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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Remote sensing of Myriophyllum spicatum L. in a shallow, eutrophic lake

An aerial 35 mm system was used for the acquisition of vertical color and color infrared imagery of the submergent aquatic macrophytes of Lake Wingra, Wisconsin. A method of photographic interpretation of stem density classes is tested for its ability to make standing crop biomass estimates of Myriophyllum spicatum. The results of film image density analysis are significantly correlated with stem densities and standing crop biomass of Myriophyllum and with the biomass of Oedogonium mats. Photographic methods are contrasted with conventional harvest procedures for efficiency and accuracy.

Gustafson, T. D.↗
DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Toward accurate measurement of electromagnetic field by retrieving and refining the center position of non-uniform diffraction disks in Lorentz 4D-STEM

Recent advancement in scanning transmission electron microscopy (STEM) allows the use of 4D-STEM, a technique that captures an electron diffraction pattern at each scan point in STEM, to measure electrostatic and magnetic potential and field in materials. However, accurate measurement, separation of the magnetic and electric signals, and removal of artifacts remain challenging, especially in the presence of complex non-uniform diffraction contrast within the disks. In this work, based on dynamic simulations of 4D-STEM patterns built upon superstructures consisting of millions of atoms to account for different sample thickness and edge geometries, we show how the shape and intensity distribution of the central disk are affected by multiple scattering. We propose a robust refinement procedure through iteration of the spin-sensitive peak position of the disk-center in the circular Hough transform filtered images from experimental Lorentz 4D-STEM dataset after minimizing the possible artifacts, such as those due to the change of thickness, dynamic scattering, and scanning process. We verify that caution must be taken as in practice the rigid-disk-shift model used to reconstruct induction maps can easily break down due to disk-protrusion when there exists a nonconstant phase gradient or thickness within the width of the probe. Through quantitative analysis and comparing experiment with calculation the effect of the non-spin-related intensity distribution inside the disk as well as that causes the disk shift due to the intensity-protrusion can be removed, and high-quality magnetic field mapping is possible.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2003

Effect of stromal-cell-derived factor 1 on stem-cell homing and tissue regeneration in ischaemic cardiomyopathy

BACKGROUND: Myocardial regeneration via stem-cell mobilisation at the time of myocardial infarction is known to occur, although the mechanism for stem-cell homing to infarcted tissue subsequently and whether this approach can be used for treatment of ischaemic cardiomyopathy are unknown. We investigated these issues in a Lewis rat model (ligation of the left anterior descending artery) of ischaemic cardiomyopathy. METHODS: We studied the effects of stem-cell mobilisation by use of granulocyte colony-stimulating factor (filgrastim) with or without transplantation of syngeneic cells. Shortening fraction and myocardial strain by tissue doppler imaging were quantified by echocardiography. FINDINGS: Stem-cell mobilisation with filgrastim alone did not lead to engraftment of bone-marrow-derived cells. Stromal-cell-derived factor 1 (SDF-1), required for stem-cell homing to bone marrow, was upregulated immediately after myocardial infarction and downregulated within 7 days. 8 weeks after myocardial infarction, transplantation into the peri-infarct zone of syngeneic cardiac fibroblasts stably transfected to express SDF-1 induced homing of CD117-positive stem cells to injured myocardium after filgrastim administration (control vs SDF-1-expressing cardiac fibroblasts mean 7.2 [SD 3.4] vs 33.2 [6.0] cells/mm2, n=4 per group, p<0.02) resulting in greater left-ventricular mass (1.24 [0.29] vs 1.57 [0.27] g) and better cardiac function (shortening fraction 9.2 [4.9] vs 17.2 [4.2]%, n=8 per group, p<0.05). INTERPRETATION: These findings show that SDF-1 is sufficient to induce therapeutic stem-cell homing to injured myocardium and suggest a strategy for directed stem-cell engraftment into injured tissues. Our findings also indicate that therapeutic strategies focused on stem-cell mobilisation for regeneration of myocardial tissue must be initiated within days of myocardial infarction unless signalling for stem-cell homing is re-established.

NASA Discipline Cardiopulmonary↗
DOE OSTI2026

Beyond Contrast Transfer: Spectral SNR as a Finite-Dose Metric for STEM Phase Retrieval

The contrast transfer function (CTF) is widely used to evaluate phase retrieval methods in scanning transmission electron microscopy (STEM), including center-of-mass imaging, parallax imaging, direct ptychography, and iterative ptychography. However, the CTF reflects only the maximum usable signal, neglecting the effects of finite electron fluence and the Poisson-limited nature of detection. As a result, it can significantly overestimate practical performance, especially in low-dose regimes. Here, we employ the spectral signal-to-noise ratio (SSNR), as a finite-dose statistical framework to evaluate the recoverable signal as a function of spatial frequency. Using numerical reconstructions of white-noise objects, we show that center-of-mass, parallax, and direct ptychography exhibit dose-independent SSNRs, with close-form analytic expressions. In contrast, iterative ptychography exhibits a surprising dose dependence: at low fluence, its SSNR converges to that of direct ptychography; at high fluence, it saturates at a value consistent with the maximum detective quantum efficiency predicted by recent quantum Fisher information bounds. The results highlight the limitations of CTF-based evaluation and motivate SSNR as a more accurate, finite-dose metric for assessing STEM phase retrieval methods.

STEM phase retrieval↗
DOE OSTI2023

Selective oxidation and nickel enrichment hinders the repassivation kinetics of multi-principal element alloy surfaces

Robust and sustained corrosion resistance in multi-principal element alloys (MPEA) requires rapid repassivation, i.e. regrowth of the passive layer once it is damaged or destroyed at the surface. In this study, we show that the repassivation of Al 0.1 CrCoFeNi MPEA in 0.6 M NaCl solution is hindered at pH of ~ 2.4 - 6.8 due to the formation of a Ni-enriched subsurface layer as a result of selective oxidation and dissolution of several principal elements, which can be fully restored at pH of ~ 14 from the oxidation of all principal elements. Specifically, surface characterization via X-ray photoelectron spectroscopy (XPS), high-angle annular dark-field (HAADF) imaging in scanning transmission electron microscopy (STEM), and atom probe tomography (APT), are coupled with density functional theory (DFT) calculations to determine surface composition, oxidation state, and electron work function to uncover the structural origin of the pH-dependent repassivation mechanisms. It was found that selective oxidation of Cr, Co, and Fe in the acidic to neutral solutions altered the surface composition to be significantly enriched in Ni as compared to the bulk. Once the original passive film is destroyed locally by either pitting or tribocorrosion, this altered surface composition exhibited a much poorer repassivation capability due to the increased electron work function and reduced surface reactivity at higher Ni concentration. Finally, these understandings could shed light on the future compositional design of non-equiatomic MPEAs towards sustained repassivation and corrosion resistance over a wide pH range.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Complexions at the iron-magnetite interface

Synthesizing distinct phases and controlling crystalline defects are key concepts in materials design. These approaches are often decoupled, with the former grounded in equilibrium thermodynamics and the latter in nonequilibrium kinetics. By unifying them through defect phase diagrams, we can apply phase equilibrium models to thermodynamically evaluate defects—including dislocations, grain boundaries, and phase boundaries—establishing a theoretical framework linking material imperfections to properties. Using scanning transmission electron microscopy (STEM) with differential phase contrast (DPC) imaging, we achieve the simultaneous imaging of heavy Fe and light O atoms, precisely mapping the atomic structure and chemical composition at the iron-magnetite (Fe/Fe 3 O 4 ) interface. We identify a well-ordered two-layer interface-stabilized phase state (referred to as complexion) at the Fe[001]/Fe 3 O 4 [001] interface. Using density-functional theory (DFT), we explain the observed complexion and map out various interface-stabilized phases as a function of the O chemical potential. The formation of complexions increases interface adhesion by 20% and alters charge transfer between adjacent materials, impacting transport properties. Our findings highlight the potential of tunable defect-stabilized phase states as a degree of freedom in materials design, enabling optimized corrosion protection, catalysis, and redox-driven phase transitions, with applications in materials sustainability, efficient energy conversion, and green steel production.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗
DOE OSTI2025

Distributed Cross-Channel Hierarchical Aggregation for Foundation Models

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources—such as varying physical groundings or data acquisition systems—and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Tsaris, Aristeidis (aris) [ORNL] (ORCID:0000000277↗
NASA NTRS2015

Fe and O EELS Studies of Ion Irradiated Murchison CM2 Carbonaceous Chondrite Matrix

Introduction: The physical and chemical response of hydrated carbonaceous chondrite materials to space weathering processes is poorly understood. Improving this understanding is a key part of establishing how regoliths on primitive carbonaceous asteroids respond to space weathering processes, knowledge that supports future sample return missions (Hayabusa 2 and OSIRISREx) that are targeting objects of this type. We previously reported on He+ irradiation of Murchison matrix and showed that the irradiation resulted in amorphization of the matrix phyllosilicates, loss of OH, and surface vesiculation. Here, we report electron energy-loss spectroscopy (EELS) measurements of the irradiated material with emphasis on the Fe and O speciation. Sample and Methods: A polished thin section of the Murchison CM2 carbonaceous chondrite was irradiated with 4 kilovolts He(+) (normal incidence) to a total dose of 1 x 10(exp 18) He(+) per square centimeter. We extracted thin sections from both irradiated and unirradiated regions in matrix using focused ion beam (FIB) techniques with electron beam deposition for the protective carbon strap to minimize surface damage artifacts from the FIB milling. The FIB sections were analyzed using a JEOL 2500SE scanning and transmission electron microscope (STEM) equipped with a Gatan Tridiem imaging filter. EELS spectra were collected from 50 nanometer diameter regions with an energy resolution of 0.7 electronvolts FWHM at the zero loss. EELS spectra were collected at low electron doses to minimize possible artifacts from electron-beam irradiation damage. Results and Discussion: Fe L (sub 2,3) EELS spectra from matrix phyllosilicates in CM chondrites show mixed Fe(2+)/Fe(3+) oxidation states with Fe(3+)/Sigma Fe approximately 0.5. Fe L(sub 2,3) spectra from the irradiated/ amorphized matrix phyllosilicates show higher Fe(2+)/Fe(3+) ratios compared to spectra obtained from pristine material at depths beyond the implantation/amorphization layer. We also obtained O Ka spectra from phyllosilicates in both regions of the sample. The O Ka spectra show a pre-edge feature at approximately 530.5 electronvolts that is related to O 2p states hybridized with Fe 3d states. The intensity ratio of the O Ka pre-edge peak relative to the main part of the O Ka edge (that results from transitions of O 1s to 2p states) is lower in the irradiated layer compared to the pristine material and may reflect the loss of O (as OH) as was observed by IR spectroscopy. Conclusions: In addition to amorphization and OH loss, EELS spectra of He(+) irradiated matrix phyllosilicates in Murchison show that some of the Fe(3+) is reduced to Fe(2+). Spectral deconvolution is underway to extract quantitative ratios from the EELS spectra.

Keller, L. P.↗
NASA NTRS2018

Hi-C2.1 Observations of Solar Jetlets at Edges of Network Lanes

We present high resolution (0.2”) high cadence (5s) extreme ultraviolet (EUV) observations of small-­scale jetlet-­like features and spicule-­like features observed with NASA’s High-­resolution Coronal Imager2.1 (Hi-C) during its 5‐min observing span. We investigate the magnetic setting of 4 on-disk jetlets and 2 on-­disk spicules by using high resolution 172A images from Hi-­‐C and EUV images from Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) and line-­of-­sight magnetograms from SDO/Helioseismic and Magnetic Imager (HMI). The jetlets and spicules are at edges of magnetic network lanes. From magnetograms co-­aligned with the Hi-­C and AIA images, we find that the jetlets stem from sites of likely flux cancelation between merging majority-polarity and weaker minority-­polarity flux, but in contrast to larger jetlets observed by IRIS some do not show obvious enhanced brightenings at their base. Based on the similarity of these observations of approx. 4 obvious Hi-­C small jetlets with our previous observations of 10 IRIS larger jetlets and approx. 30 coronal jets in quiet regions and coronal holes, we infer that flux cancelation is probably the essential process in the buildup and triggering of jetlets. Our observations suggest that network jetlet eruptions, large and small, are small-scale analogs of both larger-­scale coronal jet eruptions and the still-­larger-­scale eruptions that makemajor CMEs. For simplicitywe will use termjetlets for jetlet-­like features and spicules for spicule-­like features.

Panesar, Navdeep K.↗
DOE OSTI2022

Analysis of Interpretable Data Representations for 4D-STEM Using Unsupervised Learning

Abstract Understanding the structure of materials is crucial for engineering devices and materials with enhanced performance. Four-dimensional scanning transmission electron microscopy (4D-STEM) is capable of mapping nanometer-scale local crystallographic structure over micron-scale field of views. However, 4D-STEM datasets can contain tens of thousands of images from a wide variety of material structures, making it difficult to automate detection and classification of structures. Traditional automated analysis pipelines for 4D-STEM focus on supervised approaches, which require prior knowledge of the material structure and cannot describe anomalous or deviant structures. In this article, a pipeline for engineering 4D-STEM feature representations for unsupervised clustering using non-negative matrix factorization (NMF) is introduced. Each feature is evaluated using NMF and results are presented for both simulated and experimental data. It is shown that some data representations more reliably identify overlapping grains. Additionally, real space refinement is applied to identify spatially distinct sample regions, allowing for size and shape analysis to be performed. This work lays the foundation for improved analysis of nanoscale structural features in materials that deviate from expected crystallographic arrangement using 4D-STEM.

Bruefach, Alexandra (ORCID:0000000209323477)↗
DOE OSTI2025

Investigating Grain Structure and Microcracking in SiCf-SiCm Composites Using 4D-STEM

Silicon carbide (SiC) fiber-reinforced SiC matrix composites (SiCf-SiCm) are promising materials for accident-tolerant fuel (ATF) cladding in light water reactors. This paper, using four-dimensional scanning transmission electron microscopy (4D-STEM), studied the microstructure of SiCf-SiCm at nanoscale and provided grain statistics at the CVI/fiber region. Here, the results reveal that CVI region mainly contain irregular sub-micron grains (115 nm to 1800 nm) with a preferred orientation, while the fiber region has nano equiaxed grains (21 nm to 78 nm) with random orientations. Centered dark field imaging over the fiber regions verified the grain size measurement by 4D-STEM. Through 4D-STEM virtual dark field imaging and orientation mapping, intragranular propagation was identified as the fracture mechanism for a microcrack observed within the CVI region. The amorphous pyrolytic carbon layer was also shown to effectively arrest the microcrack.

4D-STEM↗
DOE OSTI2023

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, in this work, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

36 MATERIALS SCIENCE↗
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↗
NASA NTRS1984

The vectorization of a ray tracing program for image generation

Ray tracing is a widely used method for producing realistic computer generated images. Ray tracing involves firing an imaginary ray from a view point, through a point on an image plane, into a three dimensional scene. The intersections of the ray with the objects in the scene determines what is visible at the point on the image plane. This process must be repeated many times, once for each point (commonly called a pixel) in the image plane. A typical image contains more than a million pixels making this process computationally expensive. A traditional ray tracing program processes one ray at a time. In such a serial approach, as much as ninety percent of the execution time is spent computing the intersection of a ray with the surface in the scene. With the CYBER 205, many rays can be intersected with all the bodies im the scene with a single series of vector operations. Vectorization of this intersection process results in large decreases in computation time. The CADLAB's interest in ray tracing stems from the need to produce realistic images of mechanical parts. A high quality image of a part during the design process can increase the productivity of the designer by helping him visualize the results of his work. To be useful in the design process, these images must be produced in a reasonable amount of time. This discussion will explain how the ray tracing process was vectorized and gives examples of the images obtained.

Plunkett, D. J.↗
DOE OSTI2023

Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy

High-resolution microscopy of deep tissue with large field-of-view (FOV) is critical for elucidating organization of cellular structures in plant biology. Microscopy with an implanted probe offers an effective solution. However, there exists a fundamental trade-off between the FOV and probe diameter arising from aberrations inherent in conventional imaging optics (typically, FOV < 30% of diameter). Here, we demonstrate the use of microfabricated non-imaging probes (optrodes) that when combined with a trained machine-learning algorithm is able to achieve FOV of 1x to 5x the probe diameter. Further increase in FOV is achieved by using multiple optrodes in parallel. With a 1 × 2 optrode array, we demonstrate imaging of fluorescent beads (including 30 FPS video), stained plant stem sections and stained living stems. Our demonstration lays the foundation for fast, high-resolution microscopy with large FOV in deep tissue via microfabricated non-imaging probes and advanced machine learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations

This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

47 OTHER INSTRUMENTATION↗