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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deciphering the structure of Arabidopsis thaliana 5-enol-pyruvyl-shikimate-3-phosphate synthase: An essential step toward the discovery of novel inhibitors to supersede glyphosate

Glyphosate interferes with plant aromatic metabolism through the inhibition of 5-enol-pyruvyl-shikimate-3-phosphate (EPSP) synthase [EPSPS, EC 2.5.1.19]. For this reason, EPSPS has been extensively studied in a vast array of organisms. This notwithstanding, up to date, the crystal structure of the protein has been solved exclusively in a few prokaryotes, while that of the plant enzyme has been only deduced in silico by similarity. This study aimed at determining the structure of EPSPS from the plant model species Arabidopsis thaliana, which has been cloned, heterologously expressed and affinity-purified. The kinetic properties of the enzyme have been determined, as well as its susceptibility to the inhibition brought about by glyphosate. The crystal structure of the protein has been resolved at high resolution (1.4 Å), showing open conformation of the enzyme, which is the state ready for substrate/inhibitor binding. This provides a framework for the structure-based design of novel EPSPS inhibitors. Surface regions near the active-site cleft entrance or at the interdomain hinge appear promising for inhibitor selectivity, while bound chloride near the active site is a potential placeholder for anionic moieties of future herbicides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Interlayer Interactions in 1D Van der Waals Moiré Superlattices.

Studying two-dimensional (2D) van der Waals (vdW) moiré superlattices and their interlayer interactions have received surging attention after recent discoveries of many new phases of matter that are highly tunable. Different atomistic registry between layers forming the inner and outer nanotubes can also form one-dimensional (1D) vdW moiré superlattices. In this review, experimental observations and theoretical perspectives related to interlayer interactions in 1D vdW moiré superlattices are summarized. The discussion focuses on double-walled carbon nanotubes (DWNTs), a model 1D vdW moiré system, and the authors highlight the new optical features emerging from the non-trivial strong interlayer coupling effect and the unique physics in 1D DWNTs. Future directions and questions in probing the intriguing physical phenomena in 1D vdW moiré superlattices such as, correlated physics in different 1D moiré systems beyond DWNTs are proposed and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Structure of the essential inner membrane lipopolysaccharide–PbgA complex

Lipopolysaccharide (LPS) resides in the outer membrane of Gram-negative bacteria where it is responsible for barrier function. LPS can cause death as a result of septic shock, and its lipid A core is the target of polymyxin antibiotics. Despite the clinical importance of polymyxins and the emergence of multidrug resistant strains, our understanding of the bacterial factors that regulate LPS biogenesis is incomplete. Here we characterize the inner membrane protein PbgA and report that its depletion attenuates the virulence of Escherichia coli by reducing levels of LPS and outer membrane integrity. In contrast to previous claims that PbgA functions as a cardiolipin transporter, our structural analyses and physiological studies identify a lipid A-binding motif along the periplasmic leaflet of the inner membrane. Synthetic PbgA-derived peptides selectively bind to LPS in vitro and inhibit the growth of diverse Gram-negative bacteria, including polymyxin-resistant strains. Proteomic, genetic and pharmacological experiments uncover a model in which direct periplasmic sensing of LPS by PbgA coordinates the biosynthesis of lipid A by regulating the stability of LpxC, a key cytoplasmic biosynthetic enzyme. In summary, we find that PbgA has an unexpected but essential role in the regulation of LPS biogenesis, presents a new structural basis for the selective recognition of lipids, and provides opportunities for future antibiotic discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Colossal transverse magnetoresistance due to nematic superconducting phase fluctuations in a copper oxide

Electronic anisotropy (“nematicity”) has been detected in cuprate superconductors by various experimental techniques. Using angle-resolved transverse resistance (ARTR) measurements, a very sensitive and background-free technique that can detect 0.5% anisotropy in transport, we have observed it also in La 2-x Sr x CuO 4 (LSCO) for 0.02 ≤ x ≤ 0.25. A central enigma in LSCO is the rotation of the nematic director (orientation of the largest longitudinal resistance) with temperature; this has not been seen before in any material. Here, we address this puzzle by measuring the angle-resolved transverse magnetoresistance (ARTMR) in LSCO. We report the discovery of colossal transverse magnetoresistance (CTMR)—an order-of-magnitude drop in the transverse resistivity in the magnetic field of 6 T. We show that the apparent rotation of the nematic director is caused by anisotropic superconducting fluctuations, which are not aligned with the normal electron fluid, consistent with coexisting bond-aligned and diagonal nematic orders. We quantify this by modeling the (magneto-)conductivity as a sum of normal (Drude) and paraconducting (Aslamazov–Larkin) channels but extended to contain anisotropic Drude and Cooper-pair effective mass tensors. Strikingly, the anisotropy of Cooper-pair stiffness is much larger than that of the normal electrons. It grows dramatically on the underdoped side, where the fluctuations become quasi-one-dimensional. Our analysis is general rather than model dependent. Still, we discuss some candidate microscopic models, including coupled strongly-correlated ladders where the transverse (interladder) phase stiffness is low compared with the longitudinal intraladder stiffness, as well as the anisotropic superconducting fluctuations expected close to the transition to a pair-density wave state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Supermassive Black Holes, Ultralight Dark Matter, and Gravitational Waves from a First Order Phase Transition

We report that the formation of ultra rare supermassive black holes (SMBHs), with masses of $\mathcal{O}$(10 9 M ⊙ ), in the first billion years of the Universe remains an open question in astrophysics. At the same time, ultralight dark matter (DM) with mass in the vicinity of $\mathcal{O}$(10 -20 eV) has been motivated by small scale DM distributions. Though this type of DM is constrained by various astrophysical considerations, certain observations could be pointing to modest evidence for it. We present a model with a confining first order phase transition at ~ 10 keV temperatures, facilitating production of $\mathcal{O}$(10 9 M ⊙ ) primordial SMBHs. Such a phase transition can also naturally lead to the implied mass for a motivated ultralight axion DM candidate, suggesting that SMBHs and ultralight DM may be two sides of the same cosmic coin. We consider constraints and avenues to discovery from superradiance and a modification to N eff . On general grounds, we also expect primordial gravitational waves – from the assumed first order phase transition – characterized by frequencies of $\mathcal{O}$(10 -12 - 10 -9 Hz). This frequency regime is largely uncharted, but could be accessible to pulsar timing arrays if the primordial gravitational waves are at the higher end of this frequency range, as could be the case in our assumed confining phase transition.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Bridging Cloud and Edge Computing at NREL Using CONNECT: Cloud Optimized Networking for Next-Gen Edge Computing Technologies [Slides]

CONNECT is an innovative on-premise hardware and software solution that integrates edge and cloud computing infrastructure at NREL. Built on the AWS Greengrass middleware and leveraging the MQTT protocol, CONNECT enables real-time data streaming from IoT devices and gateways to both cloud and local services, empowering researchers to rapidly capture, analyze, and act upon edge-generated data while leveraging cloud capabilities. The platform addresses research infrastructure challenges by providing a pre-approved platform which is already configured with the correct networking and cybersecurity baselines thus eliminating procurement delays and enabling on-demand availability. CONNECT's hybrid architecture efficiently manages burstable workloads, allowing research teams to dynamically scale computational capacity, handle peak data loads, and reduce operational bottlenecks. Advanced capabilities include built-in GPU support for executing machine learning models which enables low-latency inference at the edge from models trained in the cloud. This architecture supports real-time analytics and filtering, providing a mechanism to allow only transmitting and processing high-value data. Cloud-based configuration management permits engineers to manage on-premise systems remotely, optimizing operational efficiency. By bridging edge and cloud computing, CONNECT provides NREL researchers with a flexible, scalable platform that accelerates scientific discovery while maintaining robust security and performance standards.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning

Specific structural motifs in inorganic solids are often related to their targeted physical properties. For many classes of solids, such as Zintl phases and polar intermetallics, the crystal structures are diverse and not easy to predict. Various antimonides that are potential thermoelectric materials were proposed to be synthesizable on the basis of their estimated formation energies. Their structures were broadly classified as clathrate, channel, layered, or network through a machine learning model trained on existing ternary phases and features based on elemental properties using the sure independence screening and sparsifying operator algorithm. Through experimental validation, three new ternary antimonides were synthesized and confirmed to form layered structures: tetragonal RbAlSb 2 and CsAlSb 2 , which are isopointal but not isotypic to LiBSi 2 ; and monoclinic Rb 2 Al 2 Sb 3 , which adopts the Na 2 Al 2 Sb 3 -type structure. Finally, reinvestigation of the related compound Cs 2 In 2 Sb 3 revealed a low thermal conductivity and p-type semiconducting behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery

Methods that accelerate the evaluation of molecular properties are essential for chemical discovery. While some degree of ligand additivity has been established for transition metal complexes, it is underutilized in asymmetric complexes, such as the square pyramidal coordination geometries highly relevant to catalysis. To develop predictive methods beyond simple additivity, we apply a many-body expansion to octahedral and square pyramidal complexes and introduce a correction based on adjacent ligands (i.e., the cis interaction model). We first test the cis interaction model on adiabatic spin-splitting energies of octahedral Fe(II) complexes, predicting DFT-calculated values of unseen binary complexes to within an average of 1.4 kcal/mol. Uncertainty analysis reveals the optimal basis, comprising the homoleptic and mer symmetric complexes. We next show that the cis model (i.e., the cis interaction model solved for the optimal basis) infers both DFT- and CCSD(T)-calculated model catalytic reaction energies to within 1 kcal/mol on average. The cis model predicts low-symmetry complexes with reaction energies outside the range of binary complex reaction energies. We observe that trans interactions are unnecessary for most monodentate systems but can be important for some combinations of ligands, such as complexes containing a mixture of bidentate and monodentate ligands. Lastly, we demonstrate that the cis model may be combined with Δ-learning to predict CCSD(T) reaction energies from exhaustively calculated DFT reaction energies and the same fraction of CCSD(T) reaction energies needed for the cis model, achieving around 30% of the error from using the CCSD(T) reaction energies in the cis model alone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data-driven multi-element substitution of TiFe alloys for tunable thermodynamics and enhanced activation behaviour for hydrogen storage

Due to their high volumetric hydrogen storage capacity under moderate storage conditions, TiFe alloys have been widely investigated as candidates for practical solid-state hydrogen storage. Partially substituting Ti or Fe sites can improve the key characteristics of TiFe alloys, such as the first hydrogen absorption step (activation) and the equilibrium hydrogen pressure (thermodynamic properties). However, the selection of substitution elements has heavily relied on intuition and trial-and-error. Also, conventional substitution strategies have mainly focused on single-element substitution within the TiFe alloy, limiting the design space and tunability for target applications. Here, to address this limitation, we report a multi-element substitution strategy motivated by an efficient, data-driven machine learning (ML) approach combined with corroborating density functional theory (DFT) calculations. Our models successfully predict experimentally measured hydride stability in five selected alloys using only compositional descriptors. Most importantly, the multi-element substitution leads to enhanced activation properties compared to pure TiFe, achieving near room-temperature activation behaviour. This work provides a method for on-demand tuning of hydrogen storage and activation properties, which may have broad implications for data-driven discovery of energy storage materials.

Cho, YongJun [Korea Advanced Institute Science and↗
DOE OSTI2022

Identifying $\textit{f}$-electron symmetries of UTe 2 with O-edge resonant inelastic x-ray scattering

The recent discovery of spin-triplet superconductivity emerging from a nonmagnetic parent state in UTe 2 has stimulated great interest in the underlying mechanism of Cooper pairing. Experimental characterization of short-range electronic and magnetic correlations is vital to understanding these phenomena. Here we use resonant inelastic x-ray scattering (RIXS), x-ray absorption spectroscopy (XAS), and atomic-multiplet-based modeling to shed light on the active debate between $5f^26d^1$-based models with singlet crystal field states versus $5f^3$-based models that predict atomic Kramers doublets and much greater $5\textit{f}$ itinerancy. In this work, the XAS and RIXS data are found to agree strongly with predictions for a $5f^26d^1$-like valence electron configuration with weak intradimer magnetic correlations, and provide a context for interpreting recent investigations of the electronic structure and superconducting pairing mechanism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Flavor patterns of fundamental particles from quantum entanglement?

The Cabibbo-Kobayashi-Maskawa (CKM) matrix, which controls flavor mixing between the three generations of quark fermions, is a key input to the standard model of particle physics. In this paper, we identify a surprising connection between quantum entanglement and the degree of quark mixing. Focusing on a specific limit of 2 → 2 quark scattering mediated by electroweak bosons, we find that the quantum entanglement generated by scattering is minimized when the CKM matrix is almost (but not exactly) diagonal, in qualitative agreement with observation. With the discovery of neutrino masses and mixings, additional angles are needed to parametrize the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix in the lepton sector. Applying the same logic, we find that quantum entanglement is minimized when the PMNS matrix features two large angles and a smaller one, again in qualitative agreement with observation, plus a hint for suppressed C P violation. We speculate on the (unlikely but tantalizing) possibility that minimization of quantum entanglement might be a fundamental principle that determines particle physics input parameters. Published by the American Physical Society 2025

Thaler, Jesse (ORCID:0000000224068160)↗
DOE OSTI2023

What’s the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction

Abstract We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as “real–bogus” classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real–bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses “image triplets” (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ∼96% to ∼91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real–bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the difference image analysis entirely.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

A data-driven method to estimate the antiproton background in the Mu2e experiment

The Mu2e experiment at Fermilab will search for the Charged Lepton Flavour Violating (CLFV) process of coherent, neutrinoless µ− → e − conversion in the field of an aluminum nucleus. The expected signal is a monochromatic electron with the energy of 104.97 MeV, slightly below the muon rest mass. Observation of a CLFV process would provide unambiguous evidence for Beyond the Standard Model (BSM) physics. Mu2e is sensitive to a wide range of BSM models and has the capability to distinguish between them, guiding us towards the most accurate models. The key features of the Mu2e experiment are: (1) a high intensity pulsed negative muon beam with about 1010 stopped µ −/s, and (2) a sophisticated superconducting solenoid system with a gradient magnetic field to form and guide the intense muon beam to the target. The Mu2e physics data taking is expected to begin in 2027. For Run I, the expected 5σ discovery sensitivity is Rµe = 1.2 × 10−15, with a total expected background of 0.11 ± 0.03 events. In the absence of a signal, the expected upper limit is Rµe < 6.2 × 10−16 at 90% CL. The success of this experiment hinges on the accurate estimation of the background from various SM processes that could provide signal-like electrons. One of the background processes is antiprotons annihilating in the stopping target to produce signal like electrons through π0 → γγ decays followed by γ conversions, and π− → µ−ν¯ decays followed by µ− decay. It is a relatively small background with large uncertainty (100%) due to the lack of antiproton production cross section information for the Mu2e proton beam energy of 8 GeV. We have developed a novel methodology to estimate the antiproton background in-situ. This forms the main theme of the thesis. We observed that at Mu2e energies, antiproton annihilation in the stopping target is the only source of events with multiple, simultaneous particle trajectories. From Geant4 simulations, only about 0.2% of the simulated antiproton annihilation events have a signal-like electron. Meanwhile, ∼ 5% of events have multiple reconstructible particle tracks per event. Therefore, we have devised a methodology to reconstruct the multi-track events and estimate the antiproton background by exploiting the large ratio of the production rates of the two final states.

Chithirasreemadam, Namitha [Pisa U.] (ORCID:000000↗
DOE OSTI2024

Formation and coarsening of epitaxially-supported metal nanoclusters

This mini-review describes developments over the last ~30 years in characterizing the nucleation & growth of epitaxially-supported metal nanoclusters (NCs) or islands during vapor deposition, as well as their post-deposition coarsening. A beyond-mean-field treatment for homogeneous nucleation & growth corrects the deficiencies of traditional treatments in describing, e.g., the island size distribution, but also necessitates consideration of the spatial distribution of islands and their capture zones. We discuss advances in modeling capabilities, including those based upon on an ab-initio level treatment of periphery diffusion kinetics, for description of the non-equilibrium growth shapes of these NCs, focusing on 2D NCs. For post-deposition coarsening of arrays of NCs, there is generally a competition between Ostwald Ripening (OR) and Smoluchowski Ripening (SR). SR is also known as Particle Migration & Coalescence. For 2D NCs in homoepitaxial systems, conventional OR is observed on pristine fcc(111) surfaces, dramatically enhanced OR in the presence of even trace amounts of chalcogens for Cu(111) and Ag(111), and anomalous OR on anisotropic fcc(110) surfaces. The unexpected discovery of SR for fcc(100) homoepitaxial systems prompted extensive analysis of the underlying diffusivities of 2D NCs as a function of size, as well as of NC coalescence dynamics. A comprehensive understanding of these processes is now available. Self-assembly of 3D NCs during deposition, issues related to heterogeneous nucleation, directed assembly, NC growth structure selection, and coarsening are addressed. Finally, for SR of 3D epitaxial NCs, recent insights into the size-dependence of diffusivity are described.

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

Generative Electrolyte Solvent and Formulation Discovery

Molecular mixtures and/or formulations are of great importance in fields ranging from materials science to pharmaceuticals to chemistry. In batteries, electrolytes are complex molecular mixtures consisting of multiple salts and solvents and additives at different concentrations that dictate battery capacity, safety, and cycle life, among others. Unfortunately, due to the complex composition and infinite design space as well as the conflicting property requirements, electrolyte design is the rate-determining step in the design of next generation battery chemistries. In this work, we develop a transformer-based generative AI model − ElectrolyteGPT − capable of generating solvents and electrolyte formulations to satisfy a wide range of desired property requirements. First, we curate an electrolyte-relevant database and develop a new line notation for formulations. Then, we show that ElectrolyteGPT can generate solvents and formulations conditioned on a wide range of important electrolyte properties such as ionic conductivity, oxidative stability, Coulombic efficiency, viscosity, and more. Finally, we experimentally synthesize the generated solvents and fabricate the electrolyte formulations and show that they can meet the desired property requirements and enable longterm cycling in energy-dense anode-free lithium metal batteries. Our work showcases the ability of generative models to address challenges in molecular mixture design for next generation batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

What's Left for a Computational Chemist To Do in the Age of Machine Learning?

Machine learning (ML) has become a central focus of the computational chemistry community. In this paper, I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low-cost methods (e.g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open-shell transition-metal chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimal celestial bodies for dark matter detection

A wide variety of celestial bodies have been considered as dark matter detectors. Which stands the best chance of delivering the discovery of dark matter? Which is the most powerful dark matter detector? We investigate a range of objects, including the Sun, Earth, Jupiter, Brown Dwarfs, White Dwarfs, Neutron Stars, Stellar populations, and Exoplanets. We quantify how different objects are optimal dark matter detectors in different regimes by deconstructing some of the in-built assumptions in these search sensitivities, including observation potential and particle model assumptions. We find new constraints and future sensitivities across a range of dark matter annihilation final states. We quantify mediator properties leading to detectable celestial-body energy injection or Standard Model fluxes, and show how different objects can be expected to deliver corroborating signals. We discuss different search strategies, their opportunities and limitations, and the interplay of regimes where different celestial objects are optimal dark matter detectors. Deconstructing the assumptions of these searches leads us to point out a new search using the Galactic center stellar population that can provide greater sensitivity to the dark matter-nucleon scattering cross section than the Sun, despite being significantly further away in our Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

An Analysis of High Opportunity Geothermal Leasing Areas on BLM and USFS Lands

This analysis, conducted by the National Renewable Energy Laboratory (NREL) with support from the U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO), models potential geothermal deployment on BLM and USFS land for the years 2025, 2030, 2035, and 2050 under various scenarios using the Renewable Energy Potential (reV) model and the Regional Energy Deployment System (ReEDS) model. This report identifies potential future high opportunity geothermal leasing areas (i.e., Best, Middle, and Least identified opportunities) based on available data for geothermal resource potential, natural resource conflicts, and transmission access. In addition, this analysis considers the impacts of the Energy Policy Act of 2005's (EPAct 2005's) default competitive leasing provisions, specifically the impact this may have had on geothermal resource discovery outside of known geothermal resource areas.

15 GEOTHERMAL ENERGY↗