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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advancing radioactive material research method: the development of a novel in situ particle-attached microfluidic electrochemical cell

This study aims to develop a microgram-scale microfluidic electrochemical cell (E-cell) for investigating the redox behavior of uranium oxide (UO 2 ). The traditional bulk electrochemical methods may require shielded facilities to investigate the hazardous materials, e.g., spent nuclear fuel, due to high radiation levels. Microfluidic E-cells offer advantages such as reduced radiation exposure, control over fluid flow rates, and high-throughput capabilities. Methods: The design of the E-cell considers electrode morphology, adhesion to a thin membrane, electrode configuration, and vacuum compatibility. Three techniques, including FIB-SEM lift-out, Au coating, and polyvinylidene fluoride (PVDF) binder, are explored for fabricating and attaching microgram quantities of UO 2 as working electrodes. The PVDF binder method proves to be the most effective, enabling the creation of a vacuum-compatible microfluidic E-cell. Results and discussion: The PVDF binder method demonstrates successful electrochemical responses and allows for real-time monitoring of UO 2 electrode behavior at the microscale. It offers chemical imaging capabilities using in situ SEM/EDS analysis. The technique provides consistent redox outcomes similar to bulk electrochemical analysis. Conclusion: The development of a microgram-scale microfluidic electrochemical cell using the PVDF binder technique enables the investigation of UO 2 redox behavior. It offers a low-risk approach with reduced radiation exposure and high-throughput capabilities. The technique provides real-time monitoring and chemical imaging capabilities, making it valuable for studying spent nuclear fuel systems and material characterization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Role of Mineralogy in Controlling Fracture Formation

The presence of fractures in caprocks can pose increased risks in subsurface energy systems and processes like CO 2 sequestration by introducing high-permeability leakage paths. Fracture apertures and permeability can be altered through mineral dissolution and precipitation reactions, but the reactive evolution of fractures is not well understood. In fractures, minerals that are otherwise inaccessible to reactive fluids can become exposed, resulting in mineral reactions unpredicted by bulk formation data. This work seeks to understand the relationship between mineralogy and fracture formation to enhance our understanding of reactive fracture evolution and CO 2 leakage potential. Here, the mineral compositions of mechanically induced fracture surfaces in samples of the Mancos and Marcellus shales have been quantified and compared to those of the near-fracture matrices using imaging and bulk X-ray diffraction (XRD) data. In the Mancos shale, the concentrations of clay minerals are enhanced along fracture surfaces with respect to the bulk, and the fracture is most likely to form at kaolinite–kaolinite interfaces. Further, evaluation of the mineralogical spatial variability through cross-correlation analysis of the surrounding matrix in images of samples cut perpendicular to the fracture shows that clay is 16.7 times more likely to be present than carbonate minerals near the fracture surface. The high correlation persists roughly 200 μm into the surrounding matrix for the Mancos sample and implies that the fracture formed within a defined clay-rich lithofacies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Random projection using random quantum circuits

The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [ ∼ O ( n ) ] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024

Kumaran, Keerthi (ORCID:0009000949125721)↗
DOE OSTI2020

The Prevalence of Anions at Plasmonic Nanojunctions: A Closer Look at P-Nitrothiophenol

We revisit the reductive coupling of p-nitrothiophenol (NTP) to form dimercaptoazobenzene (DMAB), herein monitored through gap-mode tip-enhanced Raman spectroscopy (TERS) and nano-imaging. We employ a plasmonic Au probe (100 nm diameter at its apex) illuminated with a 633 nm laser source (50 ?W/?m2 at the sample position) to image a NTP-coated faceted silver nanoparticle (~70 nm diameter). A detailed analysis of the recorded spectra reveals that anionic NTP species contribute to the recorded spectral images, in addition to the more thoroughly described DMAB product. Notably, the signatures of the anions are more pronounced than those of the DMAB product under our present experimental conditions. Our results thus demonstrate that anions and their spectral signatures must be considered in the analysis of plasmon-enhanced spectra and images.

Wang, Chih-Feng↗
DOE OSTI2022

Scanning tunneling microscopy study of the antiferromagnetic topological insulator MnBi 2 Se 4

Topological insulators with intrinsic magnetic ordering such as MnBi 2 Se 4 have been predicted to host many exotic topological phenomena including dissipationless edge states and magnetoelectric effects. Here we use scanning tunneling microscopy to study the surface of MnBi 2 Se 4 epitaxial thin films. Large scale topographic images show steps between van der Waals layers and screw dislocations. While we find the Se-termination predominant on the surface, areas attributed to Bi-termination are found at the edges of small islands in as-grown films, or in larger areas upon removal of surface capping. Here, we compare these terminations through an analysis of step heights, atomic resolution images and tunneling spectroscopy. Tunneling spectra on the Se-termination reveals semiconducting behavior, with an apparent bandgap of >0.18eV that depends on the tip height. In contrast, tunneling spectra of the Bi-termination appears gapless, which could be an indication of dangling bond states on the surface. In both cases, a hexagonal 3.9Å lattice is observed that is consistent with the expected crystal structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Subsurface Characterization of Hydraulic Fracture Test Site-2 (HFTS-2), Delaware Basin

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Wolfcamp Formation of the Permian (Delaware) Basin. This paper focuses on integration, advanced geological characterization, and 3D subsurface modeling of the comprehensive HFTS-2 dataset. The study showcases a multidisciplinary reservoir characterization approach that incorporates geology, petrophysics, geochemistry, geomechanics, microseismic, and subsurface engineering analysis. Subsurface characterization of organic-rich mudstone formations requires understanding complex hydraulic fracture network growth in relation to inherent lithology, geomechanical properties, and interaction with pre-existing natural fractures. This paper presents a characterization workflow incorporating pre- and post-stimulation subsurface data, unique to the HFTS-2 dataset. The study integrated: (1) rock properties from logs, cores, and thin sections; (2) natural and hydraulic fracture descriptions from cores and image logs; (3) local and regional stresses; (4) geomechanics; (5) microseismic; (6) fiber optic (FO) and bottomhole pressure gauge (BHPG) response; and (7) produced fluids analysis. During a stimulation treatment, creation of the stimulated rock volume (SRV) is influenced by several subsurface factors. Key contributing factors include structural context, stress conditions, lithology, facies architecture, pre-existing natural fractures, and geomechanical properties. The HFTS-2 subsurface data integration indicates that the SRV is comprised of a complex juxtaposition of hydraulic fracture swarms, as evidenced by image logs analysis, core description, and microseismic monitoring. The HFTS-2 microseismic event density was used to generate 3D heat maps that serve as a representative SRV footprint, corroborated by secondary datasets. These maps were further integrated with petrophysical and geomechanical characteristics, as well as responses from FO and BHPG, to estimate the lateral and vertical dimensions of the effective fractures. The geological characterization for the HFTS-2 dataset combined with 3D modeling for petrophysical and geomechanical properties provides a strong foundation for subsurface simulation and optimization studies. Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S005R001/2477501/urtec-2021-5243-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5243 The workflow improved our understanding of HFTS-2 hydraulic fracture propagation and characteristics in relation to offset pressure depletion and interaction with pre-existing natural fractures. Analysis showed that fracture geometry varies by stage and by well, and a complex fracture network is generated with varying fracture density. The multidisciplinary workflow presented herein for integration and characterization serves as a foundation to evaluate completion efficiency and estimate areal and vertical stimulation and depletion extent for the project. Furthermore, the workflow and learnings can also be transferred to other unconventional plays.

58 GEOSCIENCES↗
DOE OSTI2024

Scalable Nanoimprint Manufacturing of Functional Multilayer Metasurface Devices

Optical metasurfaces, consisting of subwavelength-scale meta-atom arrays, hold great promise of overcoming the fundamental limitations of conventional optics. Due to their structural complexity, metasurfaces usually require high-resolution yet slow and expensive fabrication processes. Here, using a metasurface polarimetric imaging device as an example, the photonic structures and the Nanoimprint lithography (NIL) processes are designed, creating two separate NIL molds over a patterning area of > 20 mm2 with designed Moiré alignment markers by electron-beam writing, and further subsequently integrate silicon and aluminum metasurface structures on a chip. Uniquely, the silicon and aluminum metasurfaces are fabricated by using the nanolithography and 3D pattern-transfer capabilities of NIL, respectively, achieving nanometer-scale linewidth uniformity, sub-200 nm translational overlay accuracy, and <0.017 rotational alignment error while significantly reducing fabrication complexity and surface roughness. Here, the micro-sized multilayer metasurfaces have high circular polarization extinction ratios as large as ≈20 and ≈80 in blue and red wavelengths. Further, the metasurface chip-integrated CMOS imager demonstrates high accuracy in broad-band, full Stokes parameter analysis in the visible wavelength ranges and single-shot polarimetric imaging. This novel, NIL-based, multilayered nanomanufacturing approach is applicable to the scalable production of large-area functional structures for ultra-compact optic, electronic, and quantum devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multimodal imaging of oil-in-water bilgewater emulsion and biofilms

Bilgewater emulsions, consisting of oil and surfactant mixtures in water, affect many aquatic species. Thus development of methods and techniques to treat or mitigate the formation and undesired consequences of shipboard emulsions are urgently needed. This work presents new results of the evolution of the bilgewaer chemical and physical properties using in situ imaging. A Navy O/W emulsion consisting of three oils and a detergent mixture was used as the synthetic bilgewater model [1, 2]. Synthetic oil-in-water (O/W) emulsion droplets were seled in a vacuum compatible microfluidic sample holder and imaged using a suite of tools including scanning electron microscopy (SEM), time-of-flight secondary ion mass spectrometry (ToF-SIMS), confocal laser scanning microscopy, and optical microscopy, highlighting the advantage of multiscale analysis and its applications in waste reduction and treatment in the environment. The transferrable and vacuum compatible microfluidic interface, System for Analysis at the Liquid Vacuum Interface (SALVI), was used in this study. SALVI enabled surface analysis of liquids and liquid-solid interactions using ToF-SIMS and SEM [3, 4]. Its detection window is an aperture of 2 ?m in diameter open to vacuum, permitting direct detection of the liquid surface. Liquid is withheld by surface tension within the aperture. The interface is composed of a silicon nitride membrane and polydimethylsiloxane microchannel [3, 4]. A variety of samples including complex liquid mixtures, ionic liquids, single mammalian cells, live biofilms, and solid-electrolyte interface have been analyzed using in situ imaging [5, 6]. The feasibility of using SALVI for in situ SEM imaging of nanoparticles and colloids in liquid was recently demonstrated [7]. This paper shows new findings of multimodal imaging and analysis of synthesized emulsion particles in water. Figure 1 depicts a schematic of multimodal in situ imaging of bilgewater emulsion using SEM and ToF-SIMS. Furthermore, we study the interactions of microbes and bilgewater emulsion. Microbes play an important role in the ocean. We hypothesize that microbes release organics that can act as surfactants that affect bilgewater formation or weakening. We present the first systematic study of emulsions and biofilms and investigate the effects of biofilms on bilgewater emulsions. Pseudomonas was selected as a model strain. Biofilms were cultured in a microchannel to allow culture [8]. Once a thick layer of biofilms was formed, the medium solution was changed to a mixture consisting of 50 % bilgewater emulsion. Dispersed biofilms were collected at 24 hrs. and 48 hrs. after emulsions were introduced into the channel. Bilgewater emulsions, biofilms, and mixtures of bilgewater emulsions and biofilms were analyzed using multiple in situ and ex situ techniques as described earlier. Our findings indicate that biofilms change the chemical makeup of the emulsion surface compositions and emulsion droplet size distribution, confirming the hypothesis that extracellular polymeric substance (EPS) related components released from biofilms can function as surfactants and change the oil-in-water interfaces.

bilgewater, multimodal imaging, biofilms↗
DOE OSTI2022

Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks

Abstract For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure, e.g., via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view. Cracks within Li-ion electrode particles are an example of fine features, representative quantification of which requires large volumes of tens of particles. To overcome the trade-off between the imaged volume of the material and the resolution achieved, we deploy generative adversarial networks (GAN), namely SRGANs, to super-resolve SEM images of cracked cathode materials. A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles. This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view, thus enabling representative quantification of fine features.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Luo, Yufeng (ORCID:0000000246230683)↗
DOE OSTI2025

Ecosystem consequences of a nitrogen-fixing proto-organelle

Microscale symbioses can be critical to ecosystem functions, but the mechanisms of these interactions in nature are often cryptic. Here, we use a combination of stable isotope imaging and tracing to reveal carbon (C) and nitrogen (N) exchanges among three symbiotic primary producers that fuel a salmon-bearing river food web. Bulk isotope analysis, nanoSIMS (secondary ion mass spectrometry) isotope imaging, and density centrifugation for quantitative stable isotope probing enabled quantification of organism-specific C- and N-fixation rates from the subcellular scale to the ecosystem. After winters with riverbed-scouring floods, the macroalga Cladophora glomerata uses nutrients in spring runoff to grow streamers up to 10 m long. During summer flow recession, riverine N concentrations wane and Cladophora becomes densely epiphytized by three species of Epithemia, diatoms with N-fixing endosymbionts (proto-organelles) descended from a free-living Crocosphaera cyanobacterium. Over summertime epiphyte succession on Cladophora, N-fixation rates increased as Epithemia spp. became dominant, Cladophora C-fixation declined to near zero, and Epithemia C-fixation increased. Carbon transfer to caddisflies grazing on Cladophora with high densities of Epithemia was 10-fold higher than C transfer to caddisflies grazing Cladophora with low Epithemia loads. In response to demand for N, Epithemia allocates high levels of newly fixed C to its endosymbiont. Consequently, these endosymbionts have the highest rates of C and N accumulation of any taxon in this tripartite symbiosis during the biologically productive season and can produce one of the highest areal rates of N-fixation reported in any river ecosystem.

Marks, Jane C. [Northern Arizona Univ., Flagstaff,↗
DOE OSTI2020

How “hot” are hotspots: Statistically localizing the high-activity areas on soil and rhizosphere images

The topic of microbial hotspots in soil requires not only visualizing their spatial distribution and biochemical analyses, but also statistical approaches to identify these hotspots and separate them from the surrounding activities (background). We hypothesized that each hotspot type (e.g. enzyme activities in the rhizosphere, root exudation, localization of herbicide accumulation) is a result of local process driven by biotic and/or abiotic factors, and the process rates in the hotspots are much faster than those in the soil background. We further hypothesized that the background and hotspot activities in soil belong to different statistical distributions. Consequently, hotspot determination should be based on statistical separation of activities significantly higher than the background. We analyzed for the statistical distributions of grey values on three groups of published images: 1) 14 C images of carbon input by roots into the rhizosphere, 2) 14 C glyphosate accumulation in the plant, and 3) zymogram of leucine aminopeptidase activity in rooted soil. The two Gaussian distributions were fit (the first representing the background, the second the hotspots) to the distribution of grey values in the images, the parameters (means and standard deviations, SD) of the fitted distributions were calculated, and the background was removed. Thus, we identified hotspots as areas outside of the Mean+2SD image intensity (corresponding to the upper ~ 2.5% of activity, being over 97.5% of background values) and finally, visualized images of solely hotspot locations. Finally, these results were compared with previously used decisions on hotspot intensity thresholding (i.e. Top-25% and 17 standard thresholding approaches in ImageJ) and discussed the advantages of the Mean+2SD as well as Mean+3SD approaches. These advantages include: i) simple unification of the thresholding approach for several imaging methods with various principles of activity distribution, ii) identification of hotspots with various activity levels, iii) analysis of “time-specific” hotspots in temporal sequences of images. Compared with 17 standard thresholding methods, we concluded that objectively elucidating and separating the hotspots should be based on statistical distribution analysis, e.g. using the Mean+2SD or Mean+3SD approaches. Furthermore, this simple Mean+2SD approach delivered suitable results for three groups of images and so, helps to understand the processes responsible for the highest activities and elucidate hotspots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A bi-channel aided stitching of atomic force microscopy images

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images when performing image stitching. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on Atomic Force Microscopy (AFM) generated Pantoea sp. YR343 biofilm and PTO thin film sample images as experimental data. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude and phase channels of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional direct stitching approach in AFM topographical image stitching task. Here, we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow can serve as a valuable augmentation strategy for microscopy image stitching tasks and will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.

Atomic force microscopy↗
DOE OSTI2023

Power Electronics Materials and Bonded Interfaces - Reliability and Lifetime

High temperature operation of wide bandgap devices continue to be a challenge for the power electronics packages. Thermal performance and reliability are important factors that determine the viability of a bonded interface for operation at high temperatures. In this presentation, we present the technical approach and key results from the research on sintered silver, transient liquid phase alloy, and polymeric materials. A lifetime prediction model that incorporates the thermomechanical behavior of sintered silver at 200C was developed. The copper-aluminum transient alloy completed 350 thermal cycles from -40C to 200C and little increase in the defect level was observed. In addition to material research, we initiated a time-series analysis on the scanning acoustic microscope images of eutectic solder to explore statistical forecasting methods and machine learning techniques. Initial results that report the accuracy of a few different statistical models are presented.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

SDSS-IV MaNGA: The incidence of major mergers in type I and II AGN host galaxies in the DR15 sample

We present a study on the incidence of major mergers and their impact on the triggering of nuclear activity in 47 type I and 236 type II optically selected AGN from the MaNGA DR15 sample. From an estimate of non-parametric image predictors (Gini, M_20, concentration (C), asymmetry (A), clumpiness (S), Sérsic index (n), and shape asymmetry ()) using the SDSS images, in combination with a Linear Discriminant Analysis Method, we identified major mergers and merger stages. We reinforced our results by looking for bright tidal features in our post-processed SDSS and DESI legacy images. We find a statistically significant higher incidence of major mergers of 29 per cent ± 3 per cent in our type I+II AGN sample compared to 22 per cent ± 0.8 per cent for a non-AGN sample matched in redshift, stellar mass, colour, and morphological type, finding also a prevalence of post-coalescence (51 per cent ± 5 per cent) over pre-coalescence (23 per cent ± 6 per cent) merger stages. The levels of AGN activity among our massive major mergers are similar to those reported in other works using [O iii] tracers. However, similar levels are produced by our AGN-galaxies hosting stellar bars, suggesting that major mergers are important promoters of nuclear activity but are not the main nor the only mechanism behind the AGN triggering. The tidal strength parameter Q was considered at various scales looking for environmental differences that could affect our results on the merger incidence, finding non-significant differences. Finally, the H-H β diagram could be used as an empirical predictor for the flux coming from an AGN source, useful to correct photometric quantities in large AGN samples emerging from surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE

This report summarizes the neutron imaging techniques and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. The experimental results and technique developments reported herein support the US Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in hightemperature microreactor designs is addressed by means of high-spatial-resolution neutron imaging coupled with custom-built and application-specific furnaces. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. Neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metals hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on the first two in-situ heating measurements of YH x performed over the 2020 and 2021 LANSCE operation cycles. In parallel with in-situ heating measurements, neutron computed tomography scans were performed to visualize hydrogen aggregates in post-heated YH x samples. Analysis and results from these neutron imaging measurements are given along with outlooks and plans for improving future hydrogen diffusion measurements at LANSCE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Detecting and characterizing special nuclear material for nuclear nonproliferation applications

Abstract There is an urgent need for new, better instrumentation and techniques for detecting and characterizing special nuclear material (SNM), i.e., highly enriched uranium and plutonium. The development of improved instruments and techniques requires experiments performed with the SNM itself, which is of limited availability. This paper describes the findings of experiments performed at the National Criticality Experiments Research Center conducted using new instruments and techniques on unclassified, kg-quantity SNM objects. These experiments, performed in the framework of the Department of Energy, National Nuclear Security Administration Consortium for Monitoring, Technology, and Verification, focused on detecting, characterizing, and localizing SNM samples with masses ranging from 3.3 to 13.8 kg, including plutonium and highly enriched uranium using prototype detectors and techniques. The work demonstrates SNM detection and characterization using recently-developed prototype detection systems. Specifically, we present new results in passive detection and imaging of plutonium and uranium objects using gamma-ray and dual particle (fast neutron and gamma-ray) imaging. We also present a new analysis of the delayed neutron emissions during active interrogation of uranium using a neutron generator.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Element differentiation with a Hartmann- based X-ray phase imaging system

Significant efforts are currently ongoing in X-ray imaging to provide multimodal imaging systems, targeting better sensitivity and specificity for both biomedical or non-destructive testing applications. Knowing the elemental composition of specific structures, such as breast microcalcifications in mammography, would help to differentiate malign and benign tumours. Standard X-ray Phase Contrast Imaging techniques provide only qualitative information on elements with similar absorption properties. However, their chemical composition can be determined from the measurement of the phase as it is directly related to the optical index of elemental materials. We will show new experimental results obtained with an X-ray phase imaging system based on a Hartmann mask. Early data treatment succeeded in retrieving both the real and imaginary parts of the refractive index. The system demonstrates the capability to discriminate materials based on elemental composition.

36 MATERIALS SCIENCE↗