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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Pore-Scale Dynamics of Liquid CO2–Water Displacement in 2D Axisymmetric Porous Micromodels Under Strong Drainage and Weak Imbibition Conditions: High-Speed μPIV Measurements

Resolving pore-scale transient flow dynamics is crucial to understanding the physics underlying multiphase flow in porous media and informing large-scale predictive models. Surface properties of the porous matrix play an important role in controlling such physics, yet interfacial mechanisms remain poorly understood, in part due to a lack of direct observations. This study reports on an experimental investigation of the pore-scale flow dynamics of liquid CO 2 and water in two-dimensional (2D) circular porous micromodels with different surface characteristics employing high-speed microscopic particle image velocimetry (μPIV). The design of the micromodel minimized side boundary effects due to the limited size of the domain. The high-speed μPIV technique resolved the spatial and temporal dynamics of multiphase flow of CO 2 and water under reservoir-relevant conditions, for both drainage and imbibition scenarios. When CO 2 displaced water in a hydrophilic micromodel (i.e., drainage), unstable capillary fingering occurred and the pore flow was dominated by successive pore-scale burst events (i.e., Haines jumps). When the same experiment was repeated in a nearly neutral wetting micromodel (i.e., weak imbibition), flow instability and fluctuations were virtually eliminated, leading to a more compact displacement pattern. Energy balance analysis indicates that the conversion efficiency between surface energy and external work is less than 30%, and that kinetic energy is a disproportionately smaller contributor to the energy budget. This is true even during a Haines jump event, which induces velocities typically two orders of magnitude higher than the bulk velocity. These novel measurements further enabled direct observations of the meniscus displacement, revealing a significant alteration of the pore filling mechanisms during drainage and imbibition. While the former typically featured burst events, which often occur only at one of the several throats connecting a pore, the latter is typically dominated by a cooperative filling mechanism involving simultaneous invasion of a pore from multiple throats. This cooperative filling mechanism leads to merging of two interfaces and releases surface energy, causing instantaneous high-speed events that are similar, yet fundamentally different from, burst events. Finally, pore-scale velocity fields were statistically analyzed to provide a quantitative measure of the role of capillary effects in these pore flows.

Li, Yaofa↗
DOE OSTI2020

Microanalytical Approaches to Characterizing REE in Appalachian Basin Underclays

The search for a reliable U.S. domestic source of rare earth elements (REE) is necessary to support the demand of advanced energy applications (e.g., catalysts, electronics, magnets). Sedimentary deposits may be sources for selectively recovering REE and critical metals—specifically the interbedded seat rock, or underclay, that underlies or forms the floor of a coal seam. This material is often a major component of coal waste fines and refuse and thus readily available. This study examines several Appalachian Basin underclays associated with actively mined coal seams as potential feedstocks for the REE. Multimodal microanalytical electron microscopy (SEM, FIB-SEM, EMPA) synchrotron-based µXRF, and image processing techniques are coupled with detailed elemental and mineral data to classify the 2D and 3D petrophysical properties of the materials. The REE contents of Appalachian Basin underclays were measured from 235–399 ppm and predominantly observed as discrete REE-bearing minerals such as monazite and xenotime on the order of 10–100 µm in size. These REE-bearing minerals typically accounted for less than 1% of the scanned areas and volumes under SEM and FIB-SEM analysis, with the exception of regions enriched in crandallite. Synchrotron-based µXRF elemental maps further identified several REE deposition environments in different underclays, including micro-scale (10–100 µm) light REEs co-localizing with Ca and P, micro-scale heavy REEs with Fe, and large-scale light REEs (>200 µm) co-localizing with Sr, Ba, Ca and P.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterization of a Pixelated Cadmium Telluride Detector System Using a Polychromatic X-Ray Source and Gold Nanoparticle-Loaded Phantoms for Benchtop X-Ray Fluorescence Imaging

In this paper, the imaging dose and scan time have been considered as the two major constraints for routine benchtop x-ray fluorescence computed tomography (XFCT) imaging. One way to address this issue is to acquire x-ray fluorescence (XRF) signals in parallel through a 2D array of single-crystal detectors or a pixelated detector along with the cone-beam x-ray source. To identify a detector system suitable for this purpose, a commercially available, fully spectroscopic cadmium telluride (CdTe) pixelated detector, HEXITEC (High-Energy X-ray Imaging Technology), was tested under the experimental conditions optimized for benchtop XFCT imaging of gold nanoparticles (GNPs). Specifically, two different parallel-hole stainless steel collimators were fabricated and coupled with the detector for seamless integration into our existing benchtop cone-beam XFCT system. After the detector deployment, this benchtop XFCT system was used to detect XRF photons from GNP-loaded phantoms. A pixel-merging algorithm was introduced to enhance the sensitivity of XRF photon detection thereby minimizing the scan time. The effect of pixel-level charge sharing correction algorithms was investigated within the context of benchtop XFCT imaging. The detector energy resolution, in terms of the full width at half maximum (FWHM) values at different gold K-shell XRF energies, was also determined. Of the two charge sharing correction algorithms examined, the charge sharing addition gave better sensitivity than the charge sharing discrimination (csd). On the other hand, under the current experimental conditions, the energy resolution of the HEXITEC detector was the best with the csd and estimated to be 1.56 keV FWHM at 66-69 keV photon energy. Overall, despite some degradation of the detector energy resolution (compared with typical single crystal CdTe detectors), the HEXITEC detector enabled parallel data acquisition under the experimental conditions typical of benchtop XFCT imaging and operated well within our benchtop XFCT setup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

The co-located arrangement of ECEI and MIR microwave imaging diagnostics on EAST tokamak

The co-located configuration of Passive Electron Cyclotron Emission Imaging (ECEI) and active Microwave Imaging Reflectometry (MIR) diagnostics systems has been successfully implemented on the EAST fusion reactor. These advanced 2D microwave diagnostic tools offer 10–20 mm poloidal spatial resolution through large aperture quasi optics. This arrangement not only optimizes the utilization of limited observation windows but also facilitates concurrent measurements of plasma density and electron temperature at specific toroidal positions. Furthermore, this co-located setup holds great promise for enhancing research into electron heat transport and other pivotal plasma phenomena.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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 OSTI2020

Imaging Polarity in Two Dimensional Materials by Breaking Friedel's Law

Friedel's law guarantees an inversion-symmetric diffraction pattern for thin, light materials where a kinematic approximation or a single-scattering model holds. Typically, breaking Friedel symmetry is ascribed to multiple scattering events within thick, non-centrosymmetric crystals. However, two-dimensional (2D) materials such as a single monolayer of MoS 2 can also violate Friedel's law, with unexpected contrast between conjugate Bragg peaks. Here, we show analytically that retaining higher order terms in the power series expansion of the scattered wavefunction can describe the anomalous contrast between hkl and $\bar{hkl}$ peaks that occurs in 2D crystals with broken in-plane inversion symmetry. These higher-order terms describe multiple scattering paths starting from the same atom in an atomically thin material. Furthermore, 2D materials containing heavy elements, such as WS 2 , always act as strong phase objects, violating Friedel's law no matter how high the energy of the incident electron beam. Experimentally, this understanding can enhance diffraction-based techniques to provide rapid imaging of polarity, twin domains, in-plane rotations, or other polar textures in 2D materials.

2D materials↗
DOE OSTI2026

In Situ Imaging Reveals Efficient Charge Separation in Monolayer MoS 2 –WS 2 Type-II Heterojunctions

Covalently bonded in-plane two-dimensional (2D) transition metal dichalcogenide (TMD) heterojunctions with atomically sharp interfaces hold great promise for photocatalytic applications in solar energy conversion and environmental remediation; however, their spatially resolved charge distribution and transport, particularly under operando conditions, remain poorly understood. Here, we employ photoscanning electrochemical microscopy (photo-SECM) to directly visualize photoinduced charge separation in monolayer MoS 2 –WS 2 in-plane heterojunctions. Spatial separation of photogenerated carriers is observed, with electrons accumulating in MoS 2 and holes in WS 2 , leading to strongly asymmetric interfacial kinetics: Fc + reduction proceeds rapidly on MoS 2 (0.6 cm s –1 ), whereas Fc oxidation on WS 2 is significantly slower (0.008 cm s –1 ). High-resolution surface photovoltage microscopy (SPVM) enables a quantitative comparison of charge-separation capacity across architectures. The in-plane MoS 2 –WS 2 heterojunction shows the largest photovoltage contrast (−35 mV in MoS 2 , 20 mV in WS 2 ), exceeding the vertical heterojunction (−18 mV in MoS 2 , 11 mV in WS 2 ) and the individual monolayers (−12 mV for MoS 2 , – 1 mV for WS 2 ), establishing the following trend: in-plane > vertical > monolayers. Ultraviolet photoelectron spectroscopy (UPS) indicates that this directional charge separation is driven by intrinsic type-II band alignment, while photoluminescence (PL) imaging shows that the interface acts as a recombination center that limits efficient carrier extraction. These results provide direct experimental evidence of type-II-driven charge separation in in-plane heterojunctions and offer critical insights for interface design in high-efficiency photocatalytic and optoelectronic systems.

electrical properties↗
DOE OSTI2023

Bridging Time-series Image Phenotyping and Functional–Structural Plant Modeling to Predict Adventitious Root System Architecture

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional–structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Electric fields and stationary drift flows in the island divertor SOL of Wendelstein 7-X

Abstract In the island divertor scrape-off layer (SOL) of the Wendelstein 7-X stellarator, 2D-resolved Langmuir probe measurements uncover a complex spatial structure of electron temperature, density, and floating potential. Depending on details of magnetic island size and position, significant poloidal gradients and non-monotonic radial profiles are observed. The resulting electric fields imply the presence of sheared stationary drift flows with poloidal velocities of a few km/s, with flow channels as narrow as one cm. Such flows are observed with a gas puff imaging diagnostic in qualitative agreement with the probe results and imply a significant transport channel in the SOL. The 2D distribution of plasma parameters is intricately associated to the unique magnetic structure of the island divertor SOL and highlights the necessity for more accurate magnetic field reconstructions.

Killer, Carsten (ORCID:0000000177473066)↗
DOE OSTI2024

Optical image analysis for graphene layer detection: Enhanced green channel methodology

Graphene, a material of increasing research interest, requires accurate layer identification due to its sensitivity to layer count. Existing methods for graphene layer number identification are either time-consuming or of low accuracy, with high-accuracy methods often requiring expensive processes. This paper aims to address this challenge by proposing a cost-effective and efficient approach. Specifically, the current work highlights only the green channel—one of the three primary color channels (red, green, blue) that make up an optical image—from images of exfoliated graphene flakes for layer count identification. A linear regression is performed between pixel position and substrate green channel value, and this effect is subtracted from the entire optical image to mitigate background effects. By storing the range of green channel values for each type of flake (monolayer, bilayer, or tri-layer) based on a few images, we establish thresholds for identifying different types of layers in a particular setup. Additionally, our methodology allows for flexible threshold tuning using a single reference image, enabling adjustment to changes in detection setup such as illumination level, magnification, or microscope used. Finally, demonstrating high accuracy and flexibility, this methodology presents a suitable technique for graphene layer number identification without the need for large datasets or expensive instruments.

2D materials↗
DOE OSTI2022

Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learning

Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS 2 nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.

2D materials↗
DOE OSTI2020

Evidence of restricted heat transport in National Ignition Facility Hohlraums

We present experimental evidence of restricted electron thermal conduction in the high-Z coronal plasma regions of laser-driven Hohlraums on the National Ignition Facility. Four separate measurements, three of which are direct observations of Hohlraum dynamics, corroborate this finding. (1) The velocity of the coronal plasma ablated and heated by the outer-cone laser beams is determined by time-dependent imaging of the gold plasma plume, or “bubble.” The velocities of the incoming plume (perpendicular to the Hohlraum axis) are consistent with high-fidelity 2D radiation-hydrodynamic simulations using flux-limited thermal electron conduction with a flux multiplier f = 0.03. Simulations using f = 0.15, which is very nearly classical Spitzer–Härm transport, predict plume velocities slower than measured. (2) Specific features in time-resolved images of the Hohlraum wall at an angle of 19° are also more consistent with f = 0.03 simulations compared to f = 0.15. (3) Spectroscopic tracers were added to the Hohlraum wall in the outer-beam bubble region. The ratios of hydrogen-like to helium-like line emission are sensitive to the electron temperature of the bubble. The hydrogen-like to helium-like ratios extracted from the time-integrated spectra of manganese and cobalt tracers from two observation angles are consistent with f = 0.03 and not with f = 0.15. (4) The time of peak capsule emission, or “bang time,” an integrated measurement, is also more consistent with f = 0.03 than with f = 0.15. Overall, while these findings do not identify the causes of restricted thermal conduction in Hohlraums, they motivate future experiments to test specific hypotheses and focus on model development in the regions of the plasma exhibiting restricted transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Nano-infrared imaging of epitaxial graphene on SiC revealing doping and thickness inhomogeneities

Here, we report on a nano-infrared (IR) imaging and spectroscopy study of epitaxial graphene on silicon carbide (SiC) by using scattering-type scanning near-field optical microscopy (s-SNOM). With nano-IR imaging, we reveal in real space microscopic domains with distinct IR contrasts. By analyzing the nano-IR, atomic force microscopy, and scanning tunneling microscopy imaging data, we conclude that the imaged domains correspond to single-layer graphene, bilayer graphene (BLG), and higher-doped BLG. With nano-IR spectroscopy, we find that graphene can screen the SiC phonon resonance, and the screening is stronger at more conductive sample regions. Our work offers insights into the rich surface properties of epitaxial graphene and demonstrates s-SNOM as an efficient and effective tool in characterizing graphene and possibly other two-dimensional materials.

2D materials↗
DOE OSTI2026

Space-Charge-Limited van der Waals Spin Transistor

Integrating semiconducting and magnetic materials could combine transistorlike operation with nonvolatility and enable architectures such as logic in memory. Here, in this work, we employ correlated electrical transport and scanning nitrogen-vacancy center magnetic imaging to elucidate a spin transistor concept that amalgamates vertical and lateral hopping transport inside a 2D antiferromagnetic semiconductor, mechanistically distinct from vertical tunneling devices. Our device, based on a monolayer-bilayer junction in CrSBr, displays giant, gate-tunable magnetoresistance driven by the dual action of electrostatic doping on space-charge-limited lateral conduction and interlayer exchange coupling. Moreover, we visualize a field-trainable, layer-sharing effect that selects between coherent or domain-wall reversal at the spin-flip transition, enabling multilevel, memristive conductance states. Our layer-dependent space charge mechanism for convergent electrical and magnetic control opens opportunities to address limitations in contemporary computing.

2-dimensional systems↗
DOE OSTI2025

Disclosure of the XRD pipeline software

The XRD pipeline software is a program to reduce 2D X-ray diffraction data from area detectors with advanced algorithms on automatic masking and image segmentation, which help characterization of multiple phases recorded in the data and facilitate data analysis.SF-25-114

XU, WENQIAN [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2026

Generation of random geological models using multi-randomization for machine learning

Generating high-fidelity geological models is essential for advancing machine learning (ML) methods in automated seismic interpretation. For instance, seismic images paired with corresponding fault labels are foundational for ML-based fault detection from seismic migration sections. While several open-access datasets of random geological models exist, open-source tools specifically designed to produce large volumes of such models for ML applications remain scarce. To address this gap, we present RGM (Random Geological Model), an open-source software package for efficiently generating 2D and 3D synthetic geological models tailored for ML workflows. RGM supports the creation of diverse model components, including medium property distributions (P-/S-wave velocities and density), seismic reflectivity images (i.e., synthetic migration sections), relative geological time, and discrete fault attributes such as probability, dip, strike, rake, and displacement. It also accommodates the creation of complex geological features such as salt bodies and unconformities. The model generation algorithm employs a multi-randomization strategy, yielding an effectively infinite-dimensional model space that encompasses a wide range of geological scenarios and associated seismic features. Furthermore, RGM incorporates a method to generate synthetic elastic migration images using analytical elastic reflection coefficients combined with frequency-dependent scaling. This functionality enables the creation of training datasets for ML models that leverage elastic seismic images. RGM is implemented in modern object-oriented Fortran, allowing users to flexibly control statistical parameters governing model variability. We demonstrate the capability, performance, and geological realism of the package through comprehensive 2D and 3D examples.

58 GEOSCIENCES↗
DOE OSTI2020

Quantifying patterns in optical micrographs of one- and two-dimensional ellipsoidal particle assemblies

Current developments in colloidal science include the assembly of anisotropic colloids with broad geometric diversity. As the complexity of particle assemblies increases, the need for ubiquitous algorithms that quantitatively analyze images of the assemblies to deliver key information such as quantification of crystal structures becomes more urgent. This contribution describes algorithms capable of image analysis for classifying colloidal structures based on abstracted interparticle relationship information and quantitatively analyzing the abundance of each structure in mixed pattern assemblies. The algorithm parameters can be adjusted, allowing for the algorithms to be adapted for different image analyses. Three different ellipsoidal particle assembly images are presented to demonstrate the effectiveness of the algorithms: a one-dimensional (1D) particle chain assembly and two two-dimensional (2D) polymorphic crystals each consisting of assemblies of two distinct plane symmetry groups. Angle relationships between neighbouring particles are calculated and neighbour counts of each particle are determined. Combining these two parameters as rules for classification criteria allows for the labeling and quantification of each particle into a defined symmetry class within an assembly. The algorithms provide a labelled image comprising classification results and particle counts of each defined class. For multiple images or individual frames from a video, the script can be looped to achieve automatic processing. The yielded classification data allow for more in-depth image analysis of mixed pattern particle assemblies. Finally, we envision that these algorithms will have utility in quantitative analysis of images comprising ellipsoidal colloidal materials, nanoparticles, or biological matter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗