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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Ginzburg--Landau functionals in the large-graph limit

Ginzburg–Landau (GL) functionals on graphs, which are relaxations of graph-cut functionals on graphs, have yielded a variety of insights in image segmentation and graph clustering. In this paper, we study large-graph limits of GL functionals by taking a functional-analytic view of graphs as nonlocal kernels. For a graph Wn with n nodes, the corresponding graph GL functional GL W n ϵ is an energy for functions on Wn. We minimize GL functionals on sequences of growing graphs that converge to functions called graphons. For such sequences of graphs, we show that the graph GL functional Γ-converges to a continuous and nonlocal functional that we call the graphon GL functional. We investigate the sharp-interface limits of the graph GL and graphon GL functionals, and we relate these limits to a nonlocal total-variation (TV) functional. We express the limiting GL functional in terms of Young measures and thereby obtain a probabilistic interpretation of the minimization problem in the large-graph limit. Finally, to develop intuition about graphon GL functionals, we determine the GL minimizer for several example families of graphons.

Zhang, Edith↗
DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗
DOE OSTI2020

PSF–NET: A Nonparametric Point-spread Function Model for Ground-based Optical Telescopes

Ground-based optical telescopes are seriously affected by atmospheric turbulence induced aberrations. Understanding properties of these aberrations is important both for instrument design and image restoration method development. Because the point-spread function can reflect performance of the whole optic system, it is appropriate to use the point-spread function to describe atmospheric turbulence induced aberrations. Assuming point-spread functions induced by the atmospheric turbulence with the same profile belong to the same manifold space, we propose a nonparametric point-spread function—PSF–NET. The PSF–NET has a cycle convolutional neural network structure and is a statistical representation of the manifold space of PSFs induced by the atmospheric turbulence with the same profile. Testing the PSF–NET with simulated and real observation data, we find that a well trained PSF–NET can restore any short exposure images blurred by atmospheric turbulence with the same profile. Besides, we further use the impulse response of the PSF–NET, which can be viewed as the statistical mean PSF, to analyze interpretation properties of the PSF–NET. We find that variations of statistical mean PSFs are caused by variations of the atmospheric turbulence profile: as the difference of the atmospheric turbulence profile increases, the difference between statistical mean PSFs also increases. The PSF–NET proposed in this paper provides a new way to analyze atmospheric turbulence induced aberrations, which would benefit the development of new observation methods for ground-based optical telescopes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate

Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.

Accelerated aging↗
DOE OSTI2023

Nanoscale imaging of He-ion irradiation effects on amorphous TaO x toward electroforming-free neuromorphic functions

Resistive switching in thin films has been widely studied in a broad range of materials. Yet, the mechanisms behind electroresistive switching have been persistently difficult to decipher and control, in part due to their non-equilibrium nature. Here, we demonstrate new experimental approaches that can probe resistive switching phenomena, utilizing amorphous TaO x as a model material system. Specifically, we applied scanning microwave impedance microscopy and cathodoluminescence (CL) microscopy as direct probes of conductance and electronic structure, respectively. These methods provide direct evidence of the electronic state of TaO x despite its amorphous nature. For example, CL identifies characteristic impurity levels in TaO x , in agreement with first principles calculations. We applied these methods to investigate He-ion-beam irradiation as a path to activate conductivity of materials and enable electroforming-free control over resistive switching. However, we find that even though He-ions begin to modify the nature of bonds even at the lowest doses, the films' conductive properties exhibit remarkable stability with large displacement damage and they are driven to metallic states only at the limit of structural decomposition. Finally, we show that electroforming in a nanoscale junction can be carried out with a dissipated power of <20 nW, a much smaller value compared to earlier studies and one that minimizes irreversible structural modifications of the films. Importantly, the multimodal approach described here provides a new framework toward the theory/experiment guided design and optimization of electroresistive materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning informed control systems for extrusion printing processes

Systems and methods for controlling a material extrusion device to extrude a filament of an ink are provided. An extrusion printing control system collects from one or more sensors measurements representing an internal state of material extrusion processing during extrusion of the filament. In addition, the system collects an image of the filament as the filament is extruded. The system applies a classifier to the collected image to generate an image-derived state characterizing the filament. Based on the internal state and the image-derived state, the system estimates a derived state using a model. The system determines control parameters using the model to achieve a desired quality of the filament by minimizing a cost function based on the internal state, the image-derived state, the derived state, and constraints of the material extrusion device. Finally, the system provides the control parameters to a controller of the material extrusion device.

Howell, Brian↗
DOE OSTI2021

The Initial Mass Function of Low-mass Stars and Brown Dwarfs in the W3 Complex

We have used archival infrared images obtained with the Wide Field Camera 3 on board the Hubble Space Telescope to constrain the initial mass function of low-mass stars and brown dwarfs in the W3 star-forming region. The images cover 438 arcmin{sup 2}, which encompasses the entire complex, and were taken in the filters F110W, F139M, and F160W. We have estimated extinctions for individual sources in these data from their colors and have dereddened their photometry accordingly. By comparing an area of the images that contains the richest concentration of previously identified W3 members to an area that has few members and is dominated by background stars, we have estimated the luminosity function for members of W3 with masses of 0.03–0.4 M {sub ⊙}. That luminosity function closely resembles data in typical nearby star-forming regions that have much smaller stellar populations than W3 ( ≲ 500 versus several thousand objects). Thus, we do not find evidence of significant variations in the initial mass function of low-mass stars and brown dwarfs with star-forming conditions, which is consistent with recent studies of other distant massive star-forming regions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved imaging

Abstract Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material’s processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of Mo x Re 1- x S 2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

25 ENERGY STORAGE↗
DOE OSTI2024

Fracture length data for geothermal applications

Fracture lengths govern permeability and are unknowns in geothermal assessment. Along their lengths, fracture widths vary due to growth by linkage. Under the influence of diagenesis, narrow widths seal, breaking porosity continuity and reducing open length. The largest range of widths and thus susceptibility to fill occurs where fractures are linked by narrow segments. Outcrops of a geothermal target, Cambrian Potsdam quartz arenite, contain opening-mode fractures having lengths spanning five orders of magnitude from 0.082 mm to 17.9 m. Combined lengths measured at a range of scales can be described by power laws, but at a given image resolution, lengths are best fit by exponential functions. Owing to preferential sealing of small fractures, open fractures follow exponential functions, but values depend on rules for designating fractures as continuous. En échelon segments, offset 10 mm, are connected by narrow fractures or microfractures (hard linked) not evident on outcrop 1 m-elevation LiDAR or 30 m-height drone images. A rule that identifies where narrow and likely connected segments are located can yield lengths meaningful for flow simulation. Depending on diagenesis, continuity rules can halve or double average and maximum length values. Length values from outcrop for geothermal applications should be adjusted based on wellsite-specific diagenesis information.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Observation of nanoparticle coalescence during core-shell metallic nanowire growth in colloids via nanoscale imaging

The surface morphology and shape of crystalline nanowires significantly influence their functional properties, including phonon transport, electrocatalytic performance, to name but a few. However, the kinetic pathways driving these morphological changes remain underexplored due to challenges in real-space and real-time imaging at single-particle and atomic resolutions. This study investigates the dynamics of shell (Au, Pd, Pt, Fe, Cu, Ni) deposition on AuAg alloy seed nanowires during core-shell formation. By using chiral/non-chiral seed nanowires, advanced imaging techniques, including liquid-phase transmission electron microscopy (LPTEM), cryogenic TEM, and three-dimensional electron tomography, a three-step deposition process is revealed: heterogeneous nucleation, nanoparticle attachment, and coalescence. It is found that colloidal Ostwald ripening, metal reactivity, and deposition amount modulate nanoparticle size and surface roughness, shaping final morphologies. Noble metal nanoparticles (Au, Ag, Pd, Pt) coalesce with seed nanowire along the 〈111〉 direction, distinct from that of other metals. These findings are consistent across different metals, including Ru, Cu, Fe, and Ni, highlighting the hypothesis of these processes in nanowire formation. These findings enhance traditional crystallographic theories and provide a framework for designing nanowire morphology. Additionally, our imaging techniques may be applied to investigate phenomena like electrodeposition, dendrite growth in batteries, and membrane deformation.

Yang, Dahai↗
DOE OSTI2021

EVAP: A two-photon imaging tool to study conformational changes in endogenous Kv2 channels in live tissues

A primary goal of molecular physiology is to understand how conformational changes of proteins affect the function of cells, tissues, and organisms. Here, we describe an imaging method for measuring the conformational changes of the voltage sensors of endogenous ion channel proteins within live tissue, without genetic modification. We synthesized GxTX-594, a variant of the peptidyl tarantula toxin guangxitoxin-1E, conjugated to a fluorophore optimal for two-photon excitation imaging through light-scattering tissue. We term this tool EVAP (Endogenous Voltage-sensor Activity Probe). GxTX-594 targets the voltage sensors of Kv2 proteins, which form potassium channels and plasma membrane–endoplasmic reticulum junctions. GxTX-594 dynamically labels Kv2 proteins on cell surfaces in response to voltage stimulation. To interpret dynamic changes in fluorescence intensity, we developed a statistical thermodynamic model that relates the conformational changes of Kv2 voltage sensors to degree of labeling. We used two-photon excitation imaging of rat brain slices to image Kv2 proteins in neurons. We found puncta of GxTX-594 on hippocampal CA1 neurons that responded to voltage stimulation and retain a voltage response roughly similar to heterologously expressed Kv2.1 protein. Our findings show that EVAP imaging methods enable the identification of conformational changes of endogenous Kv2 voltage sensors in tissue.

59 BASIC BIOLOGICAL SCIENCES↗
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 OSTI2023

Nanoscale Magnetic Domains in Polycrystalline Mn 3 Sn Films Imaged by a Scanning Single-Spin Magnetometer

Noncollinear antiferromagnets with novel magnetic orders, vanishingly small net magnetization, and exotic spin related properties hold enormous promise for developing next-generation, transformative spintronic applications. A major ongoing research focus of this community is to explore, control, and harness unconventional magnetic phases of this emergent material system to deliver state-of-the-art functionalities for modern microelectronics. Here we report direct imaging of magnetic domains of polycrystalline Mn 3 Sn films, a prototypical noncollinear antiferromagnet, using nitrogen-vacancy-based single-spin scanning microscopy. Nanoscale evolution of local stray field patterns of Mn 3 Sn samples are systematically investigated in response to external driving forces, revealing the characteristic “heterogeneous” magnetic switching behaviors in polycrystalline textured Mn 3 Sn films. Furthermore, our results contribute to a comprehensive understanding of inhomogeneous magnetic orders of noncollinear antiferromagnets, highlighting the potential of nitrogen-vacancy centers to study microscopic spin properties of a broad range of emergent condensed matter systems.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Systematic discovery of new nano-scale metastable intermetallic eutectic phases in laser rapid solidified Aluminum-Germanium alloy

Laser surface remelting of as-cast Al-Ge eutectic alloy is shown to produce ultrafine lamellar eutectic morphology with interlamellar spacing refined up to ∼60 nm and composed of FCC Al solid solution and unusual Al x Ge y intermetallic phases that do not form during near-equilibrium solidification. The microstructures are characterized and analyzed using a combination of selected area electron diffraction, high-resolution scanning transmission electron microscopy, energy dispersive X-ray spectroscopy to obtain high-resolution elemental maps, and atomistic modeling using density functional theory followed by atomic-scale image simulation. Depending on the local solidification conditions, the crystallography of the Al x Ge y intermetallic phases in the eutectic microstructure is either monoclinic (C 2/c) or monoclinic (P 2 1 ), with high densities of defects in both cases. This is in sharp contrast to the as-cast alloys that showed nominally pure Al and Ge phases with significant solute partitioning and equilibrium FCC and diamond cubic crystal structures, respectively. Corresponding kinetic phase diagrams are proposed to interpret the evolution of nano-lamellar eutectic morphologies with equilibrium Al and metastable Al x Ge y phases, and to explain increased solid solubility in the Al phases manifested by precipitation of ultrafine clusters of Ge. Furthermore, the reasons for the formation of these metastable eutectics under laser rapid solidification are discussed from the perspective of the competitive growth criterion.

Al-Ge eutectic↗
DOE OSTI2020

mpfit: a robust method for fitting atomic resolution images with multiple Gaussian peaks

The standard technique for sub-pixel estimation of atom positions from atomic resolution scanning transmission electron microscopy images relies on fitting intensity maxima or minima with a two-dimensional Gaussian function. While this is a widespread method of measurement, it can be error prone in images with non-zero aberrations, strong intensity differences between adjacent atoms or in situations where the neighboring atom positions approach the resolution limit of the microscope. Here we demonstrate mpfit, an atom finding algorithm that iteratively calculates a series of overlapping two-dimensional Gaussian functions to fit the experimental dataset and then subsequently uses a subset of the calculated Gaussian functions to perform sub-pixel refinement of atom positions. Based on both simulated and experimental datasets presented in this work, this approach gives lower errors when compared to the commonly used single Gaussian peak fitting approach and demonstrates increased robustness over a wider range of experimental conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Multi-modal Free-moving Data Fusion (MFDF) v1.0

Multi-modal Free-moving Data Fusion (MFDF) is a python package that implements low and high-level functionality for performing qualitative and quantitative gamma-ray imaging analyses. Such analyses enable the use of gamma-ray spectrometers and/or imagers to estimate the distribution and quantity of radiological materials in an environment. Free-moving 3D imaging requires additional information about the 3D trajectory and orientation of the system, however all of these methods can be applied to 2D static gamma-ray imaging as well. The package includes methods (MLEM and MAP) for distributed source reconstruction, methods (PSL) for point source reconstruction, the ability to ingest and utilize quantitative detector response functions (for absolute analyses), and the ability to perform 3D estimation of dose-rates from quantitative reconstructions.

Joshi, Tenzing↗
DOE OSTI2025

Imaging Photonic Resonances within an All‐Dielectric Metasurface via Photoelectron Emission Microscopy

Dielectric metasurfaces, through volume‐type photonic resonances, enable precise control of light‐matter interactions for applications including imaging, holography, and sensing. The application space of dielectric metasurfaces has extended from infrared to visible wavelengths by incorporating high refractive index materials, such as titanium dioxide (TiO 2 ). Understanding the fundamental and fabrication limits for these applications requires metrology with nanoscale resolution, sensitivity to electromagnetic fields within the meta‐atom volume, and far‐field excitation. In this work, photoelectron emission microscopy (PEEM) is used to image field distributions of photonic resonances in a TiO 2 metasurface excited with far‐field, visible‐wavelength illumination. The local volumetric field variations within the meta‐atoms are analyzed as a function of illumination angle and polarization by comparing photoelectron images to finite‐difference time‐domain simulations. This study determines the inelastic mean free path of very low‐energy (<1 eV) photoelectrons to be 35 ± 10 nm, which is comparable to the meta‐atom height thereby highlighting PEEM sensitivity to resonances within the volume. Additionally, the simulations reveal high sensitivity of PEEM images to an in‐plane component of the illumination k ‐vector. These results demonstrate that photoelectron imaging with subwavelength resolution offers unique advantages for examining light‐matter interactions in volume‐type (as opposed to surface) photonic modes within dielectric nanophotonic structures.

electron inelastic mean free path↗
DOE OSTI2024

Plasma Bubble Column Reactor: A High Throughput Reactor Design for Water Treatment

Plasma-based water treatment (PWT) is a promising technology that can degrade various emerging contaminants. However, PWT application on an industrially viable scale is hindered by the lack of an efficient reactor design that combines enhanced plasma-liquid contact with high liquid throughput. This work investigates the applicability of a bubble column gas-liquid contactor to PWT. A pulsed plasma bubble column reactor (PBCR) with a concentric rod-cylinder electrode configuration was used to correlate contaminant removal performance with the gas-liquid contact parameters of the bubble column. A surfactant, rhodamine B dye, and a nonsurfactant, caffeine, were used as model contaminants at µM concentration levels. The bubble column characteristics, i.e., gas holdup, bubble size distribution, and gas-liquid area, were measured as a function of superficial gas velocity using image-based methods. Degradation rates of both contaminants increased with gas flowrate. For caffeine, the increase was attributed to intensified bulk liquid mixing, while dye degradation increased due to the increased gas-liquid area. Ultimately, we show that bubble column contactors significantly improve the utilization of plasma-generated reactive species toward contaminant degradation by distributing them over a large contact area. As a result, a better match between the plasma species interfacial flux and the interfacial contaminant concentration leads to improved treatment energy efficiency. Furthermore, typical degradation energy efficiencies were ~10 g/kWh for caffeine and ~60 g/kWh for rhodamine B.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗