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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Constraining nucleon effective masses with flow and stopping observables from the S π RIT experiment

Properties of the nuclear equation of state (EoS) can be probed by measuring the dynamical properties of nucleus-nucleus collisions. In this study, we present the directed flow (υ 1 ), elliptic flow (υ 2 ) and stopping (VarXZ) measured in fixed target Sn + Sn collisions at 270 AMeV with the SπRIT Time Projection Chamber. We perform Bayesian analyses in which EoS parameters are varied simultaneously within the Improved Quantum Molecular Dynamics-Skyrme (ImQMD-Sky) transport code to obtain a multivariate correlated constraint. The varied parameters include symmetry energy, S 0 , and slope of the symmetry energy, L, at saturation density, isoscalar effective mass, $m^{⁎}_{S}$/$m_{N}$ , isovector effective mass, $m^{⁎}_{υ}$/$m_{N}$ and the in-medium cross-section enhancement factor η. We find that the flow and VarXZ observables are sensitive to the splitting of proton and neutron effective masses and the in-medium cross-section. Comparisons of ImQMD-Sky predictions to the SπRIT data suggest a narrow range of preferred values for $m^{⁎}_{S}$/$m_{N}$, $m^{⁎}_{υ}$/$m_{N}$ and η.

Astronomy & Astrophysics↗
DOE OSTI2022

Near-infrared spectra and molar absorption coefficients of trivalent lanthanides dissolved in molten LiCl–KCl eutectic

Determining the concentration of the dissolved lanthanide species in LiCl–KCl eutectic salt is important to the development of pyrochemical reprocessing of used nuclear fuel. In this process, lanthanide fission products are found dissolved in the electrorefiner electrolyte in their trivalent oxidation state. The presence of dissolved trivalent lanthanides increases the liquidus temperature of the electrolyte mixture and can lead to the formation of insoluble oxide or oxychloride phases and must therefore be continuously monitored and controlled during the operation. Absorbance spectroscopy is a promising method for continuous measurement of the concentration of lanthanides and other elements dissolved in the electrolyte. The absorption of light by elements is linearly proportional to the concentration of the element for relatively dilute solutions according to the Beer-Lambert law. Although measurement of the absorption of ultraviolet and visible range light by lanthanides in LiCl–KCl eutectic molten salt have been explored previously, near infrared (NIR) absorption spectroscopy has received far less attention. It may, however, provide a better analytical signal when insoluble phases are present due to less Raleigh scattering compared to shorter wavelength radiation. Additionally, it may allow for concentration determination for certain elements using NIR absorption features where UV and visible range features are overlapping with features from other species. In this study, we report the UV–Vis–NIR spectra of the trivalent lanthanide chlorides of neodymium, samarium, and dysprosium in LiCl–KCl eutectic. Molar absorption coefficients are reported for analytically useful absorption maxima, with a focus on the molar absorption coefficients for NIR absorption maxima which have not been reported previously. Additionally, we observe a NIR-range absorption band of Nd3+ which was previously predicted but never experimentally observed. Here, we compare the calculated crystal field levels to the newly observed absorbance band and find them to be in good agreement with previous predictions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Regulation of the normalized rate of driven magnetic reconnection through shocked flux pileup

Magnetic reconnection is explored on the Terrestrial Reconnection Experiment (TREX) for asymmetric inflow conditions and in a configuration where the absolute rate of reconnection is set by an external drive. Magnetic pileup enhances the upstream magnetic field of the high-density inflow, leading to an increased upstream Alfvén speed and helping to lower the normalized reconnection rate to values expected from theoretical consideration. In addition, a shock interface between the far upstream supersonic plasma inflow and the region of magnetic flux pileup is observed, important to the overall force balance of the system, thereby demonstrating the role of shock formation for configurations including a supersonically driven inflow. Despite the specialized geometry where a strong reconnection drive is applied from only one side of the reconnection layer, previous numerical and theoretical results remain robust and are shown to accurately predict the normalized rate of reconnection for the range of system sizes considered. This experimental rate of reconnection is dependent on system size, reaching values as high as 0.8 at the smallest normalized system size applied.

Physics↗
DOE OSTI2026

Machine-Learning-Driven Discovery of Water Splitting BaFe 2 O 4 and Human-in-the-Loop Improvement via Al-Substitution for Increased Thermal Stability

Thermochemical hydrogen (TCH) production offers a promising method for converting thermal energy into hydrogen fuel through heat-driven redox cycles of metal oxides. Here, in this work a defect graph neural network (dGNN) was used to predict oxygen vacancy formation energies ΔH V O combined with Materials Project predictions of oxygen chemical potential stability to screen candidate oxides via high-throughput database analysis. BaFe 2 O 4 was identified as a promising material for experimental validation based on its predicted ΔH V O , oxygen chemical potential stability range, and potential for tunable substitutions to improve thermal properties. Experimental validation using thermogravimetric analysis (TGA), stagnation flow reactor (SFR), X-ray diffraction (XRD), and electron microscopy confirmed positive water-splitting behavior but also revealed limitations in thermal stability under aggressive reduction conditions. To address this, a human-in-the-loop modification strategy was employed introducing Al substitution in BaFe 2–x Al x O 4 ; this modification improves thermal stability, alters the crystal structure and enhances overall performance. These results demonstrate a combined computational and experimental workflow in which machine learning accelerates identification of promising candidates, while targeted experimental design enables optimization of functional performance. This approach advances the development of robust, cost-effective TCH materials and highlights the importance of integrating data-driven discovery with human-guided materials design in paving the way for scalable hydrogen production technologies.

organic↗
DOE OSTI2025

The Crucial Role of Vacancy Concentration in Enabling Superatomic Diffusion in Lithium Intermetallics

Anode-free solid-state Li batteries promise significant increases in energy densities compared to current commercial batteries that rely on liquid electrolytes. Major challenges persist in controlling morphological evolution during the plating and stripping of lithium metal at the anode current collector. Elemental additives that alloy with lithium have been found to modify the plating and stripping behavior of lithium. Many alloying elements form intermetallics with lithium and the mobility of Li through these intermetallics is believed to have an important effect on morphological evolution. This study shows that Li transport coefficients through intermetallics span a wide range in values, with the B32 LiAl intermetallic predicted to have a Li tracer diffusion coefficient as high as 10 –6 cm 2 /s at room temperature, which is 8 orders of magnitude larger than that of isostructural B32 LiZn. This work demonstrates the crucial role of vacancy concentration in controlling the mobility of Li atoms through intermetallics. While the migration barriers for Li-vacancy exchanges in both LiAl and LiZn are remarkably low, the superatomic conductivity in LiAl is shown to arise from the unique electronic structure of the B32 LiAl compound, which favors high concentrations of vacancies.

25 ENERGY STORAGE↗
DOE OSTI2026

Mixed Nd 4+/3+ and Cluster Magnetism in Hexagonal Perovskite 12R-Ba 4 NdMn 3 O 12−γ

Hexagonal perovskite oxides with 12R stacking host well-separated face-sharing octahedral metal trimers with short metal–metal distances, leading in some cases to large degrees of magnetic frustration and cluster magnetism. Introducing magnetic ions in proximity to these trimers can influence the degree of frustration and cluster magnetism, but the extent to which the magnetism can be tuned by varying neighboring metal cations remains an open question. In this work, we test the impacts of using Nd in proximity to Mn trimers in the hexagonal perovskite 12R-Ba 4 NdMn 3 O 12−γ . Given this stoichiometry, Nd should assume the 4+ oxidation state with a spin state of S = 1, which would be the first realization of Nd 4+ in an oxide environment. Through detailed bulk magnetic, X-ray absorption spectroscopic, and powder neutron diffraction (PND) measurements, we find that Nd 4+ is realized in this material, but there is also partial reduction to Nd 3+ , which is charge-balanced by O vacancies. Magnetometry measurements indicate an antiferromagnetic ordering temperature T N ≈ 16 K, and PND measurements reveal a surprising collinear antiferromagnetic structure with magnetic space group Pc2/m (no. 10.49 in BNS notation), which has not previously been seen in this class of materials. Our results represent a comprehensive analysis of the structural, electronic, and magnetic properties of 12R-Ba 4 NdMn 3 O 12−γ , showing the first observation of partial Nd 4+ in an oxide, and demonstrating that this structural class can host a broad range of magnetic structures which are not easily predicted based on compositional trends.

14 SOLAR ENERGY↗
DOE OSTI2023

Range and strength of mechanical interactions of force dipoles in elastic fiber networks

Mechanical forces generated by myosin II molecular motors drive diverse cellular processes, most notably shape change, division and locomotion. These forces may be transmitted over long range through the cytoskeletal medium – a disordered, viscoelastic network of biopolymers. The resulting cell size scale force chains can in principle mediate mechanical interactions between distant actomyosin units, leading to self-organized structural order in the cell cytoskeleton. Inspired by such force transmission through elastic structures in the cytoskeleton, we consider a percolated fiber lattice network, where fibers are represented as linear elastic elements that can both bend and stretch, and the contractile activity of myosin motors is represented by force dipoles. Then, by using a variety of metrics, we show how two such contractile force dipoles interact with each other through their mutual mechanical deformations of the elastic fiber network. As a prelude to two-dipole interactions, we quantify how forces propagate through the network from a single anisotropic force dipole by analyzing clusters of nodes connected by highly strained bonds, as well as through the decay rate of strain energy with distance from a force dipole. We show that predominant fiber bending screens out force propagation, resulting in reduced and strongly network configuration-dependent dipole interactions. On the other hand, stretching-dominated networks support longer-ranged inter-dipole interactions that recapitulate the predictions of linear elasticity theory. By characterizing the differences between tensile and compressive force propagation in the fiber network, we show how inter-dipole interaction depends on the dipoles’ mutual separation and orientation. The resulting elastic interaction energy may mediate a force between multiple distant dipoles, leading to their self-organization into ordered configurations. In conclusion, this provides a potential pathway for active mechanical force-driven structural order in elastic biopolymer networks.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Machine learning to accelerate screening for Marcus reorganization energies

Understanding and predicting the charge transport properties of π-conjugated materials is an important challenge for designing new organic electronic devices, such as solar cells, plastic transistors, light-emitting devices, and chemical sensors. A key component of the hopping mechanism of charge transfer in these materials is the Marcus reorganization energy which serves as an activation barrier to hole or electron transfer. While modern density functional methods have proven to accurately predict trends in intramolecular reorganization energy, such calculations are computationally expensive. In this work, we outline active machine learning methods to predict computed intramolecular reorganization energies of a wide range of polythiophenes and their use toward screening new compounds with low internal reorganization energies. Our models have an overall root mean square error (RMSE) of ±0.113 eV, but a much smaller RMSE of only ±0.036 eV on the new screening set. Since the larger error derives from high-reorganization energy compounds, the new method is highly effective to screen for compounds with potentially efficient charge transport parameters.

14 SOLAR ENERGY↗
DOE OSTI2021

Ion-dependent protein–surface interactions from intrinsic solvent response

Significance Hard–soft interfaces between inorganic surfaces and biomolecules promote self-assembly processes with broad implications in biogeochemistry, energy sciences, nanomedicine, and origins of life. Yet, detailed molecular-scale understanding of inorganic–biomolecule interactions and their dependence on solution conditions is missing. We present a theory for the initial stages of inorganic–biomolecule assembly based on the far-field response of water, using experimentally characterized interactions between muscovite surfaces and mica-binding proteins as model systems. Our work connects molecular details of the solution to assembly outcomes and suggests the initial driving forces for assembly are dominated by long-range, ion-specific interactions. The connections made between interfacial structure and long-range surface–biomolecule interactions provide insights toward a predictive understanding of biomolecular self-assembly on mineral surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Unusual magnetotransport in twisted bilayer graphene from strain-induced open Fermi surfaces

Anisotropic hopping in a toy Hofstadter model was recently invoked to explain a rich and surprising Landau spectrum measured in twisted bilayer graphene away from the magic angle. Suspecting that such anisotropy could arise from unintended uniaxial strain, we extend the Bistritzer–MacDonald model to include uniaxial heterostrain and present a detailed analysis of its impact on band structure and magnetotransport. We find that such strain strongly influences band structure, shifting the three otherwise-degenerate van Hove points to different energies. Coupled to a Boltzmann magnetotransport calculation, this reproduces previously unexplained nonsaturating B 2 magnetoresistance over broad ranges of density near filling ν = ± 2 and predicts subtler features that had not been noticed in the experimental data. In contrast to these distinctive signatures in longitudinal resistivity, the Hall coefficient is barely influenced by strain, to the extent that it still shows a single sign change on each side of the charge neutrality point—surprisingly, this sign change no longer occurs at a van Hove point. The theory also predicts a marked rotation of the electrical transport principal axes as a function of filling even for fixed strain and for rigid bands. More careful examination of interaction-induced nematic order versus strain effects in twisted bilayer graphene could thus be in order.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Understanding atom probe’s analytical performance for iron oxides using correlation histograms and ab initio calculations

Abstract Field evaporation from ionic or covalently bonded materials often leads to the emission of molecular ions. The metastability of these molecular ions, particularly under the influence of the intense electrostatic field (10 10 Vm −1 ), makes them prone to dissociation with or without an exchange of energy amongst them. These processes can affect the analytical performance of atom probe tomography (APT). For instance, neutral molecules formed through dissociation may not be detected at all or with a time of flight no longer related to their mass, causing their loss from the analysis. Here, we evaluated the changes in the measured composition of FeO, Fe 2 O 3 and Fe 3 O 4 across a wide range of analysis conditions. Possible dissociation reactions are predicted by density-functional theory calculations considering the spin states of the molecules. The energetically favoured reactions are traced on to the multi-hit ion correlation histograms, to confirm their existence within experiments, using an automated Python-based routine. The detected reactions are carefully analyzed to reflect upon the influence of these neutrals from dissociation reactions on the performance of APT for analysing iron oxides.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Proton-proton interactions and onset of deconfinement

The experiment of the NA61/SHINE Collaboration at the CERN SPS is performing a unique study of the phase diagram of strongly interacting matter by varying collision energy and nuclear mass number of colliding nuclei. In central Pb + Pb collisions, the experiment of the NA49 Collaboration found structures in the energy dependence of several observables in the energy range of the CERN SPS that had been predicted for the transition to a deconfined phase. New measurements of the NA61/SHINE Collaboration find intriguing similarities in $p + p$ interactions for which no deconfinement transition is expected at the energies of the SPS. Possible implications will be discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Efficient emulation of relativistic heavy ion collisions with transfer learning

Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different collision systems and methodically account for experimental and theoretical uncertainties. Such studies require a vast number of costly numerical simulations. While computationally inexpensive surrogate models (“emulators”) can be used to efficiently approximate the predictions of heavy ion simulations across a broad range of model parameters, training a reliable emulator remains a computationally expensive task. We use transfer learning to map the parameter dependencies of one model emulator onto another, leveraging similarities between different simulations of heavy ion collisions. By limiting the need for large numbers of simulations to only one of the emulators, this technique reduces the numerical cost of comprehensive uncertainty quantification when studying multiple collision systems and exploring different models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Laser-driven ion and electron acceleration from near-critical density gas targets: Towards high-repetition rate operation in the 1 PW, sub-100 fs laser interaction regime

Ion acceleration from gaseous targets driven by relativistic-intensity lasers was demonstrated as early as the late 1990s, yet most of the experiments conducted to date have involved picosecond-duration, Nd:glass lasers operating at low repetition rate. Here, we present measurements on the interaction of ultraintense ( ≈ 10 20 W cm − 2 , 1 PW ) , ultrashort ( ≈ 70 fs ) Ti:Sa laser pulses with near-critical ( ≈ 10 20 cm − 3 ) helium gas jets, a debris-free targetry with the potential for future compatibility with high ( ≈ 1 Hz ) repetition rate operation. We provide evidence of α particles being forward accelerated up to ≈ 2.7 − MeV energy with a total flux of ≈ 10 11 sr − 1 as integrated over > 0.1 − MeV energies and detected within a 0.5 − mrad solid angle. We also report on on-axis emission of relativistic electrons with an exponentially decaying spectrum characterized by a ≈ 10 − MeV slope, i.e., five times larger than the standard ponderomotive scaling. The total charge of these electrons with energy above 2 MeV is estimated to be of ≈ 1 nC , corresponding to ≈ 0.1 % of the laser drive energy. In addition, we observe the formation of a plasma channel, extending longitudinally across the gas density maximum and expanding radially with time. These results are well captured by large-scale particle-in-cell simulations, which reveal that the detected fast ions most likely originate from reflection off the rapidly expanding channel walls. The latter process is predicted to yield ion energies in the MeV range, which compare well with the measurements. Finally, direct laser acceleration is shown to be the dominant mechanism behind the observed electron energization. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Low-Frequency Stability Analysis of Inverter-Based Islanded Multiple-Bus AC Microgrids Based on Terminal Characteristics

For system planning of three-phase inverter-based islanded ac microgrids, the low frequency instability issue caused by interactions of inverter droop controllers is a major concern. When internal control information of procured commercial inverters is unknown, impedance-based small-signal stability criteria facilitate prediction of resonances in medium and high frequency ranges, but they usually assume the grid fundamental frequency as constant and thus they are incapable of analyzing the low-frequency oscillation of the fundamental frequency in islanded microgrids. Aiming at solving this issue, this paper proposes two stability analysis methods based on terminal characteristics of inverters and passive connection network including the dynamics of the fundamental frequency for analysis of low-frequency stability in islanded multiple-bus microgrids. Based on the Component Connection Method (CCM) to systematically separate inverters from the passive connection network, a general approach is developed to model the microgrid as a multiple-input-multiple-output (MIMO) negative feedback system in the common system d-q reference frame. By applying the generalized Nyquist stability criterion (GNC) to the return-ratio and return-difference matrices of the MIMO system model, the low-frequency stability related to the fundamental frequency can be analyzed using the measured terminal characteristics of inverters. Finally, analysis and simulation of a 37-bus microgrid verify the effectiveness of the proposed stability analysis methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

PWM for Simultaneous Minimization of Switching Losses and CM Voltage in Current Source Inverters

This article proposes a new pulsewidth modulation (PWM) scheme that reduces common-mode (CM) voltage and switching losses simultaneously in CSI. The proposed PWM [i.e., reduced voltages PWM (RVM)] reduces CM voltages and switching losses by selecting the optimal zero current vector and PWM sequence based on the CSI output voltage conditions. This article also proves the proposed RVM achieves both objectives (i.e., reduced CM voltage and switching losses) simultaneously for all CSI operating conditions without compromising the other objective. The proposed RVM inherently achieves zero-voltage switching (ZVS) conditions during the zero current vector transitions, further reducing the switching losses. Experimental results demonstrate significant reductions in CM voltage, reaching up to approximately 95%, accompanied by an increase of up to about 5.5% in CSI efficiency across a wide operating range compared to alternative PWM methods, confirming the predicted advantages of the proposed RVM.

42 ENGINEERING↗
DOE OSTI2023

Representations of Materials for Machine Learning

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation.

36 MATERIALS SCIENCE↗