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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Energy Barriers for H 2 Uptake and Release by Defects in Boron Nitride

Density functional theory simulations (DFT) have been used in combination with the climbing image nudged elastic band method (CI-NEB) to determine the energy barriers required to dissociate H 2 at four types of h-BN defects and evaluate the potential for reacted H species to hop to adjacent available sites. The defects were selected based on previous work in which ab initio thermodynamics was used to identify specific defects with optimal thermodynamics for reversible H 2 storage, that is, the defects would dissociate molecular H 2 while not binding H species too strongly so that hydrogen release is favorable. The four defects investigated are the N and B monovacancies, which only offer either B or N defective sites respectively, as well as larger cluster vacancy defects named 3V(1B2N) and hexagonal 6V(3B3N), which present a mixture of B and N defect sites available for H binding. While homogeneous H 2 dissociation by B and N monovacancies have energy barriers larger than 0.4 eV, we found that mixed BN terminated defects have energy barrier lower than 0.4 eV for H 2 dissociation. In particular, H 2 dissociation by the hexagonal 6V(3B3N) defect, exclusively made of Frustrated Lewis Pairs (FLPs), is almost barrierless. The energy barrier for subsequent H hopping to available adjacent B or N sites of the defect were also calculated. It was found that the hopping of H species generally involves high energy barriers ranging from 0.26 eV to 2.57 eV. In conclusion, this work completes previous thermodynamic theoretical investigations and provides a more comprehensive picture of H 2 reaction with defective h-BN for potential H 2 storage and recovery.

Sassi, Michel J. [Pacific Northwest National Labor↗
DOE OSTI2021

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Experimental and computational study of phase space dynamics in strongly coupled plasmas with steep density gradients

Understanding how plasmas thermalize when density gradients are steep remains a fundamental challenge in plasma physics, with direct implications for fusion experiments and astrophysical phenomena. Standard hydrodynamic models break down in these regimes, and kinetic theories make predictions that have never been directly tested. Here, we present the first detailed phase-space measurements of a strongly coupled plasma as it evolves from sharp density gradients to thermal equilibrium. Using laser-induced fluorescence imaging of an ultracold calcium plasma, we track the complete ion distribution function f(x,v,t). We discover that commonly used kinetic models (Bhatnagar–Gross–Krook and Lenard–Bernstein) overpredict thermalization rates, even while correctly capturing the initial counterstreaming plasma formation. Our measurements reveal that the initial ion acceleration response scales linearly with electron temperature, and that the simulations underpredict the initial ion response. In our geometry we demonstrate the formation of well-controlled counterpropagating plasma beams. This experimental platform enables precision tests of kinetic theories and opens new possibilities for studying plasma stopping power and flow-induced instabilities in strongly coupled systems.

Bergeson, Scott (ORCID:0000000231249226)↗
DOE OSTI2020

DNA origami protection and molecular interfacing through engineered sequence-defined peptoids

DNA nanotechnology has established approaches for designing programmable and precisely controlled nanoscale architectures through specific Watson−Crick base-pairing, molecular plasticity, and intermolecular connectivity. In particular, superior control over DNA origami structures could be beneficial for biomedical applications, including biosensing, in vivo imaging, and drug and gene delivery. However, protecting DNA origami structures in complex biological fluids while preserving their structural characteristics remains a major challenge for enabling these applications. Here, we developed a class of structurally well-defined peptoids to protect DNA origamis in ionic and bioactive conditions and systematically explored the effects of peptoid architecture and sequence dependency on DNA origami stability. The applicability of this approach for drug delivery, bioimaging, and cell targeting was also demonstrated. A series of peptoids (PE1–9) with two types of architectures, termed as “brush” and “block,” were built from positively charged monomers and neutral oligo-ethyleneoxy monomers, where certain designs were found to greatly enhance the stability of DNA origami. Through experimental and molecular dynamics studies, we demonstrated the role of sequence-dependent electrostatic interactions of peptoids with the DNA backbone. We showed that octahedral DNA origamis coated with peptoid (PE2) can be used as carriers for anticancer drug and protein, where the peptoid modulated the rate of drug release and prolonged protein stability against proteolytic hydrolysis. Finally, we synthesized two alkyne-modified peptoids (PE8 and PE9), conjugated with fluorophore and antibody, to make stable DNA origamis with imaging and cell-targeting capabilities. Our results demonstrate an approach toward functional and physiologically stable DNA origami for biomedical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2025

Remote detection of radioactive material using a short-pulse CO 2 laser

Detection of radioactive material at distances greater than the radiated particle range is an important goal with applications in areas such as national defense and disaster response. Here, we demonstrate avalanche-breakdown-based remote detection of a 3.6 mCi α-particle source at a standoff distance of 10 m, using 70 ps, long-wave infrared (λ = 9.2 µm) CO 2 laser pulses. This is ∼10 times longer than our previous results using a mid-IR laser. The primary detection method is direct backscatter from microplasmas generated in the laser focal volume. The backscatter signal is amplified as it propagates back through the CO 2 laser chain, enhancing sensitivity by >100 times. Here we also characterize breakdown plasmas with fluorescence imaging, and present a simple model to estimate backscattered signals as a function of the seed density profile in the laser focal volume. All of this is achieved with a relatively long-drive laser focal geometry (f/200) that is readily scalable to >100 m.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images

Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Chemo–mechanical properties of carbon fiber reinforced geopolymer interphase

Geopolymers, as a potentially environmentally friendly alternative to Portland cement, are increasingly attracting attention in the construction industry. Various methods have been applied for customizing the properties of geopolymers and improving their commercial viability. One of the promising methods for refining the properties of geopolymers such as their toughness is the use of short fibers. The effectiveness of a high-strength short fiber in the geopolymer matrix is largely dependent on the interfacial bonding between the fiber and its surrounding matrix. While the importance of this interfacial chemistry is highlighted in the literature, the characteristics of this bonding structure have not been fully understood. In this paper, we aim to investigate the bonding mechanism between the carbon fiber and metakaolin-based geopolymer matrix. For the first time, the existence and nature of the chemical bonding at the interfacial region (interphase) between carbon fiber and geopolymer matrix has been revealed. X-ray pair distribution function computed tomography (PDF-CT), field emission-scanning electron microscopy imaging, and nanoindentation techniques are employed to discern the chemo-mechanical properties of the interphase. PDF-CT results show the emergence of a new atom-atom correlation at the interfacial region (around 1.82 angstrom). This correlation is a characteristic of interfacial bonding between the fiber and its surrounding matrix, where the existence of chemical linkages (potentially Al-V-O-C) between fibers and the matrix contributes to the adhesion between the two constituents making up the composite. Due to such chemical bonding, the nanomechanical properties of the interfacial region fall between that of the carbon fiber and geopolymer. The combination of advanced techniques is proved useful for enhancing our understanding of the interfacial chemistry between fibers and the binding matrix. This level of knowledge facilitates the engineering of composite systems through the manipulation of their nanostructure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Heterotrimeric G‐protein, Gi1, is involved in the regulation of proliferation, neuronal migration, and dendrite morphology during cortical development in vivo

Abstract Heterotrimeric G‐proteins are composed of α, β, and γ subunits, and function as signal transducers. Critical roles of the α‐subunits of Gi/o family heterotrimeric G‐proteins, Gαi2, and Gαo1, have so far been reported in brain development and neurodevelopmental disorders. In this study, we tried to clarify the role of Gαi1, α‐subunit of another Gi/o family member Gi1, during corticogenesis, based on the recent identification of its gene abnormalities in neurodevelopmental disorders. In western blot analyses, Gαi1 was found to be expressed in mouse brain in a developmental stage‐dependent manner. Morphological analyses revealed that Gαi1 was broadly distributed in cerebral cortex with relatively high expression in the ventricular zone (VZ) at embryonic day (E) 14. Meanwhile, Gαi1 was enriched in membrane area of yet unidentified early mitotic cells in the VZ and the marginal zone at E14. Acute knockdown of Gαi1 with in utero electroporation in cerebral cortex caused cell cycle elongation of the neural progenitor cells and promoted their cell cycle exit. Gαi1‐deficient cortical neurons also exhibited delayed radial migration during corticogenesis, with abnormally elongated leading processes and hampered nucleokinesis. In addition, silencing of Gαi1 prevented basal dendrite development. The migration and dendritic phenotypes were at least partially rescued by an RNAi‐resistant version of Gαi1. Collectively, these results strongly suggest a crucial role of Gi1 in cortical development, and disturbance of its function may cause deficits in synaptic network formation, leading to neurodevelopmental disorders. image

Hamada, Nanako↗
DOE OSTI2021

New Determination of Fundamental Properties of Palomar 5 Using Deep DESI Imaging Data

The legacy imaging surveys for the Dark Energy Spectroscopic Instrument project provides multiple-color photometric data, which are about 2 mag deeper than those from the SDSS. In this study, we redetermine the fundamental properties for an old halo globular cluster of Palomar 5 based on these new imaging data, including structure parameters, stellar population parameters, and luminosity and mass functions. These characteristics, together with its tidal tails, are key for dynamical studies of the cluster and constraining the mass model of the Milky Way. By fitting the King model to the radial surface density profile of Palomar 5, we derive the core radius of [[Formula]], tidal radius of [[Formula]], and concentration parameter of c = 0.78 ± 0.04. We apply a Bayesian analysis method to derive the stellar population properties and get an age of 11.508 ± 0.027 Gyr, metallicity of [Fe/H] = −1.798 ± 0.014, reddening of E(B − V) = 0.0552 ± 0.0005, and distance modulus of (m−M){sub 0} = 16.835±0.006. The main-sequence luminosity and mass functions for both the cluster center and tidal tails are investigated. The luminosity and mass functions at different distances from the cluster center suggest that there is obvious spatial mass segregation. Many faint low-mass stars have been evaporated at the cluster center, and the tidal tails are enhanced by low-mass stars. Both the concentration and relaxation times suggest that Palomar 5 is a totally relaxed system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy

We report on Bayesian estimation of the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740+6620, conditional on pulse-profile modeling of Neutron Star Interior Composition Explorer X-ray Timing Instrument event data. We condition on informative pulsar mass, distance, and orbital inclination priors derived from the joint North American Nanohertz Observatory for Gravitational Waves and Canadian Hydrogen Intensity Mapping Experiment/Pulsar wideband radio timing measurements of Fonseca et al. We use XMM-Newton European Photon Imaging Camera spectroscopic event data to inform our X-ray likelihood function. The prior support of the pulsar radius is truncated at 16 km to ensure coverage of current dense matter models. We assume conservative priors on instrument calibration uncertainty. We constrain the equatorial radius and mass of PSR J0740+6620 to be ${12.39}_{-0.98}^{+1.30}$ km and ${2.072}_{-0.066}^{+0.067}$ M ⊙ respectively, each reported as the posterior credible interval bounded by the 16% and 84% quantiles, conditional on surface hot regions that are non-overlapping spherical caps of fully ionized hydrogen atmosphere with uniform effective temperature; a posteriori, the temperature is ${\mathrm{log}}_{10}(T\,[{\rm{K}}])={5.99}_{-0.06}^{+0.05}$ for each hot region. All software for the X-ray modeling framework is open-source and all data, model, and sample information is publicly available, including analysis notebooks and model modules in the Python language. Furthermore, our marginal likelihood function of mass and equatorial radius is proportional to the marginal joint posterior density of those parameters (within the prior support) and can thus be computed from the posterior samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Quantitative Scanning Transmission Electron Microscopy for Materials Science: Imaging, Diffraction, Spectroscopy, and Tomography

Scanning transmission electron microscopy (STEM) is one of the most powerful characterization tools in materials science research. Due to instrumentation developments such as highly coherent electron sources, aberration correctors, and direct electron detectors, STEM experiments can examine the structure and properties of materials at length scales of functional devices and materials down to single atoms. STEM encompasses a wide array of flexible operating modes, including imaging, diffraction, spectroscopy, and 3D tomography experiments. This review outlines many common STEM experimental methods with a focus on quantitative data analysis and simulation methods, especially those enabled by open source software. The hope is to introduce both classic and new experimental methods to materials scientists and summarize recent progress in STEM characterization. The review also discusses the strengths and weaknesses of the various STEM methodologies and briefly considers promising future directions for quantitative STEM research.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Conditional Autoencoder for Galaxy Photometric Parameter Estimation

Astronomical photometric surveys routinely image billions of galaxies, and traditionally infer the parameters of a parametric model for each galaxy. This approach has served us well, but the computational expense of deriving a full posterior probability distribution function is a challenge for increasingly ambitious surveys. In this paper, we use deep learning methods to characterize galaxy images, training a conditional autoencoder on mock data. The autoencoder can reconstruct and denoise galaxy images via a latent space engineered to include semantically meaningful parameters, such as brightness, location, size, and shape. Our model recovers galaxy fluxes and shapes on mock data with a lower variance than the Hyper Suprime-Cam photometry pipeline, and returns reasonable answers even for inputs outside the range of its training data. When applied to data in the training range, the regression errors on all extracted parameters are nearly unbiased with a variance near the Cramr-Rao bound.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Morphological landscapes from high content imaging reveal cytokine priming strategies that enhance mesenchymal stromal cell immunosuppression

Abstract Successful clinical translation of mesenchymal stromal cell (MSC) products has not been achieved in the United States and may be in large part due to MSC functional heterogeneity. Efforts have been made to identify “priming” conditions that produce MSCs with consistent immunomodulatory function; however, challenges remain with predicting and understanding how priming impacts MSC behavior. The purpose of this study was to develop a high throughput, image‐based approach to assess MSC morphology in response to combinatorial priming treatments and establish morphological profiling as an effective approach to screen the effect of manufacturing changes (i.e., priming) on MSC immunomodulation. We characterized the morphological response of multiple MSC lines/passages to an array of Interferon‐gamma (IFN‐γ) and tumor necrosis factor‐⍺ (TNF‐⍺) priming conditions, as well as the effects of priming on MSC modulation of activated T cells and MSC secretome. Although considerable functional heterogeneity, in terms of T‐cell suppression, was observed between different MSC lines and at different passages, this heterogeneity was significantly reduced with combined IFN‐γ/TNF‐⍺ priming. The magnitude of this change correlated strongly with multiple morphological features and was also reflected by MSC secretion of immunomodulatory factors, for example, PGE2, ICAM‐1, and CXCL16. Overall, this study further demonstrates the ability of priming to enhance MSC function, as well as the ability of morphology to better understand MSC heterogeneity and predict changes in function due to manufacturing.

Andrews, Seth H.↗
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 OSTI2025

Near-field infrared imaging of polar domain walls in Ni 3 TeO 6

Domain walls are leading platforms for the development of ultra-low power switching and memory devices due to their potential to be moved, created, and erased in real time and to mitigate heat flux. Interface vs wavelength size effects unfortunately preclude the measurement of phonons by traditional spectroscopic techniques, so it has been challenging to unravel the primary excitations of the lattice and the symmetries that they represent across these functional interfaces. In this work, we employ synchrotron-based near-field infrared nanospectroscopy to image polar domain walls in multiferroic Ni 3 TeO 6 . This is a unique platform because, in addition to hosting polar and chiral domains that are interlocked with one another, Ni 3 TeO 6 displays both charged and neutral interfaces depending upon the direction allowing the development of structure–property relations. From a local structure and a strain point of view, we find charged walls that are twice as wide as neutral walls as well as strong frequency shifts of vibrational modes across the charged walls. The near-field amplitude drops across the walls as well. We discuss these trends in terms of polarization and chirality as well as phonon lifetimes at functional interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigation of the Anisotropy of Y-Ba-Cu-O Thin Films With Photoemission Electron Microscopy

Understanding the anisotropy in the electrical properties of YBa2Cu3O7-δ (YBCO) is important for understanding the fundamental mechanism of superconductivity in unconventional superconductors. Additionally it is also essential for achieving uniform and controllable critical currents and resistances in YBCO Josephson junctions. Here, this study correlates work function measurements of different intrinsic regions on YBCO thin films, obtained through PhotoEmission Electron Microscopy (PEEM), with in plane tunneling data and helium ion imaging. YBCO thin films, 40 nm in thickness, were selectively irradiated with 300 keV helium using a Pelletron to investigate the effects of irradiation compared to unirradiated regions. Post-irradiation PEEM images revealed distinct contrasts between irradiated and non-irradiated regions, suggesting a metal-to-insulator transition. In non-irradiated areas, a domain pattern was detected, showing a 25 meV difference in work function, attributed to CuO and BaO rich surface regions. A similar domain structure was observed using high-resolution helium ion imaging; however, the helium ion beam can damage the YBCO, making PEEM a valuable non-destructive alternative. This work highlights the correlation between PEEM measurements and in-plane tunneling properties of YBCO, providing insights into material orientation and its impact on Josephson junction performance. Furthermore, it offers a non-destructive approach to understanding the anisotropy of cuprate materials, its superconducting properties, and the factors affecting Josephson junction performance.

Qu, Rochelle Y. [Univ. of California, Riverside, C↗