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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Metabolite release by nitrifiers facilitates metabolic interactions in the ocean

Microbial chemoautotroph-heterotroph interactions may play a pivotal role in the cycling of carbon in the deep ocean, reminiscent of phytoplankton-heterotroph associations in surface waters. Nitrifiers are the most abundant chemoautotrophs in the global ocean, yet very little is known about nitrifier metabolite production, release, and transfer to heterotrophic microbial communities. To elucidate which organic compounds are released by nitrifiers and potentially available to heterotrophs, we characterized the exo- and endometabolomes of the ammonia-oxidizing archaeon Nitrosopumilus adriaticus CCS1 and the nitrite-oxidizing bacterium Nitrospina gracilis Nb-211. Nitrifier endometabolome composition was not a good predictor of exometabolite availability, indicating that metabolites were predominately released by mechanisms other than cell death/lysis. Although both nitrifiers released labile organic compounds, N. adriaticus preferentially released amino acids, particularly glycine, suggesting that its cell membranes might be more permeable to small, hydrophobic amino acids. We further initiated co-culture systems between each nitrifier and a heterotrophic alphaproteobacterium, and compared exometabolite and transcript patterns of nitrifiers grown axenically to those in co-culture. In particular, B vitamins exhibited dynamic production and consumption patterns in nitrifier-heterotroph co-cultures. We observed an increased production of vitamin B 2 and the vitamin B 12 lower ligand dimethylbenzimidazole by N. adriaticus and N. gracilis, respectively. In contrast, the heterotroph likely produced vitamin B 5 in co-culture with both nitrifiers and consumed the vitamin B 7 precursor dethiobiotin when grown with N. gracilis. Our results indicate that B vitamins and their precursors could play a particularly important role in governing specific metabolic interactions between nitrifiers and heterotrophic microbes in the ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Magnetic interactions and excitations in SrMnSb 2

The magnetic interactions in the antiferromagnetic (AFM) Dirac semimetal candidate SrMnSb 2 are investigated using ab initio linear response theory and inelastic neutron scattering (INS). Our calculations reveal that the first two nearest in-plane couplings (J 1 and J 2 ) are both AFM in nature, indicating a significant degree of spin frustration, which aligns with experimental observations. The orbital resolution of exchange interactions shows that J 1 and J 2 are dominated by direct and superexchange, respectively. In a broader context, a rigid-band model suggests that electron doping fills the minority spin channel and results in a decrease in the AFM coupling strength for both J 1 and J 2 . To better compare with INS measurements, we calculate the spin-wave spectra within a linear spin-wave theory, utilizing the computed exchange parameters. Although the calculated spin-wave spectra somewhat overestimate the magnon bandwidth, they exhibit overall good agreement with measurements from INS experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Floquet-driven indirect exchange interaction mediated by topological insulator surface states

Light drives offer a potential tool for the dynamical control of magnetic interactions in matter. We theoretically investigate the indirect exchange coupling between two parallel chains of magnetic impurities on the surface of a topological insulator, driven by a time-periodic circularly polarized light field in the high-frequency, off-resonant regime. We derive a closed-form analytic expression for the spin susceptibility of the photon-dressed topological insulator surface states and obtain the irradiation dependence of the Ising, Heisenberg, and Dzyaloshinskii-Moriya exchange couplings between the impurity chains. Our results show a two-pronged modification of these exchange couplings by periodic drives. First, the Ruderman-Kittel-Kasuya-Yosida (RKKY) oscillation period of the exchange couplings can be extended by enhancing the driving strength. Second, increasing driving strength enhances the envelope of RKKY oscillations of the Ising type while suppressing those of the Heisenberg type and Dzyaloshinskii-Moriya type. Furthermore, our work provides useful insights for realizing Floquet engineering of collinear and noncollinear indirect exchange interactions in topological insulating systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Role of nonmagnetic spacers in the magnetic interactions of antiferromagnetic topological insulators MnBi 4 ⁢Te 7 and MnBi 2 ⁢Te 4

MnBi 4 ⁢Te 7 belongs to a family of antiferromagnetic topological insulators. It forms a natural heterostructure of magnetic (septuple) and nonmagnetic (quintuple) topological blocks. Here, we explore the magnetism and magnetic interactions in this compound using inelastic neutron scattering. We find that the interlayer magnetic coupling is much weaker in MnBi 4 ⁢Te 7 as compared to MnBi 2⁢ Te 4 due to the insertion of nonmagnetic quintuple layers in the former. However, other key magnetic energy scales residing within a single septuple block, the single-ion anisotropy and long-range intralayer exchanges, are essentially the same. In conclusion, this identifies a transferable set of magnetic interactions applicable to the extended family of magnetic topological insulators based on MnBi 2 ⁢Te 4 –Bi 2 ⁢Te 3 heterostructures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

First measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasielasticlike interactions using an accelerator neutrino beam

We report the first measurement of neutron capture multiplicity in neutrino-oxygen neutral-current quasielasticlike interactions at the gadolinium-loaded Super-Kamiokande detector using the T2K neutrino beam, which has a peak energy of about 0.6 GeV. A total of 30 neutral-current quasielasticlike event candidates were selected from T2K data corresponding to an exposure of 1.76 × 10 20 protons on target. The 𝛾 ray signals resulting from neutron captures were identified using a neural network. The flux-averaged mean neutron capture multiplicity was measured to be 1.37 ± 0.33 (stat.)$^{+0.17}_{−0.27}$ (syst.), which is compatible within 2.3 sigma than predictions obtained using our nominal simulation. We discuss potential sources of systematic uncertainty in the prediction and demonstrate that a significant portion of this discrepancy arises from the modeling of hadron-nucleus interactions in the detector medium.

particle interactions↗
DOE OSTI2024

Characterizing Interaction Uncertainty in Human-Machine Teams

With the increasing use and adoption of artificial intelligence (AI), the reliability of modern data systems will be driven by a tighter teaming between human experts and intelligent machine teammates. As in the case of human-human teams, the success of human-machine teams will also rely on clear communication about mutual goals and actions. In this paper, we combine related literature from cognitive psychology, human-machine teaming, uncertainty in data analysis, and multi-agent systems to propose a new form of uncertainty: interaction uncertainty for characterizing bidirectional communication in human-machine teams. We map the causes and effects of interaction uncertainty and outline potential ways to mitigate uncertainty for mutual trust in a high-consequence real-world scenario.

uncertainty, data analytics, interaction, trust, h↗
DOE OSTI2024

Altering plant carbon allocation to stems has distinct effects on rhizosphere soil microbiome assembly, interactions, and potential functions in sorghum

Abstract Altering plant carbon allocation from leaves to stems is key to improve biomass for forage, fuel, and renewable chemicals. The sorghum dry stalk ( D ) locus controls a quantitative trait for sugar accumulation, with enhanced carbon allocation in the stems of juicy green ( dd ) sorghum but reduced carbon allocation in that of dry white ( DD ) sorghum. However, it remains unclear whether altering sorghum sugar accumulation in stem affects below‐ground microbiome. Here we investigated sorghum rhizosphere soil microbiome in near isogenic lines with different magnitude of carbon allocations and accumulation in the stems. Results showed that enhanced carbon accumulation in stems of juicy green sorghum results in stronger selection in rhizosphere microbiome assembly. The rhizosphere soil microbial communities selected in juicy green sorghum tended to be fast‐growing microbial taxa which possessed potential functions that would promote higher potential capacity to use chemically labile carbon sources and potentially result in higher potential decomposition rates. We found the rhizosphere microbes selected by juicy green sorghum form weaker interactions than dry white sorghum. This is the first comprehensive study revealing how the different magnitude of carbon allocations to stems regulates microbial community assembly, microbial interaction, and microbial functions. This study indicates that future plant modification for bioenergy crops should also consider the impacts on belowground microbial community without compromising the sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Deep potential molecular dynamics simulations of low-temperature plasma-surface interactions

Machine learning approaches to potential generation for molecular dynamics (MD) simulations of low-temperature plasma-surface interactions could greatly extend the range of chemical systems that can be modeled. Empirical potentials are difficult to generalize to complex combinations of multiple elements with interactions that might include covalent, ionic, and metallic bonds. This work demonstrates that a specific machine learning approach, Deep Potential Molecular Dynamics (DeepMD), can generate potentials that provide a good model of plasma etching in the Si-Cl-Ar system. Comparisons are made between MD results using DeepMD models and empirical potentials, as well as experimental measurements. Pure Si properties predicted by the DeepMD model are in reasonable agreement with experimental results. Simulations of Si bombardment by Ar + ions demonstrate the ability of the DeepMD method to predict sputtering yields as well as the depth of the amorphous-crystalline interface. Etch yields as a function of flux ratio and ion energy for simultaneous Cl 2 and Ar + impacts are in good agreement with previous simulation results and experiment. Predictions of etch yields and etch products during plasma-assisted atomic layer etching of Si-Cl 2 -Ar are shown to be in good agreement with MD predictions using empirical potentials and with experiment. Finally, good agreement was also seen with measurements for the spontaneous etching of Si by Cl atoms at 300 K. Further, the demonstration that DeepMD can reproduce results from MD simulations using empirical potentials is a necessary condition to future efforts to extend the method to a much wider range of systems for which empirical potentials may be difficult or impossible to obtain.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Dual transposon sequencing profiles the genetic interaction landscape in bacteria

Gene redundancy complicates systematic characterization of gene function as single-gene deletions may not produce discernible phenotypes. We report dual transposon sequencing (dual Tn-seq), a platform for assaying the fitness of a comprehensive double mutant pool in parallel. Dual Tn-seq couples random barcode transposon site sequencing with the Cre-lox system, enabling deep sampling of 73% of the 1.3 million possible double gene deletions in Streptococcus pneumoniae. The genetic interactions identified span a wide range of biochemical processes, revealing new factors in presumably well-studied pathways, exemplified by a cytidine triphosphate synthase PyrJ. Moreover, this approach should permit further investigation of growth condition–specific genetic interactions. Because dual Tn-seq does not require the construction of a large array of single mutants, it should be readily adaptable to various microorganisms.

CTP synthesis↗
DOE OSTI2024

Modeling PAH Mixture Interactions in a Human In Vitro Organotypic Respiratory Model

One of the most significant challenges in human health risk assessment is to evaluate hazards from exposure to environmental chemical mixtures. Polycyclic aromatic hydrocarbons (PAHs) are a class of ubiquitous contaminants typically found as mixtures in gaseous and particulate phases in ambient air pollution associated with petrochemicals from Superfund sites and the burning of fossil fuels. However, little is understood about how PAHs in mixtures contribute to toxicity in lung cells. To investigate mixture interactions and component additivity from environmentally relevant PAHs, two synthetic mixtures were created from PAHs identified in passive air samplers at a legacy creosote site impacted by wildfires. The primary human bronchial epithelial cells differentiated at the air–liquid interface were treated with PAH mixtures at environmentally relevant proportions and evaluated for the differential expression of transcriptional biomarkers related to xenobiotic metabolism, oxidative stress response, barrier integrity, and DNA damage response. Component additivity was evaluated across all endpoints using two independent action (IA) models with and without the scaling of components by toxic equivalence factors. Both IA models exhibited trends that were unlike the observed mixture response and generally underestimated the toxicity across dose suggesting the potential for non-additive interactions of components. Overall, this study provides an example of the usefulness of mixture toxicity assessment with the currently available methods while demonstrating the need for more complex yet interpretable mixture response evaluation methods for environmental samples.

3D in vitro models↗
DOE OSTI2025

Dataset, Code, and Models for Training Deep Learning Potentials for Low Temperature Plasma-Surface Interactions

This repository contains datasets, training scripts, and finished models, and test simulations used in the development of DeepREBO— a machine-learned interatomic potential trained to emulate the REBO2 empirical potential. The data was generated to study deep potential development for simulations of plasma-surface interactions. It uses an active learning framework, starting from a minimal dataset and iteratively expanding it. Included are those generated datasets, the trained models, and simulations used to evaluate the performance of the training process. This resource supports reproducibility and provides a reference framework for training deep potentials in plasma-surface interaction studies.

active learning↗
DOE OSTI2025

Simulation of Binary-single Interactions in AGN Disk. I. Gas-enhanced Binary Orbital Hardening

Stellar-mass binary black hole (BBH) mergers within the accretion disks of active galactic nuclei may contribute to gravitational wave (GW) events detected by ground-based GW detectors. In particular, the interaction between a BBH and a single stellar-mass black hole (sBH), known as the binary-single interaction (BSI) process, can potentially lead to GW events with detectable nonzero eccentricity. Previous studies of the BSI process, which neglected the effects of gas, showed that BSIs contribute non-negligibly to GW events in a coplanar disk environment. In this work, we conduct a series of two-dimensional hydrodynamical and N-body simulations to explore the BSI in a gas environment by coupling REBOUND with Athena++. We perform 360 simulation runs, spanning parameters in disk surface density Σ 0 and impact parameter b. We find that the gas-induced energy dissipation within the three-body system becomes significant if the encounter velocity between the sBHs is sufficiently large (≫c s ). Our simulation results indicate that approximately half of the end states of the BSI are changed by gas. Furthermore, at higher gas density, the number of close encounters during the BSI process will increase, and the end-state BBHs tend to be more compact. Consequently, the presence of gas may shorten the GW merger timescale for end-state BBHs and increase the three-body merger rate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Developing an Interactive OpenMP Book with Large Language Models

Abstract. This paper presents an approach to authoring a textbook titled Interactive OpenMP Programming with the assistance of Large Language Models (LLMs). The writing process utilized state-of-the-art LLMs, including Gemini Pro 1.5, Claude 3, and ChatGPT-4, to generate the initial structure and outline of the book, as well as the initial content for specific chapters. This content included detailed descriptions of individual OpenMP constructs and practical programming examples. The outline and content have then undergone extensive manual revisions to meet our book goals. In this paper, we report our findings about the capabilities and limitations of these LLMs. We address critical questions concerning the necessity of textbook resources and the effectiveness of LLMs in creating fundamental and practical programming content. Our findings suggest that while LLMs offer significant advantages in generating textbook content, they require careful integration with traditional educational methodologies to ensure depth, accuracy, and pedagogical effectiveness. The Interactive OpenMP Programming book is developed with the framework of Jupyter Book, enabling the execution of code within the book from the web browser, providing instant feedback and a dynamic learning experience that stands in contrast to traditional educational resources. The book represents a significant step towards modernizing programming education, offering insights into practical strategies for generating the textbook through advanced AI tools.

Large Language Model · OpenMP · Interactive Book ·↗
DOE OSTI2025

Strong Kitaev Interaction in BaCo 2⁢ (AsO 4 ) 2

The inelastic neutron scattering results and their analysis unequivocally point to a dominant Kitaev interaction in the honeycomb-lattice cobaltate BaCo 2 ⁢(AsO 4 ) 2 . Our anisotropic-exchange model closely describes all available neutron scattering data in the material’s field-polarized phase. Furthermore, the density-matrix renormalization group results for our model are in close accord with the unusual double-zigzag magnetic order and the low in-plane saturation field of BaCo 2 ⁢(AsO 4 ) 2 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

UOTe: Kondo‐Interacting Topological Antiferromagnet in a Van der Waals Lattice

Since the initial discovery of 2D van der Waals (vdW) materials, significant effort has been made to incorporate the three properties of magnetism, band structure topology, and strong electron correlations—to leverage emergent quantum phenomena and expand their potential applications. However, the discovery of a single vdW material that intrinsically hosts all three ingredients has remained an outstanding challenge. Here, in this work, the discovery of a Kondo-interacting topological antiferromagnet is reported in the vdW 5f electron system UOTe. It has a high antiferromagnetic (AFM) transition temperature of 150 K, with a unique AFM configuration that breaks the combined parity and time reversal (PT) symmetry in an even number of layers while maintaining zero net magnetic moment. This angle-resolved photoemission spectroscopy (ARPES) measurements reveal Dirac bands near the Fermi level, which combined with the theoretical calculations demonstrate UOTe as an AFM Dirac semimetal. Within the AFM order, the presence of the Kondo interaction is observed, as evidenced by the emergence of a 5ƒ flat band near the Fermi level below 100 K and hybridization between the Kondo band and the Dirac band. The density functional theory calculations in its bilayer form predict UOTe as a rare example of a fully-compensated AFM Chern insulator.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative Electron Beam‐Single Atom Interactions Enabled by Sub‐20‐pm Precision Targeting

The ability to probe and control matter at the picometer scale is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy offers powerful capabilities for materials analysis and modification, but sample damage, drift, and scan distortions hinder single atom analysis and deterministic manipulation. Materials analysis and modification via electron–solid interactions can be transformed by precise delivery of electrons to a specified atomic location, maintaining the beam position despite drift, and minimizing collateral dose. Here a fast, low-dose, sub-20-pm precision electron beam positioning technique is developed, “atomic lock-on,” (ALO), which offers the ability to position the beam on a specific atomic column without previously irradiating that column. This technique is used to lock onto a single selected atomic location to repeatedly measure its weak electron energy loss signal despite sample drift. Moreover, electron beam-matter interactions in single atomic events are measured with μ s time resolution. This enables observation of single-atom dynamics, such as atomic bistability, revealing partially bonded atomic configurations and recapture phenomena. This opens prospects for using electron microscopy for high-precision measurements and deterministic control of matter for quantum technologies.

2D materials↗
DOE OSTI2026

Enhancing NO Reduction by CO Over NiO/CeO 2 Catalyst by Optimizing the Metal Oxide–Support Interaction

Noble metal catalysts are widely used in three-way catalysts (TWCs) due to their high efficiency, but their scarcity and high cost drive the need for effective, low-cost alternatives. Here, in this work, a series of NiO/CeO 2 catalysts were engineered by modulating the OH content and crystallite size of the CeO 2 support. This approach yielded Ni species ranging from isolated single atoms to small clusters and larger aggregates, with varied NiO/CeO 2 interaction strengths. Evaluation of their performance in NO reduction by CO, CO oxidation, and the water–gas shift reaction revealed that the NiO/CeO 2 -300 catalyst, featuring small CeO 2 crystallites and highly dispersed NiO clusters, delivered superior activity for NO reduction and CO oxidation. It was demonstrated that small NiO clusters, due to their efficient CO adsorption and activation, were more active than Ni single atoms. Furthermore, the presence of water was found to influence the catalyst stability, with NiO clusters less stable than single atoms likely due to the hydroxylation effect leading to the formation of Ni(OH) 2 . Oxygen storage capacity measurements revealed that performance was governed by a combination of CeO 2 crystallite size and the abundance of NiO/CeO 2 interfaces. These findings demonstrated that the catalytic performance of Ni/CeO 2 systems could be maximized by optimizing the Ni nanostructure and its interaction with CeO 2 support, positioning them as a promising, multifunctional nonprecious alternative for three-way catalysis application.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In Silico Screening of CO 2 –Dipeptide Interactions for Bioinspired Carbon Capture

Carbon capture, sequestration and utilization offers a viable solution for reducing the total amount of atmospheric CO 2 concentrations. On an industrial scale, amine-based solvents are extensively employed for CO 2 capture through chemisorption. Nevertheless, this method is marked by the high cost associated with solvent regeneration, high vapor pressure, and the corrosive and toxic attributes of by-products, such as nitrosamines. An alternative approach is the biomimicry of sustainable materials that have strong affinity and selectivity for CO 2 . Bioinspired approaches, such as those based on naturally occurring amino acids, have been proposed for direct air capture methodologies. In this study, we present a database consisting of 960 dipeptide molecular structures, composed of the 20 naturally occurring amino acids. Furthermore, those structures were analyzed with a novel computational workflow presented in this work that considers certain interaction sites that determine CO 2 affinity. Density functional theory (DFT) and symmetry-adapted perturbation theory (SAPT) computations were performed for the calculation of CO 2 interaction energies, which allowed to limit our search space to 400 unique dipeptide structures. Using this computational workflow, we provide statistical insights into dipeptides and their affinity for CO 2 binding, as well as design principles that can further enhance CO 2 capture through cooperative binding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗