions and services. Unfortunately, extracting information from LiDAR data in a way that enables the spatial visualization of forest structure, as well as its temporal changes, is challenging due to the high dimensionality of these data. 2. In this study, we show how the Latent Dirichlet Allocation model applied to LiDAR data (LidarLDA) can be used to identify forest structural types and how the relative abundance of these forest types changes throughout the landscape. The code to fit this model is made available through the open-source r package LidarLDA in github. We illustrate the use of LidarLDA both with simulated data and data from a large-scale fire experiment in the Brazilian Amazon region. 3. Using simulated data, we demonstrate that LidarLDA accurately identifies the number of forest types as well as their spatial distribution and absorptance probabilities. For the empirical data, we found that LidarLDA detects both landscape-level patterns in forest structure as well as the strong interacting effect of fire and forest fragmentation on forest structure based on the experimental fire plots. More specifically, LidarLDA reveals that proximity to forest edge exacerbates the impact of fires, and that burned forests remain structurally different from unburned areas for at least 7 years, even when burned only once. Importantly, LidarLDA generates insights on the 3D structure of forest that cannot be obtained using more standard approaches that just focus on top-of-the-canopy information (e.g. canopy height models based on LiDAR data). 4. By enabling the mapping of forest structure and its temporal changes, we believe that LidarLDA will be of broad utility to the ecological research community.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Local element operations for curved simplex meshes

Summary Mesh optimization procedures are generally a combination of node smoothing and discrete operations which affect a small number of elements to improve the quality of the overall mesh. These procedures are useful as a post‐processing step in mesh generation procedures and in applications such as fluid simulations with severely deforming domains. In order to perform high‐order mesh optimization, these ingredients must also be extended to high‐order (curved) meshes. In this work, we present a method to perform local element operations on curved meshes. The mesh operations discussed in this work are edge/face swaps, edge collapses, and edge splitting (more generally refinement) for triangular and tetrahedral meshes. These local operations are performed by first identifying the patch of elements which contain the edge/face being acted on, performing the operation as a “straight‐sided one" by placing the high‐order nodes via an isoparametric mapping from the master element, and smoothing the high‐order nodes on the elements in the patch by minimizing a Jacobian‐based high‐order mesh distortion measure. Since the initial “straight‐sided guess” from the placement of the nodes via the isoparametric mapping frequently results in invalid elements, the distortion measure must be regularized which allows for mesh untangling for the optimization to succeed. We present several examples in 2D and 3D to demonstrate these local operations and how they can be combined with a high‐order node smoothing procedure to maintain mesh quality when faced with severe deformations.

Shi, A.↗

DOE OSTI2023

On the 3D printing of polyelectrolyte complexes: A novel approach to overcome rheology constraints

Strong polyelectrolytes, poly(styrene sulfonate) (PSS), and poly(diallyldimethylammonium) (PDADMAC) dissolved in aqueous KBr can be 3D printed for the first time in the air via direct ink writing (DIW). Viscous polyelectrolyte complex (PEC) solutions were deposited layer-by-layer and quenched with deionized water to produce mechanically viable, viscoelastic hydrogel objects. Optimal inks were confirmed by rheology and thermogravimetric measurements to map flow behavior with material composition. Finally, post-print handling and storage protocols were developed to produce printed resolutions more consistent with conventional DIW.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Improving Nb3Sn Cavity Performance Using Centrifugal Barrel Polishing

Nb3Sn superconducting radiofrequency (SRF) cavities have been an ongoing research topic for many years motivated by the potential for higher accelerating gradients and quality factors compared to niobium SRF cavities. The highest performing Nb3Sn cavities are manufactured using tin vapor-diffusion coating, which creates a Nb3Sn film with a surface roughness of around 100-200 nm. This is thought to be one of the limiting factors for the accelerating gradient of Nb3Sn cavities due to enhancement of magnetic field near sharp surface features. To smooth Nb3Sn SRF cavities, we have developed a mechanical polishing procedure which uses centrifugal barrel polishing to smooth the surface followed by a secondary tin coating step to repair the surface. We show that the accelerating field of a Nb3Sn SRF cavity is improved by applying this procedure. We also investigate the quench mechanism of the polished cavity by utilizing temperature mapping to measure which regions of the cavity e xperience heating during RF operation. We then cut samples from these regions and analyze the film microstructure and chemical composition in 3D using EDS and EBSD measurements together with a focused ion-beam (FIB) tomography technique.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗