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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
DOE OSTI2021

Millicharged particles from the heavens: single- and multiple-scattering signatures

For nearly a century, studying cosmic-ray air showers has driven progress in our understanding of elementary particle physics. In this work, we revisit the production of millicharged particles in these atmospheric showers and provide new constraints for XENON1T and Super-Kamiokande and new sensitivity estimates of current and future detectors, such as JUNO. We discuss distinct search strategies, specifically studies of single-energy-deposition events, where one electron in the detector receives a relatively large energy transfer, as well as multiple-scattering events consisting of (at least) two relatively small energy depositions. We demonstrate that these atmospheric search strategies — especially the multiple-scattering signature — provide significant room for improvement beyond existing searches, in a way that is complementary to anthropogenic, beam-based searches for MeV-GeV millicharged particles. Finally, we also discuss the implementation of a Monte Carlo simulation for millicharged particle detection in large-volume neutrino detectors, such as IceCube.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

42 ENGINEERING↗
DOE OSTI2022

Radioxenon signatures of molten salt reactors

Developments of molten salt reactor (MSR) technologies are making rapid progress across the globe. These reactor designs involve properly controlling radionuclides through off-gas systems. This work examines radioxenon emissions and assesses treaty detection technology to be used for monitoring. This work also includes a sensitivity study with ORIGEN on the nuclear forensic signals possible from a MSR. Multiple Isotope Ratio Comparison (MIRC) plots are used to compare the ratios of radioxenon isotopes in an MSR to a highly enriched uranium (HEU) reactor. Finally, the results show that MSR emissions may significantly overlap with signals produced in a HEU reactor and that changing reactor operations or retaining emissions in an off-gas system can significantly shift the radioxenon signature away from the HEU pulse.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Neutron capture signatures determined from gadolinium isotope compositions of uranium ore concentrates using MC-ICPMS

We present Gd isotope compositions on 25 well-characterized uranium ore concentrates (UOCs). About half the UOCs have isotope depletions in 157 Gd coupled with anticorrelated excesses in 158 Gd due to neutron capture effects. UOCs from older ore bodies and with higher U contents show larger neutron capture effects than UOCs from younger ore bodies with lower U contents. These Gd data are correlated with their previously measured Sm isotope compositions, and we use the Sm data to estimate the neutron fluence of these samples. In conclusion, this work demonstrates how Gd isotope signatures can be employed as a new tool for nuclear forensics.

Earth Sciences↗
DOE OSTI2020

Detecting keyhole porosity and monitoring process signatures in additive manufacturing: an in situ pyrometry and ex situ X-ray radiography correlation

Creation of pores and defects during laser powder bed fusion (LPBF) can lead to poor mechanical properties and thus must be minimized. Post-build inspection is required to ensure the printed parts contain acceptably low defect concentrations. These inspections are time consuming and costly, especially for large or complex parts. As a potential solution, in situ process monitoring can be used to detect the creation of defects, characterize local material behavior and predict expected component properties. However, the precise relationship between pore creation and in situ process monitoring still needs to be understood. In this work, high-speed infrared diode-based pyrometry and high-speed optical imaging signals were used to monitor LPBF printing of 446 stainless steel 316 L single tracks with varying laser power and velocity. Results indicate an increase in pyrometer signal and melt pool dimensions with increasing laser power and decreasing velocity in agreement with previous work. In addition, careful analysis of pyrometer signal reveals a distinct signature of the conduction-to-keyhole mode transition which was confirmed by metallography. Critically, pore defect initiation as characterized by ex situ X-ray radiography was correlated with in situ thermal monitoring signals to derive the probability of defect creation. Our results show that, in principle, a probabilistic prediction of pore formation can be achieved based on in situ high-speed pyrometry monitoring of the LPBF melt pool.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Sensitivity of the Cherenkov Telescope Array to spectral signatures of hadronic PeVatrons with application to Galactic Supernova Remnants

The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3 PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy γ-ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte-Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a γ-ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 h of exposure. Finally, CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with $\mathscr{O}$ (100) hours of exposure per source.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A susceptibility gene signature for ERBB2-driven mammary tumour development and metastasis in collaborative cross mice

Background: Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. Methods: 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated in vivo using genetically diverse MMTV-Erbb2 mice. Findings: Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an in vivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. Interpretation: Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

HIMU geochemical signature originating from the transition zone

Plume volcanism may sample mantle sources deeper than mid-ocean ridge and arc volcanism. Ocean island basalts (OIBs) are commonly related to plume volcanism, and their diverse isotopic and elemental compositions can be described using a limited number of mantle endmembers. However, the origins and depths of these mantle endmembers are highly debated. Here we show that the HIMU (high μ, μ = 238 U/ 204 Pb) endmember may reside in the transition zone. Specifically, we report the geochemical signature of a high-pressure multiphase diamond inclusion, entrapped at 420–440 km depth and 1450 ±50 K, which matches exactly the geochemical patterns of the HIMU-rich OIBs. Since the HIMU component is variably sampled by almost all OIBs, our finding implies that the transition zone causes a major overprint of the geochemical features of mantle plumes. Furthermore, some mantle plumes, like those feeding Bermuda, St Helena, Tubuai and Mangaia, appear to be dominated by this source. Furthermore, our finding highlights the importance of the transition zone in highly incompatible element budget of the mantle.

58 GEOSCIENCES↗
DOE OSTI2022

Sound velocity and compressibility of melts along the hedenbergite (CaFeSi2O6)-diopside (CaMgSi2O6) join at high pressure : Implications for stability and seismic signature of Fe-rich melts in the mantle

Iron-rich silicate melts play an important role in the magmatic history of the Earth and the 16 Moon. However, their elastic properties at high pressures, especially the sound velocity, are poorly 17 understood. Here we determined the ultrasonic sound velocity for the first time of a hedenbergite 18 (Hd, CaFeSi2O6) melt and a melt mixture of 50 mol% hedenbergite + 50 mol% diopside (Hd50Di50) 19 at high pressure and temperature conditions up to 6 GPa and 2329 K, using high-pressure ultrasonic 20 technique combined with synchrotron radiation in a multi-anvil apparatus. Our results show that 21 Fe can significantly reduce the sound velocity while increase the density of silicate melts. 22 Comparing the Di, Hd, and Hd50Di50 melts, we find that the sound velocity does not mix linearly 23 2 for melts in the Hd-Di join, whereas the density for Hd-Di melts at high pressures can be well-24 described by linear mixing. Combined with melt geometry and melt compaction models, we 25 applied our results to study the stability and seismic signature of Fe-rich silicate melts in the Earth’s 26 upper mantle. For the low-velocity zone (LVZ) in mantle asthenosphere, although the degree of 27 seismic velocity reduction can be explained by the presence of a small amount of partial melt 28 distributed in film/band geometry along grain boundaries, Fe-rich melts formed at this depth are 29 unlikely to be gravitationally stable, but may be dynamically unextractable if the melt supply is 30 continuous. For the low-velocity layer (LVL) above the mantle transition zone, the presence of Fe-31 rich melts (with FeO>~10 wt%) distributed in textural equilibrium with the ambient mantle is a 32 plausible explanation.

diopside↗
DOE OSTI2022

Pushing the limits: Resolving paleoseawater signatures in nanoscale fluid inclusions by atom probe tomography

New insight into the geochemistry of ancient environments can be gained through structural and chemical analyses of nanometer-scale features within minerals. Here, we present recent developments using atom probe tomography (APT) enabling direct visualization of nanoscale fluid inclusions trapped within pyrite (FeS 2 ) and thereby chemical characterization of remnant seawater. Pyrite framboids (spherical clusters of nanocrystals) were sampled from the Middle Devonian Leicester Pyrite Member (New York). Scanning transmission electron microscopy shows low density regions distributed within the pyrite consistent with nanoscale pores (<4 nm in size). APT 3D visualization and compositional mapping reveals that the nanopores are filled with water. The inclusions appear to preserve the elemental signature of the water column in which the framboids formed, specifically seawater components including Na, K, Mg, and Ca. Mg/Ca ratios within the pyrite were generally measured to be within 0.6±0.2 – consistent with calcite-dominated seawater conditions existing in the Middle Devonian. Furthermore, this study demonstrates the potential for a novel approach to reconstruct paleoenvironmental conditions from coupled elemental and structural analyses of nanoscale fluid inclusions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Signatures of rare earth element distributions in fly ash derived from the combustion of Central Appalachian, Illinois, and Powder River basin coals

We report the distribution of Rare Earth elements (REE) in coal-derived fly ashes can have distinctive patterns when fly ashes are produced from different coals within or between basins, such as the Pennsylvanian Class F fly ashes from the Illinois and Central Appalachian basins. Both the Fire Clay coal and a blend of a number of eastern Kentucky coals show strong Gd peaks and an H-type distribution in the Upper Continental Crust-corrected plots. The Fire Clay coal-derived ash has a higher heavy REE concentration than the blended coal-derived ash. The Illinois Basin-derived fly as has an overall lower REE concentration than the latter ashes. Class C fly ash derived from Powder River Basin coals has, with the exception of an Eu peak, a flatter distribution of REE and an overall L-type or indistinct H- versus L-type distribution. The signatures of the REE in fly ashes may be useful in predicting their behavior in the extraction of the REE; simple extrapolations from the basic concentrations and the predicted extraction percentages for ashes from different basins are not necessarily indicative of the actual distribution of the extracted REE.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Multi-arrival infrasound from meteoroids: Fragmentation signatures versus propagation effects in a fine-scale layered atmosphere

Infrasonic signatures of meteoroid fragmentation are frequently ambiguous: do multiple arrivals signify a complex breakup or merely the distorting effects of a layered atmosphere? Resolving this ambiguity is critical for accurate energy estimates and source reconstruction. In this study, we address this challenge by analyzing a unique regional dataset of well-constrained meteoroid events observed by the Southern Ontario Meteor Network and the co-located Elginfield Infrasound Array. We employ pseudo-differential parabolic equation (PPE) simulations to quantify how fine-scale gravity-wave structures in the stratosphere and lower thermosphere modify acoustic waveforms at ranges <300 km. Our modeling reveals that while fine-scale layering can stretch signals and generate diffuse oscillatory tails, it does not produce discrete, high-amplitude pulse splitting at ranges below ∼140 km. By applying these results to the rare multi-arrival event 20060305, we demonstrate that its distinct double arrival at 100 km range is inconsistent with atmospheric multipathing and provides definitive evidence of separate fragmentation episodes. These findings establish new diagnostic criteria for separating source physics from propagation artifacts, improving the reliability of infrasound as a monitoring tool for natural bolides, space debris re-entries, and catastrophic launch failures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Proteomic Profiling of Intra-Islet Features Reveals Substructure-Specific Protein Signatures

Despite their diminutive size, islets of Langerhans play a large role in maintaining systemic energy balance in the body. New technologies have enabled us to go from studying the whole pancreas, to isolated whole islets, to partial islet sections, and now to islet substructures isolated from within the islet. Using a microfluidic nanodroplet-based proteomics platform coupled with laser capture microdissection (LCM), we present an in-depth investigation of protein profiles specific to regions within the islet. These regions studied include the vascular tissue containing interface boundary, micro-vascular tissue internal to the islet, isolated endocrine cells, islet sections with vasculature intact, and finally acinar tissue from around the islet. Unique protein signatures observed in the inner vasculature potentially indicate increased innervation and intra-islet neuron-like crosstalk compared to external vasculature. We also demonstrate the utility of these data for isolating localized structure-specific drug-target interactions using existing protein/drug binding databases.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Persistent urinary metabolic signatures in children with type 1 diabetes

There are an estimated 3.7 million people with undiagnosed type 1 diabetes (T1D), living primarily in poor areas of the globe. Therefore, there is a need for non-invasive, affordable tests to provide accurate diagnosis despite the time post-disease onset and fasting state. Here, we studied persistent urinary T1D biomarkers that can be used to develop such tests. Here, we analyzed the urine metabolomes of three independent cohorts of samples collected within 48 h (from Indiana University), and 1 year (from University of Colorado) and 1–10 years (6 years in average) (from Children’s National Medical Center) post-diagnosis. Samples were submitted to gas chromatography-mass spectrometry and machine learning an0alyses to determine diagnostic metabolite panels. The data were also mapped into a metabolic pathway to understand persistently regulated processes in T1D. Seven metabolites showed consistent increases in all three cohorts: d-glucose, d-mannose, myo-inositol, 3-hydroxyisobutyric acid, gluconolactone, d-gluconic acid, and d-glucuronic acid. A combination of machine learning analysis and metabolite ratios as biomarker candidates diagnosed T1D with high sensitivity and specificity across different cohorts and times. Mapping the regulated metabolites into a pathway showed impairment in glycolysis and overflow of glucose towards other pathways in subjects with T1D that was persistent over time. We identified and cross-validated highly specific and sensitive urinary biomarkers. This opens opportunities to develop affordable, robust, and non-invasive tests. The results also show that most of the biomarkers were signatures of dysregulated glucose metabolism.

Type 1 diabetes↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗