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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities.

Burley, Stephen K.↗
DOE OSTI2025

Exploring the role of 𝑑* hexaquarks on quark deconfinement and hybrid stars

We investigate the impact of the d*( 2380) hexaquark on the equation of state (EOS) of dense matter within hybrid stars (HSs) using the chiral mean-field model (CMF). The hexaquark is included as a new degree of freedom in the hadronic phase, and its influence on the deconfinement transition to quark matter is explored. We reparametrize the CMF model to ensure compatibility with recent astrophysical constraints, including the observation of massive pulsars and gravitational wave events. Our results show that the presence of d* significantly modifies the EOS, leading to a softening at high densities and a consequent reduction in the predicted maximum stellar masses. Furthermore, we examine the possibility of a first-order deconfinement phase transition within the context of the extended stability branch of slow stable HSs (SSHSs). We find that the presence of hexaquarks can delay the deconfinement phase transition and reduce the associated energy density gap, affecting the structure and stability of HSs. Our results suggest that, as the hexaquark appearance tends to destabilize stellar configurations, fine-tuning of model parameters is required to obtain both the presence of hexaquarks and quark deconfinement in these systems. In this scenario, the SSHS branch plays a crucial role in obtaining HSs with hexaquarks that satisfy current astrophysical constraints. Our work provides new insights into the role of exotic particles like d* in dense matter and the complex interplay between hadronic and quark degrees of freedom inside compact stellar objects.

Nuclear astrophysics↗
DOE OSTI2025

Exploring nonmultiplicativity in the geometric measure of entanglement

The geometric measure of entanglement (GME) quantifies how close a multipartite quantum state is to the set of separable states under the Hilbert-Schmidt inner product. The GME can be nonmultiplicative, meaning that the closest product state to two states is entangled across subsystems. In this work, we explore the GME in two families of states: those that are invariant under bilateral orthogonal (𝑂⊗𝑂) transformations, and mixtures of singlet states. In both cases, a region of GME nonmultiplicativity is identified around the antisymmetric projector state. We employ state-of-the-art numerical optimization methods and models to quantitatively analyze nonmultiplicativity in these states for 𝑑=3. Here, we also investigate a constrained form of GME that measures closeness to the set of real product states and show that this measure can be nonmultiplicative even for real separable states.

Quantum correlations in quantum information↗
DOE OSTI2026

Exploring the keV-scale physics potential of CUORE

We present the analysis techniques developed to explore the keV-scale energy region of the Cryogenic Underground Observatory for Rare Events (CUORE) experiment, based on more than 2 metric ton yr of data collected over five years. By prioritizing a stricter selection over a larger exposure, we are able to optimize data selection for thresholds at 10 keV and 3 keV with 691 kg yr and 11 kg yr of data, respectively. We study how the performance varies among the 988-detector array with different detector characteristics and data-taking conditions. We achieve an average baseline resolution of 2.54 ±0.14 keV FWHM and 1.18 ±0.02 keV FWHM for the data selection at 10 keV and 3 keV, respectively. The analysis methods employed reduce the overall background by about an order of magnitude, reaching 2.06±0.05 counts/(keV kg days) and 16±2 counts/(keV kg days) at the thresholds of 10 keV and 3 keV. We evaluate for the first time the near-threshold reconstruction efficiencies of the CUORE experiment, and find these to be 50 ±2% and 26 ±4% at 10 keV and 3 keV, respectively. This analysis provides crucial insights into rare decay studies, new physics searches, and keV-scale background modeling with CUORE. We demonstrate that ton-scale cryogenic calorimeters can operate across a wide energy range, from keV to MeV, establishing their scalability as versatile detectors for rare event and dark matter physics. These findings also inform the optimization of future large mass cryogenic calorimeters to enhance the sensitivity to low-energy phenomena.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Programmable exploration of magnetic states in Lieb-kagome interpolated lattices

We investigate a hybrid modeling framework in which a quantum annealer is used to simulate magnetic interactions in molecular qubit lattices inspired by experimentally realizable systems. Using phthalocyanine assemblies as a structurally constrained prototype, we model a continuous deformation from a Lieb to a kagome lattice, revealing frustration-driven disorder and magnetic field-induced reordering in the spin structure. Here, the goal is to show how a quantum annealer can operate as a physically instantiated, programmable platform to emulate experimentally relevant lattice deformations and produce observables in a manner analogous to an experimental measurement, enabling the characterization of magnetic arrangements beyond the reach of current molecular architectures. This surrogate modeling approach offers a pathway to explore and iteratively design tunable magnetic states in synthetic materials. The synthetic design, structural characterization, and quantum simulation framework established here defines a modular and scalable paradigm for probing the limits of engineered matter across chemistry, condensed matter, and quantum information science.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Exploring the interference between the atmospheric and solar neutrino oscillation subamplitudes

The interference between the atmospheric and solar neutrino oscillation subamplitudes is said to be responsible for C P violation () in neutrino appearance channels. More precisely, is generated by the interference between the parts of the neutrino oscillation amplitude that are C P even and C P odd: even or odd when the neutrino mixing matrix is replaced with its complex conjugate. This is the interference term, as it gives a contribution to the oscillation probability, the square of the amplitude, which is opposite in sign for neutrinos and antineutrinos and is unique. For this interference to be nonzero, at least two subamplitudes are required. There are, however, other interference terms, which are even under the above exchange, and these are the C P conserving () interference terms. In this paper, we explore in detail these interference terms and show that they cannot be uniquely defined, as one can move pieces of the amplitude from the atmospheric subamplitude to the solar subamplitude and vice versa. This freedom allows one to move the interference terms around, but does not let you eliminate them completely. We also show that there is a reasonable definition of the atmospheric and solar subamplitudes for the appearance channels such that in neutrino disappearance probability there is no atmospheric-solar interference term. However, with this choice, there is a interference term within the atmospheric sector. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Exploring entanglement and spectral split correlations in three-flavor collective neutrino oscillations

In environments with prodigious numbers of neutrinos, such as core-collapse supernovae, neutron star mergers, or the early Universe, neutrino-neutrino interactions are dynamically significant. They can dominate neutrino flavor evolution and force it to be nonlinear, causing collective neutrino oscillations. Such collective oscillations have been studied numerically, for systems with up to millions of neutrinos, using mean-field or one-particle effective approximations. However, such a system of interacting neutrinos is a quantum many-body system, wherein quantum correlations could play a significant role in the flavor evolution—thereby motivating the exploration of many-body treatments that follow the time evolution of these correlations. In many-body flavor evolution calculations with two neutrino flavors, the emergence of spectral splits in the neutrino energy distributions has been found to be correlated with the degree of quantum entanglement across the spectrum. In this work, for the first time, we investigate the emergence of spectral splits in the three-flavor many-body collective neutrino oscillations. We find that the emergence of spectral splits resembles the number and location found in the mean-field approximation but not in the width. Moreover, unlike in the two-flavor many-body calculations, we find that additional degrees of freedom make it more difficult to establish a correlation between the location of the spectral splits and the degree of quantum entanglement across the neutrino energy spectrum. The observation from the two-flavor case, that neutrinos nearest to the spectral split frequency exhibit the highest level of entanglement, is more difficult to ascertain in the three-flavor case because of the presence of multiple spectral splits across different pairwise combinations of flavor and/or mass states. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
DOE OSTI2025

Exploring the Utility-Privacy Trade-Off: Impacts of Semantic and Visit Types Ambiguities on Human Mobility Simulation

Humans are in perpetual movement, constantly traversing buildings, cities, waters, oceans, and countries. Mobility stands out as a major driving force shaping our modern societies. Capturing and explaining human behavior in a world of eight billion distinct mobility agendas is a complex challenge. With the rise of interconnected devices and platforms, such as smartphones, wearables, and point-of-interest data, largescale behavioral data has become more accessible, enabling rich insights into mobility patterns. However, the widespread availability of such data introduces significant ethical challenges. Detailed mobility data can inadvertently reveal sensitive personal information, including individuals' locations, habits, social interactions, and even political or religious affiliations. Beyond privacy breaches, the ethical implications of uncovering and potentially manipulating underlying behavioral patterns demand attention. Striking a balance between the utility of mobility models and the protection of individual privacy is therefore paramount. This paper explores the utility-privacy trade-offs in human mobility modeling, focusing on the impacts of introducing semantic and visit type ambiguities. By systematically examining how these ambiguities affect the fidelity of simulated trajectories and privacy risks, we provide a framework for evaluating ethical and privacy-conscious modeling practices. Our findings emphasize the need for methods that safeguard privacy without undermining the usefulness of mobility models, contributing to the responsible advancement of mobility science in alignment with ethical standards and societal expectations.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗
DOE OSTI2024

Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems

The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.

quantum computing, Harrow-Hassidim-Lloyd, high-per↗
DOE OSTI2026

Stellarator Design Exploration Using Symbolic-Regression Neutronics Surrogates

Systems codes require fast, simplified models to rapidly evaluate fusion power plant concepts, but neutronics analyses are often a computational bottleneck. Here, to address this, surrogate models for key neutronics responses have been developed using 3-D neutronics-ready models built with the open-source code ParaStell from a database of stellarator equilibria. Neutronics responses such as tritium breeding ratio (TBR), nuclear heating, and neutron-induced radiation damage displacements per atom (dpa) were simulated using OpenMC. Through sensitivity analysis and symbolic regression (SR), simple power-law formulas were derived connecting these neutronics responses to global stellarator parameters, including fusion power, plasma surface area, and plasma elongation. Validation shows these formulas can predict the simulation results with low error, enabling quick and accurate assessment of neutronics requirements in stellarator design exploration activities with systems codes.

Modeling↗
DOE OSTI2024

Divide and conquer: using RhizoVision Explorer to aggregate data from multiple root scans using image concatenation and statistical methods

Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Exploring Electrode-Level State-of-Charge and State-of-Health Dynamics in Lithium-Ion Battery Cells: Modeling and Experimental Identification

A computationally efficient model serves as a critical prerequisite for battery performance analysis and advanced battery management algorithm design. Although battery models that capture cell-level behavior have been widely explored in existing literature, electrode-level battery models have received much lesser attention till to date. However, such electrode-level models can significantly increase battery performance and life by enabling electrode-level health-conscious control. Such electrode-level control can effectively expand usable energy and power limits of the battery cells by utilizing the knowledge of individual electrodes' charge and health. In this context, this paper presents a comprehensive battery model developed with a reference electrode insertion that captures (i) electrode-level charge/discharge dynamics, (ii) stoichiometric and temporal dependencies of electrode-level resistances, (iii) solid electrolyte interface (SEI) layer growth as key degradation phenomenon, and (iv) capacity fade and resistance rise in each electrode due to nominal battery aging. The proposed model is identified, and a preliminary validation is performed utilizing terminal voltage and negative electrode potential data collected from a pouch cell under one continuous cycling and accelerated aging conditions where the cell experienced 14% capacity loss.

aging↗
DOE OSTI2025

Exploring Novel Configuration Designs Alternatives for Lazy Wave Submarine Power Cables

Floating offshore wind turbines require a complex integrated design to balance the platform response and station-keeping requirements in a highly dynamic environment. Dynamic subsea power cables are one of the critical components that have to be considered in detail to ensure both extreme, as well as repeated operational conditions do not exceed the cable design limits. This paper presents the design feasibility limits of a 66kV dynamic cable for a lazy wave configuration. The work explores an innovative staggered cable configuration that has the potential to provide additional compliance in shallow water conditions. Both configurations are evaluated using the motions of a representative floating wind platform under development for deployment in relatively shallow water depth of 150m. The assessment was conducted using the hydrodynamic software Orcaflex, incorporating a range of design inputs and design variations. Key parameters analysed include cable curvature and effective tension and bending stress. The alternative cable configuration provides a more gradual crossing of the water column, which would allow to support increased platform excursions in shallow water depths. The design uses a combined buoyancy and bend protection element, offering designers additional options for challenging operational conditions. The paper will be of interest to designers and developers of floating wind projects, providing a comparative assessment between the conventional lazy wave configuration and an innovative alternative that enables larger cable and mooring excursion envelopes in challenging conditions.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Enhanced nucleation mechanism in ruthenium atomic layer deposition: Exploring surface termination and precursor ligand effects with RuCpEt(CO)2

Miniaturization of microelectronic devices necessitates atomic precision in manufacturing techniques, particularly in the deposition of thin films. Atomic layer deposition (ALD) is recognized for its precision in controlling film thickness and composition on intricate three-dimensional structures. This study focuses on the ALD nucleation and growth mechanisms of ruthenium (Ru), a metal that has significant future implications for microelectronics. Despite its advantages, the deposition of a high surface-free energy material like Ru on a low surface-free energy material such as an oxide often faces challenges of large nucleation delays and non-uniform growth. To address these challenges, we explored the effectiveness of organometallic surface pretreatments using trimethylaluminum (TMA) or diethylzinc (DEZ) to enhance Ru film nucleation and growth. Our study employed a less-studied Ru precursor, cyclopentadienylethyl(dicarbonyl)ruthenium [RuCpEt(CO)2], which demonstrated promising results in terms of reduced nucleation delay and increased film continuity. Ru ALD was performed on silicon substrates with native oxide, using RuCpEt(CO)2 and O2 as coreactants. Our findings reveal that surface pretreatment significantly improves nucleation density and film thickness within the initial 60 ALD cycles, achieving up to a 3.2-fold increase in Ru surface coverage compared to nonpretreated substrates. Supported by density functional theory calculations, we propose that the enhanced nucleation observed with RuCpEt(CO)2 compared to previously-studied Ru(Cp)2 is due to two key mechanisms: the facilitated removal of CO ligands during deposition, which enhances the reactivity of the precursor, and a hydrogen-abstraction reaction involving the ethyl ligand of RuCpEt(CO)2 and the metal-alkyl groups on the surface. This study not only advances our understanding of Ru ALD processes but also highlights the significant impact of precursor chemistry and surface treatments in optimizing ALD for advanced microelectronic applications.

Materials Science↗
DOE OSTI2026

Exploring life’s hidden majority: microbial dark matter symposium highlights

The Microbial Dark Matter Symposium held on August 28–29, 2025, in Laguna Beach, Orange County, CA, convened a multidisciplinary group of scientists to address the vast unknowns in microbial life—from uncultured taxa and uncharacterized proteins to elusive viruses and spacefaring microbes. Set against a scenic coastal backdrop, the symposium highlighted advances in single-cell genomics, proximity ligation sequencing, and artificial intelligence-ready bioinformatics, while also probing the limits of microbial persistence, metabolism, and ecological distribution. Sessions explored microbial dark matter from multiple dimensions: cultivability, where new strategies are enabling recovery of elusive microbes; functional ambiguity, where metagenomic dark zones are illuminated by computational annotation; and genomic representation, where single-cell methods bridge gaps left by shotgun community sequencing. Researchers shared breakthroughs in identifying atmospheric microbiomes, “dark oxygen” production in groundwater ecosystems, and microbial survival on the International Space Station. The symposium emphasized integration of methods, disciplines, and ecosystems, advancing a collective push to illuminate the microbial dark matter on Earth and beyond. By highlighting emerging tools, pressing questions, and cross-domain insights, the symposium underscored the need for collaborative, open, and adaptive approaches to study the microbial unknown. The meeting marks a pivotal moment in microbiology, where cultivating knowledge of the uncultivated promises transformative understanding of life, everywhere.

Podar, Mircea [ORNL] (ORCID:0000000327760205)↗
DOE OSTI2025

Exploring baryon resonances with transition generalized parton distributions: status and perspectives

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum, and forces in the system. Transition GPDs extend these concepts to N → N* transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions e + N → e' + M + N*, such as deeply-virtual Compton scattering (M = γ) or meson production (M = π, K, etc.), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperipheral collisions), and the theoretical and experimental developments needed to realize this program.

Experimental Nuclear Physics↗
DOE OSTI2025

Meld: A project for exploring how to meet DUNE's framework needs

Existing data-processing frameworks for HEP experiments are largely based on collider-physics concepts, which may be based on rigid, event-based data hierarchies. These data organizations are not always helpful for neutrino experiments, which must sometimes work around such restrictions by manually splitting apart events into constructs that are better suited for neutrino physics. The purpose of Meld is to explore more flexible data organizations by treating a frameworks job as: (1) A graph of data-product sequences connected by (2) User-defined functions that serve as operations to (3) Framework-provided higher-order functions.

Knoepfel, KyleJ. [Fermi National Accelerator Labor↗