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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Discovery of a new extragalactic circular radio source with ASKAP: ORC J0102–2450

ABSTRACT We present the discovery of another odd radio circle (ORC) with the Australian Square Kilometre Array Pathfinder (ASKAP) at 944 MHz. The observed radio ring, ORC J0102–2450, has a diameter of ∼70 arcsec or 300 kpc, if associated with the central elliptical galaxy DES J010224.33–245039.5 (z ∼ 0.27). Considering the overall radio morphology (circular ring and core) and lack of ring emission at non-radio wavelengths, we investigate if ORC J0102–2450 could be the relic lobe of a giant radio galaxy seen end on or the result of a giant blast wave. We also explore possible interaction scenarios, for example, with the companion galaxy, DES J010226.15–245104.9, located in or projected on to the south-eastern part of the ring. We encourage the search for further ORCs in radio surveys to study their properties and origin.

Koribalski, Bärbel S. (ORCID:000000034351993X)↗
DOE OSTI2022

Discovery of a quasar with double-peaked broad balmer emission lines

ABSTRACT Most massive galaxies contain a supermassive black hole (SMBH) at their centre. When galaxies merge, their SMBHs sink to the centre of the new galaxy, where they are thought to eventually merge. During this process, an SMBH binary is formed. The presence of two sets of broad emission lines in the optical spectrum of an active galactic nucleus (AGN) has been interpreted as evidence for two broad-line regions (BLRs), one surrounding each SMBH in a binary. We modelled the broad Balmer emission lines in the SDSS spectra of 373 extreme variability AGNs using one broad and several narrow Gaussian components. We report on the discovery of SDSS J021647.53 − 011341.5 (hereafter J0216) as a double-peaked broad emission line source. Among the 373 AGNs, there were five sources that are known as double-peaked emission line sources. Three of these have been reported as candidate SMBH binaries in previous studies. We present all six objects and their double-peaked broad Balmer emission lines, and discuss the implications for a tidal disruption event (TDE) interpretation of the extreme variability, assuming the double-peaked sources are SMBH binaries.

Terwel, Jacco H. (ORCID:0000000198343439)↗
DOE OSTI2022

LOFAR discovery of rare large FR I jets in the low-luminosity radio galaxy NGC 5322

ABSTRACT The discovery of faint Fanaroff–Riley type I (FR I) radio jets in the elliptical galaxy NGC 5322 is reported here using the 144-MHz image from Data Release 2 of the Low-Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS). The jets have an angular extent of ∼40 arcmin or a projected physical extent of ∼360 kpc. The faint jets remain well collimated and disappear in the intergalactic medium, without any detected hotspots or radio lobes. The relatively brighter jets, previously detected only up to the ∼21 kpc extent at higher frequencies, are within the optical extent of the galaxy. The jets become faint abruptly outside, where detection is made only in the LOFAR image. The total radio luminosity of the galaxy at 144 MHz is estimated to be (3.7 ± 0.4) × 1022 W Hz−1. The 144-MHz radio luminosity of the faint jets outside the optical extent is estimated to be (7.1 ± 2.0) × 1021 W Hz−1. The size of the jets is exceptionally large for the galaxy’s low radio luminosity, when compared with other radio galaxies. It makes NGC 5322 a rare radio galaxy, previously not detected in other radio surveys. A combined effect of stellar core depletion and low-density environment around the jets, which results in a weak entrainment of the material surrounding the jets, could be responsible for its large size despite a low radio luminosity.

Astronomy & Astrophysics↗
DOE OSTI2024

Final results of the search for new Milky Way satellites in the Hyper Suprime-Cam Subaru Strategic Program survey: Discovery of two more candidates

Abstract We present the final results of our search for new Milky Way (MW) satellites using the data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) survey over ∼1140 deg2. In addition to three candidates that we have already reported, we have identified two new MW satellite candidates in the constellations of Sextans, at a heliocentric distance of D⊙ ≃ 126 kpc, and Virgo, at D⊙ ≃ 151 kpc, named Sextans II and Virgo III, respectively. Their luminosities (Sext II: MV ≃ −3.9 mag; Vir III: MV ≃ −2.7 mag) and half-light radii (Sext II: rh ≃ 154 pc; Vir III: rh ≃ 44 pc) place them in the region of size–luminosity space of ultra-faint dwarf galaxies (UFDs). Including four previously known satellites, there are a total of nine satellites in the HSC-SSP footprint. This discovery rate of UFDs is much higher than that predicted from the recent models for the expected population of MW satellites in the framework of cold dark matter models, thereby suggesting that we encounter a too many satellites problem. Possible solutions to settle this tension are also discussed.

Homma, Daisuke↗
DOE OSTI2021

Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Discovery of 98 Sn Produced by the Projectile Fragmentation of a 345-MeV/Nucleon 124 Xe Beam

We present the discovery of a new isotope of $^{98}$Sn beyond the double-magic N = Z = 50 nucleus $^{100}$Sn. $^{98}$Sn was identified among the projectile-fragmentation products of a $^{124}$Xe beam at 345 MeV/nucleon at the RI Beam Factory, RIKEN, Japan. Additionally, we have confirmed the production of $^{96}$In and $^{94}$Cd, previously reported as new isotopes at RIKEN. These highly proton-rich nuclei were separated and identified using the large-acceptance two-stage separator BigRIPS. Furthermore, we have determined the cross sections of $^{98}$Sn and its neighboring nuclei. These experimental values were compared to the semi-empirical cross-section formula EPAX3.1a, resulting in a reduction of the predicted cross sections by roughly a factor of 5.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

A Viable New Strategy for the Discovery of Peptide Proteolytic Cleavage Products in Plant-Microbe Interactions

Small peptides that are proteolytic cleavage products (PCPs) of less than 100 amino acids are emerging as key signaling molecules that mediate cell-to-cell communication and biological processes that occur between and within plants, fungi, and bacteria. Yet, the discovery and characterization of these molecules is largely overlooked. Today, selective enrichment and subsequent characterization by mass spectrometry–based sequencing offers the greatest potential for their comprehensive characterization, however qualitative and quantitative performance metrics are rarely captured. Herein, we addressed this need by benchmarking the performance of an enrichment strategy, optimized specifically for small PCPs, using state-of-the-art de novo–assisted peptide sequencing. As a case study, we implemented this approach to identify PCPs from different root and foliar tissues of the hybrid poplar Populus × canescens 717-1B4 in interaction with the ectomycorrhizal basidiomycete Laccaria bicolor . In total, we identified 1,660 and 2,870 Populus and L. bicolor unique PCPs, respectively. Qualitative results supported the identification of well-known PCPs, like the mature form of the photosystem II complex 5-kDa protein (approximately 3 kDa). A total of 157 PCPs were determined to be significantly more abundant in root tips with established ectomycorrhiza when compared with root tips without established ectomycorrhiza and extramatrical mycelium of L. bicolor . These PCPs mapped to 64 Populus proteins and 69 L. bicolor proteins in our database, with several of them previously implicated in biologically relevant associations between plant and fungus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Discovery of new isotopes in the fragmentation of 82 Se and insights into their production

The production cross sections of neutron-rich nuclei for elements just above 60 Ca were measured at the Facility for Rare Isotope Beams (FRIB). Four previously unobserved isotopes ( 63 Sc, 65,66 Ti, and 68 V) were produced, separated, and identified for the first time using the Advanced Rare Isotope Separator (ARIS). One event was found to be consistent with 61 Ca . The new isotopes were created through the interaction of an 82 Se beam with a carbon target at an energy of 228 MeV/𝑢 and a primary beam current of 1.07 p⁢µ⁢A. The event-by-event particle identification of the mass number (𝐴), atomic number (𝑍), and ionic charge state (𝑞) for the reaction products was achieved by combining measurements of energy loss, time of flight, magnetic rigidity, and total kinetic energy. This successful search for new isotopes, conducted at the beginning of FRIB's third year of operation, highlights the facility's discovery potential, which will continue to grow as the beam current increases. The production cross sections were analyzed in the context of the newly developed Δ⁢𝐵⁢𝐸 systematics that provides trends in neutron-rich regions and increased sensitivity to nuclear binding, an important nuclear structure observable. Furthermore, the new systematics addresses gaps in 𝑄 𝑔 -based methods commonly used to interpret the production cross sections of light neutron-rich elements, and provides an efficient empirical approach to describe cross-section trends across isotopic chains. Good agreement with the measured data was obtained when the Δ⁢𝐵⁢𝐸 systematics and abrasion-ablation calculations were performed using the Hartree-Fock-Bogoliubov (HFB-22) mass table.

59 ≤ A ≤ 89↗
DOE OSTI2023

Machine learning guided discovery of superconducting calcium borocarbides

Pursuit of superconductivity in light-element systems at ambient pressure is of great experimental and theoretical interest. In this work, we combine a machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary Ca-B-C compounds. Three layered borocarbides (stable CaBC 5 and metastable Ca 2 BC 11 and CaB 3 C 3 ) are predicted to be phonon-mediated superconductors at ambient pressure. The stable CaBC 5 and the low-energy metastable Ca 2 BC 11 (with formation energy only 9.5 meV/atom above the convex hull) have a superconducting T c of 5.2 and 8.9 K, respectively. While the hexagonal CaB 3 C 3 possesses a T c of 26.1 K, it is metastable with formation energy of 153 meV/atom above the convex hull. The ML-guided approach opens up a way for greatly accelerating the discovery of new high-T c superconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electron g − 2 foreshadowing discoveries at FCC-ee

A future e + e − circular collider (FCC-ee) may provide a unique probe of the electron Yukawa coupling through Higgs boson production on resonance. Motivated by this exciting possibility, we examine a simple model which can result in O ( 10 ) modifications of the Higgs coupling to electrons. The model can also lead to deviations in the electron anomalous magnetic moment, g e − 2 , which at present shows a + 2.2 σ or − 3.7 σ deviation, implied by differing precision determinations of the electromagnetic fine structure constant. The electron g e − 2 can be a forerunner for FCC-ee discoveries which, as we elucidate, may not be accessible to the high-luminosity LHC measurements. A simple extension of our model can also account for the current deviation in the muon g μ − 2 . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

ADMX Axion Dark Matter Bounds around 3.3 μ eV with Dine-Fischler-Srednicki-Zhitnitsky Discovery Ability

We report the results of a QCD axion dark matter search with discovery ability for Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) axions using an axion haloscope. Sub-Kelvin noise temperatures are reached with an ultralow noise Josephson parametric amplifier cooled by a dilution refrigerator. This work excludes (with a 90% confidence level) DFSZ axions with masses between 3.27 to 3.34 μ eV , assuming a standard halo model with a local energy density of 0.45 G e V / cm 3 made up 100% of axions. Published by the American Physical Society 2025

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine learning-accelerated discovery of iron cobalt phosphides as rare-earth-free magnets

Here, the discovery of rare-earth-free permanent magnets has been a goal of scientists for decades. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (K 1 ). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (J s > 1 T). The structures with desirable magnetic properties center on (Fe,Co) 2⁢ P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe 2 ⁢P and Co 2 ⁢P. Our work provides guidance for synthesis. We find Fe 7 ⁢CoP 4 shows the most promise (J s = 1.03T and K 1 = 0.83MJ/m 3 ).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Clustering-Based Predictive Analytics to Improve Scientific Data Discovery

Given the sheer volume of scientific data archived within the data-intensive projects at the US Department of Energy's Oak Ridge National Laboratory, finding precisely what data we are looking for may not be a trivial task; conversely, we may also miss a more prominent data product. To address such issues, we propose improving the data discovery system and using data analytics methods to comprehend what specific users might be interested in based on their physiological state, search patterns, and past data usage history. This work's primary goal is to prune the complexity, increase the visibility of popular data products, and direct users toward the data that best meet their needs. The proposed algorithm constructs a user profile based on the user's explicit or implicit interactions with the system, such as items they are currently looking at on-site and the key metadata mappings related to the data set. The pattern is then used to build a training data set, which will help find relevant data to recommend to the user.

Devarakonda, Ranjeet↗
DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗
DOE OSTI2024

AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities]

The design and discovery of materials with desired functional properties is pivotal to the scientific mission of the United States Department of Energy (US-DOE) [1], which includes within its portfolio several important applications for the national economy and security. Importantly, these applications range from: renewable energy (e.g., solar cells, organic photovoltaics, and organic light-emitting diodes), energy storage (e.g., batteries and supercapacitors), and carbon capture and sequestration, to synthesis of manufacturing of new materials (e.g., drugs, or materials with desired conductivity, thermal stability, and catalytic activity), and nuclear energy (e.g., highly performant nuclear fuels and materials with improved nuclear shielding properties).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient Quantum Gate Discovery with Optimal Control

Optimal control theory provides a framework for numerical discovery of device controls that implement quantum logic gates, but common objective functions used for optimization often assign arbitrarily high costs to otherwise useful controls. We propose a framework for designing objective functions that permit novel gate designs such as echo pulses or locally-equivalent gates. We use numerical simulations to demonstrate the efficacy of the new objective functions by designing microwave-only pulses that act as entangling gates for superconducting transmon architectures. We observe that the proposed objective functions lead to higher fidelity controls in fewer optimization iterations than obtainable by traditional objective functions.

Kairys, Paul↗
DOE OSTI2023

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties

Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties.

Biswas, Arpan↗
DOE OSTI2022

Machine learning enables interpretable discovery of innovative polymers for gas separation membranes

Polymer membranes perform innumerable separations with far-reaching environmental implications. Despite decades of research, design of new membrane materials remains a largely Edisonian process. To address this shortcoming, we demonstrate a generalizable, accurate machine learning (ML) implementation for the discovery of innovative polymers with ideal performance. Specifically, multitask ML models are trained on experimental data to link polymer chemistry to gas permeabilities of He, H 2 , O 2 , N 2 , CO 2 , and CH 4 . We interpret the ML models and extract valuable insights into the contributions of different chemical moieties to permeability and selectivity. We then screen over 9 million hypothetical polymers and identify thousands that lie well above current performance upper bounds, including hundreds of never-before-seen ultrapermeable polymer membranes with O 2 and CO 2 permeability greater than 10 4 and 10 5 Barrers, respectively. High-fidelity molecular dynamics simulations confirm the ML-predicted gas permeabilities of the promising candidates, which suggests that many can be translated to reality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗