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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Applications of persistent homology in nuclear collisions

Here, we introduce a novel set of observables associated to the rapidly developing field of persistent homology for the quantitative characterization of nuclear collisions and their evolution. Persistent homology allows for the identification of topological and homological characteristics of distributions in multidimensional spaces. We demonstrate here how to apply the tool kit of persistent homology to the extraction of novel clustering signatures and the identification of long-range flow correlations in the particle production process of nuclear collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems

Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. Here this paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated subregion. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.

42 ENGINEERING↗
DOE OSTI2025

Global Archaeal Diversity Revealed Through Massive Data Integration: Uncovering Just Tip of Iceberg

The domain of Archaea has gathered significant interest for its ecological and biotechnological potential and its role in helping us to understand the evolutionary history of Eukaryotes. In comparison to the bacterial domain, the number of adequately described members in Archaea is relatively low, with less than 1000 species described. It is not clear whether this is solely due to the cultivation difficulty of its members or, indeed, the domain is characterized by evolutionary constraints that keep the number of species relatively low. Based on molecular evidence that bypasses the difficulties of formal cultivation and characterization, several novel clades have been proposed, enabling insights into their metabolism and physiology. Given the extent of global sampling and sequencing efforts, it is now possible and meaningful to question the magnitude of global archaeal diversity based on molecular evidence. To do so, we extracted all sequences classified as Archaea from 500 thousand amplicon samples available in public repositories. After processing through our highly conservative pipeline, we named this comprehensive resource the ‘Global Archaea Diversity’ (GAD), which encompassed nearly 3 million molecular species clusters at 97% similarity, and organized it into over 500 thousand genera and nearly 100 thousand families. Saline environments have contributed the most to the novel taxa of this previously unseen diversity. The majority of those 16S rRNA gene sequence fragments were verified by matches in metagenomic datasets from IMG/M. These findings reveal a vast and previously overlooked diversity within the Archaea, offering insights into their ecological roles and evolutionary importance while establishing a foundation for the future study and characterization of this intriguing domain of life.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Revealing the Phase Separation Behavior of Thermodynamically Immiscible Elements in a Nanoparticle

Phase-separation is commonly observed in multimetallic nanomaterials, yet it is not well understood how immiscible elements distribute in a thermodynamically stable nanoparticle. Herein, we studied the phase-separation of Au and Rh in nanoparticles using electron microscopy and tomography techniques. The nanoparticles were thermally annealed to form thermodynamically stable structures. HAADF-STEM and EDS characterizations reveal that Au and Rh segregate into two domains while their miscibility is increased. Using aberration-corrected HAADF-STEM and atomic electron tomography, we show that the increased solubility of Au in Rh is achieved by forming Au clusters and single atoms inside the Rh domains and on the Rh surface. Furthermore, based on the three-dimensional reconstruction of a AuRh nanoparticle, we can visualize the uneven interface that is embedded in the nanoparticle. Finally, the results advance our understanding on the nanoscale thermodynamic behavior of metal mixtures, which is crucial for the optimization of multimetallic nanostructures for many applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

4D-STEM Coupled with Unsupervised Machine Learning to Reveal at Large-Scale the Microstructural Evolution in Li- and Mn-Rich Cathodes

Li- and Mn-rich (LMR) layered oxides are known to exhibit a thin surface reconstruction layer, which grows during electrochemical cycling in a manner that depends on exposed crystallographic facets, cycling conditions, and electrolyte chemistry. Direct characterization of this layer has traditionally relied on high-resolution electron microscopy, which is inherently limited to small fields of view. Here, we employ four-dimensional scanning transmission electron microscopy (4D-STEM) combined with unsupervised machine-learning clustering to quantitatively map phase distributions over large areas and track their evolution in LMR cathodes during electrochemical aging. Our results show that the surface reconstruction layer consists predominantly of a rocksalt phase, whose thickness varies across different facets following activation cycling and becomes substantially thicker and more uniform during calendar aging. In contrast, a spinel-like phase is observed within the particle bulk. Large-area phase mapping and correlative high-resolution imaging reveal that this spinel-like phase preferentially nucleates at bulk crystallographic defects, including boundaries between 60°-rotated layered domains and associated mixed-phase regions, rather than exclusively at the particle surface. Our findings establish a mechanistic distinction between surface-driven rocksalt formation and bulk-defect-mediated spinel nucleation while demonstrating the unique capability of 4D-STEM to provide statistically robust, mesoscale insight into complex phase-evolution processes in LMR cathodes.

4D-STEM↗
DOE OSTI2023

Small-Magnitude Seismic Swarms in Central Utah (US): Interactions of Regional Tectonics, Local Structures and Hydrothermal Systems

Swarms in Central Utah are situated in the complex transition between the Basin and Range (BR) province and the Colorado Plateau. Transecting transverse structures, volcanic deposits, and hydrothermal systems complicate the extensional BR horst and graben structures and provide a multitude of plausible triggering mechanisms. Revisiting the catalog of the University of Utah Seismograph Stations (1981–2022), we analyze spatio-temporal patterns and characteristic features of seismic sequences. Swarms with alternating seismicity rates, bursts, and longer swarms with persistent moment release exhibit a remarkable diversity in temporal evolution. Swarm durations do not scale with cumulative seismic moment: swarms lasting less than 1 day can have similar cumulative seismic moments as month-long swarms. Here, we observe stationary swarms re-occurring for years (e.g., Mineral Mountains), as well as singular swarms in low-seismicity areas (e.g., activating a local structure). The swarms show a pronounced heterogeneity in triggering and driving mechanisms, observed in the detailed analysis of exemplary sequences (detections, relocations, moment tensors, waveform-based clustering, and repeater analysis). The 2022 Sevier Valley sequence activated a BR-related normal fault, the first resolved fault plane in the valley since 1983. The 2011 Circleville sequence is interpreted as a swarm triggered by mainshock-aftershock activity characterized by increasing magnitudes, changing rupture mechanisms, and a concentration of highly similar events in the second part of the sequence. By jointly discussing exemplary sequences and catalog statistics, we draw a comprehensive picture of swarm activity and its relation to geothermal and tectonic activity.

58 GEOSCIENCES↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of Water Extraction on Malonamide Aggregation: A Molecular Dynamics and Graph Theoretic Approach

Solution structure in liquid-liquid extraction affects the efficacy of separation; however, even for simplified organic phases, structural characterization and attribution of aggregation to intermolecular interactions are fundamental challenges. We investigate water uptake into organic phases for two malonamides commonly applied to actinide and lanthanide separations. Extracted water induces reorganization of the amphiphilic extractant molecules, although we find this rearrangement is not strongly manifested in small-angle X-ray scattering making it challenging to probe without methods such as atomistic simulation. Using a graph theoretic approach to define hydrogen bonded water/malonamide aggregates from molecular dynamics simulations, we find evidence of a characteristic aggregate size by water number that results from geometric accommodation of the surrounding malonamide molecules. Furthermore, this implies a degree of size selectivity inherent to these water-in-oil aggregates. Conversely, we find no evidence of a characteristic size of the aggregates with respect to their malonamide number. By defining a separate graphical representation of self-association of the amphiphilic malonamides, we quantify how water affects the local and nonlocal topology of the malonamide network, providing a basis for characterization of the structure and impact of polar solutes in increasingly complex organic phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Impact of Soret effect on hydrogen and helium retention in PFC tungsten under ELM-like conditions

Abstract In our previous work, we have demonstrated using nonequilibrium molecular-dynamics simulations that the fluxes of helium and self-interstitial atoms in the presence of a thermal gradient in tungsten are directed opposite to the heat flux, indicating that species transport is governed by a Soret effect, namely, thermal-gradient-driven diffusion, characterized by a negative heat of transport that drives species transport uphill, i.e. from the cooler to the hot regions of the tungsten sample. In this work, the findings of our thermal and species transport analysis have been implemented in our cluster-dynamics code, Xolotl, which has been used to compute temperature and species profiles over spatiotemporal scales representative of plasma-facing component (PFC) tungsten under typical reactor operating conditions, including extreme heat loads at the plasma-facing surface characteristic of plasma instabilities that induce edge localized modes (ELMs). We demonstrate that the steady-state species profiles, when properly accounting for the Soret effect, vary significantly from those where temperature-gradient-driven transport is not accounted for and discuss the implications of such a Soret effect on the response to plasma exposure of plasma-facing tungsten. Although our cluster-dynamics simulations do not yet include self-clustering of helium or hydrogen blister formation, our simulation results show that the Soret effect substantially reduces helium and hydrogenic species retention inside PFC tungsten.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rotational transition, domain formation, dislocations, and defects in vortex systems with combined sixfold and twelvefold anisotropic interactions

We introduce a phenomenological model for a pairwise repulsive interaction potential of vortices in a type-II superconductor, consisting of superimposed sixfold and twelvefold anisotropies. Using numerical simulations we study how the vortex lattice configuration varies as the magnitudes of the two anisotropic interaction terms change. A triangular lattice appears for all values, and rotates through 30 ° as the ratio of the sixfold and twlevefold anisotropy amplitudes is varied, in agreement with experimental results. The transition causes the vortex lattice to split into domains that have rotated clockwise or counterclockwise, with grain boundaries that are “decorated” by dislocations consisting of fivefold and sevenfold coordinated vortices. We also find intradomain dislocations and defects, and characterize them in terms of their energy cost. We discuss how this model could be generalized to other particle-based systems with anisotropic interactions, such as colloids, and consider the limit of very large anisotropy where it is possible to create cluster crystal states.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Whole Genome Sequencing Links Mycobacterium bovis From Cattle, Cheese and Humans in Baja California, Mexico

Mycobacterium bovis causes tuberculosis (TB) in cattle, which in turn can transmit the pathogen to humans. Tuberculosis in dairy cattle is of particular concern where the consumption of raw milk and dairy products is customary. Baja California (BCA), Mexico, presents high prevalence of TB in both cattle and humans, making it important to investigate the molecular epidemiology of the disease in the region. A long-term study was undertaken to fully characterize the diversity of M. bovis genotypes circulating in dairy cattle, cheese and humans in BCA by whole-genome sequencing (WGS). During a 2-year period, 412 granulomatous tissue samples were collected from local abattoirs and 314 cheese samples were purchased from local stores and vendors in BCA and sent to the laboratory for mycobacterial culture, histology, direct PCR and WGS. For tissue samples M. bovis was recovered from 86.8%, direct PCR detected 90% and histology confirmed 85.9% as mycobacteriosis-compatible. For cheese, M. bovis was recovered from 2.5% and direct PCR detected 6% of the samples. There was good agreement between diagnostic tests. Subsequently, a total of 345 whole-genome SNP sequences were obtained. Phylogenetic analysis grouped these isolates into 10 major clades. SNP analysis revealed putative transmission clusters where the pairwise SNP distance between isolates from different dairies was ≤3 SNP. Also, human and/or cheese isolates were within 8.45 (range 0–17) and 5.8 SNP (range 0–15), respectively, from cattle isolates. Finally, a comparison between the genotypes obtained in this study and those reported previously suggests that the genetic diversity of M. bovis in BCA is well-characterized, and can be used to determine if BCA is the likely source of M. bovis in humans and cattle in routine epidemiologic investigations and future studies. In conclusion, WGS provided evidence of ongoing local transmission of M. bovis among the dairies in this high-TB burden region of BCA, as well as show close relationships between isolates recovered from humans, cheese, and cattle. This confirms the need for a coordinated One Health approach in addressing the elimination of TB in animals and humans. Overall, the study contributes to the knowledge of the molecular epidemiology of M. bovis in BCA, providing insight into the pathogen's dynamics in a high prevalence setting.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

APOGEE-2S Mg–Al anti-correlation of the metal-poor globular cluster NGC 2298

We present detailed elemental abundances and radial velocities of stars in the metal-poor globular cluster (GC) NGC 2298, based on near-infrared high-resolution ($R$ ~ 22 500) spectra of 12 members obtained during the second phase of the Apache Point Observatory Galactic Evolution Experiment (APOGEE-2) at Las Campanas Observatory as part of the seventeenth Data Release (DR 17) of the Sloan Digital Sky Survey IV (SDSS-IV). We employed the Brussels Automatic Code for Characterizing High accuracy Spectra (BACCHUS) software to investigate abundances for a variety of species including α elements (Mg, Si, and Ca), the odd-Z element Al, and iron-peak elements (Fe and Ni) located in the innermost regions of NGC 2298. We find a mean and median metallicity [Fe/H] = -1.76 and -1.75, respectively, with a star-to-star spread of 0.14 dex, which is compatible with the internal measurement errors. Thus, we find no evidence for an intrinsic [Fe/H] abundance spread in NGC 2298. The typical $α$-element enrichment in NGC 2298 is overabundant relative to the Sun, and it follows the trend of other metal-poor GCs. We confirm the existence of an Al-enhanced population in this cluster, which is clearly anti-correlated with Mg, indicating the prevalence of the multiple-population phenomenon in NGC 2298.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. Here, a comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Extracting dark-matter velocities from halo masses: A reconstruction conjecture

Increasing attention has recently focused on nontraditional dark-matter production mechanisms which result in primordial dark-matter velocity distributions with highly nonthermal shapes. Here, in this paper, we undertake an assessment of how the detailed shape of a general dark-matter velocity distribution impacts structure formation in the nonlinear regime. In particular, we investigate the impact on the halo-mass and subhalo-mass functions, as well as on astrophysical observables such as satellite and cluster-number counts. We find that many of the standard expectations no longer hold in situations in which this velocity distribution takes a highly nontrivial, even multimodal shape. For example, we find that the nominal free-streaming scale alone becomes insufficient to characterize the effect of free-streaming on structure formation. In addition, we propose a simple one-line conjecture which can be used to “reconstruct” the primordial dark-matter velocity distribution directly from the shape of the halo-mass function. Although our conjecture is completely heuristic, we show that it successfully reproduces the salient features of the underlying dark-matter velocity distribution even for nontrivial distributions which are highly nonthermal and/or multimodal, such as might occur for nonminimal dark sectors. Moreover, since our approach relies only on the halo-mass function, our conjecture provides a method of probing dark-matter properties even for scenarios in which the dark and visible sectors interact only gravitationally.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Electronic Structure of Atomically Dispersed Supported Iridium Catalyst Controls Iridium Aggregation

Supported iridium complexes, Ir(C 2 H 4 ) 2 /support, were characterized by X-ray absorption spectroscopy during a temperature ramp to 120 °C in flowing H 2 . Iridium in complexes bonded to weak and moderate electron-donor supports, SiO 2 and γ-Al 2 O 3 , underwent aggregation, forming nanoparticles and clusters, respectively. When the support was a strong electron-donor (MgO), iridium remained site-isolated. Density functional theory calculations confirm the dependence of iridium–support bond strength on the support’s electron-donor character. Coating the SiO 2 -supported complexes with 1-n-ethyl-3-methyl-imidazolium acetate enhanced electron density on the iridium, hindering its aggregation. Finally, these results demonstrate opportunities for stabilizing atomically dispersed supported noble metals under reducing conditions by choice of support/ionic liquid sheath combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Engineering Escherichia coli for Urease-Driven Synthesis of Metal Oxide Nanomaterials

The development of functional nanomaterials with controlled morphologies is essential for advancements in medicine, electronics and computing, energy, catalysis, and environmental applications. However, conventional synthesis methods often demand high energy input and pose significant environmental challenges. Urease-based biomineralization presents an efficient, eco-friendly alternative for nanomaterial production under mild conditions. In this study, we engineered Escherichia coli ( E. coli ) to express a urease gene cluster from Sporosarcina pasteurii using CRAGE-Duet technology. The engineered strain successfully synthesized calcium carbonate and calcium phosphate crystals. Expanding the approach, we synthesized metal oxide nanoparticles, including hematite (Fe 2 O 3 ), and nanocrystalline anatase titanium dioxide (TiO 2 ). These nanomaterials were characterized by electron microscopy, demonstrating the potential of E. coli as a sustainable and versatile platform for green nanomaterial synthesis.

bacteria↗
DOE OSTI2023

Cofactorless oxygenases guide anthraquinone-fused enediyne biosynthesis

The anthraquinone-fused enediynes (AFEs) combine an anthraquinone moiety and a ten-membered enediyne core capable of generating a cytotoxic diradical species. AFE cyclization is triggered by opening the F-ring epoxide, which is also the site of the most structural diversity. Previous studies of tiancimycin A, a heavily modified AFE, have revealed a cryptic aldehyde blocking installation of the epoxide, and no unassigned oxidases could be predicted within the tnm biosynthetic gene cluster. Here, in this study, we identify two consecutively acting cofactorless oxygenases derived from methyltransferase and α/β-hydrolase protein folds, TnmJ and TnmK2, respectively, that are responsible for F-ring tailoring in tiancimycin biosynthesis by comparative genomics. Further biochemical and structural characterizations reveal that the electron-rich AFE anthraquinone moiety assists in catalyzing deformylation, epoxidation and oxidative ring cleavage without exogenous cofactors. These enzymes therefore fill important knowledge gaps for the biosynthesis of this class of molecules and the underappreciated family of cofactorless oxygenases. Cofactorless oxygenases are rare in nature and natural product biosynthesis. Here the authors describe the biochemical and structural characterization of two such oxygenases catalyzing deformylation, ring cleavage and epoxidation in the biosynthesis of the enediyne natural product tiancimycin A.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

60 APPLIED LIFE SCIENCES↗