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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Ultranarrow electroluminescence from magnetic excitons in the van der Waals antiferromagnetic semiconductor NiPS 3

Electrically driven light emission from two-dimensional (2D) semiconducting materials has enabled numerous optoelectronic technologies, including light-emitting diodes, solid-state lasers, and single-photon sources for quantum communication. Here we report ultranarrow electroluminescence from the magnetic excitonic state of the van der Waals antiferromagnetic semiconductor NiPS 3 . This electroluminescence is enabled by the fabrication of gate-tunable NiPS 3 devices that remain electrically conductive below the antiferromagnetic ordering temperature of 155 K, ultimately allowing field-effect mobilities of 1.3 cm 2 V –1 s –1 and 4.5 cm 2 V –1 s –1 to be directly measured at room temperature and 7 K, respectively. By applying a high-frequency square wave voltage to the gate electrode of the resulting field-effect transistors, electroluminescence is capacitively induced from the magnetic excitons of NiPS 3 . Due to the coupling of these excitons with the underlying NiPS 3 antiferromagnetic order, the electroluminescence has an ultranarrow linewidth of 1 meV and a high degree of linear polarization (ρ = 0.78). In addition to facilitating fundamental studies of the coupling between spin states and excitons in van der Waals magnetic semiconductors, this work will accelerate the development of emerging 2D opto-spintronic applications.

two-dimensional materials↗
DOE OSTI2025

Beam Non-Uniformity Characterization at the SpinQuest and DarkQuest Experiments

The SpinQuest experiment, including upgrades to SpinQuest designed to increase sensitivity to dark sector searches (commonly known as DarkQuest), utilizes the high-intensity 120 GeV proton beam delivered by the Fermilab Accelerator Complex to probe the inner structure of nucleons and search for new physics beyond the Standard Model. The SpinQuest beam is extracted from the Main Injector synchrotron at Fermilab in what is known as a slow spill . The slow spill involves a complex non-linear half-integer extraction method, which results in non-uniform beam behavior. SpinQuest observes spikes of very high intensity beam which can saturate detectors and reduce trigger efficiency, significantly impacting the experiment's sensitivity. In this project we address this challenge by developing an analysis framework to characterize the beam delivered to SpinQuest. By discovering trends within each spill and by comparing thousands of spills, we can better inform the Accelerator Division and improve the slow spill extraction. We have also begun a collaboration with the Accelerator Division in order to simulate the slow spill and improve the magnet ramp process controls which will improve the uniformity of the beam. These improvements will directly enhance the physics reach of SpinQuest/DarkQuest, increasing their sensitivity to key measurements such as the Sivers function and searches for new physics.

Dolen, James William [Purdue U., Calumet] (ORCID:0↗
DOE OSTI2024

Efficient Parameterization of Density Functional Tight-Binding for 5 f -Elements: A Th–O Case Study

Density functional tight binding (DFTB) models for f-element species are challenging to parametrize owing to the large number of adjustable parameters. The explicit optimization of the terms entering the semiempirical DFTB Hamiltonian related to f orbitals is crucial to generating a reliable parametrization for f-block elements, because they play import roles in bonding interactions. However, since the number of parameters grows quadratically with the number of orbitals, the computational cost for parameter optimization is much more expensive for the f-elements than for the main group elements. In this work we present a set of efficient approaches for mitigating the hurdle imposed by the large size of the parameter space. A novel group-by-orbital correction functions for two-center bond integrals was developed. With this approach the number of parameters is reduced, and it grows linearly with the number of elements, maintaining the accuracy and the number of parameters, in the case of f elements, by more than 40%. The parameter optimization step was accelerated by means of the mini-batch BFGS method. This method allows parameter optimizations with much larger training sets than other single batch methods. A stochastic optimizer was employed that helped overcome shallow local minima in the objective function. The proposed algorithm was used to parametrize the DFTB Hamiltonian for the Th–O system, which was subsequently applied to the study of ThO 2 nanoparticles. The training set consisted of 6322 unique structures, which is barely feasible with conventional optimization methods. The optimized parameter set, LANL-ThO, displays good agreement with DFT-calculated properties such as energies, forces, and structures for both clusters and bulk ThO 2 . Benefiting from the fewer number of parameters and lower computational costs for objective function evaluations, this new approach shows its potential applications in DFTB parametrization for elements with high angular momentum, which present a challenge to conventional methods.

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

Demonstration of improvement of energy conversion rate from kJ PW laser to protons with electron confinement

Kilojoule-class relativistic intensity lasers can produce energetic protons with high energy conversion efficiencies in the interaction with a thin foil target. Using the national ignition facility advanced radiographic capability (NIF-ARC) laser, we demonstrated an enhancement of energy conversion from laser to protons by the effective confinement of fast electrons in the laser spot by random kicks from the self-excited field. The number of fast electrons was increased by 4.6 times by increasing the ratio of the laser spot size to the foil thickness to strengthen the confinement effect. The energy conversion efficiency from laser to protons increases approximately linearly with the enhancement of the number of fast electrons. The conversion rate for protons with energies above 8 MeV was 1.8 %. The result leads to high efficiency proton acceleration which is beneficial in applications, such as proton radiography and plasma heating in laser fusion.

Physics↗
DOE OSTI2024

Performance and Durability of Heavy-Duty Fuel Cell Systems with an Advanced Ordered Intermetallic ORR Alloy Catalyst and Novel Support

Ordered PtCo intermetallic (OIM) catalyst (L1 0 -PtCo/C) is a promising candidate as the oxygen reduction reaction (ORR) catalyst in hybrid fuel cell systems (FCS) for class-8 heavy duty (HD) trucks. Compared to a baseline annealed Pt on high surface area carbon (a-Pt/HSC) catalyst, its mass activity (MA) is 71% higher initially and 144% higher after 90,000 potential cycles in an accelerated stress test (AST). Analysis of the AST data indicates that the ORR kinetic constants do not change with aging and the degradation in the OIM catalyst activity is linearly proportional to the loss in the electrochemically active surface area (ECSA). Several operational strategies are investigated to mitigate catalyst degradation and achieve 25,000-h electrode lifetime and 2.5 kW g −1 Pt utilization on a HD truck duty cycle including load sharing with the hybrid battery, regulating the radiator fan power to maintain the coolant temperature close to 60 °C, clipping the maximum cell voltage below 850 mV, limiting the ECSA loss to 55%, and oversizing the active area of the membrane electrode assemblies by 20%. Drive cycle simulations indicate that the lifetime average voltage degradation rate is about 1.8 μV h −1 and the integrated stack and FCS drive cycle efficiencies decrease by 3.5 to 3.9%.

25 ENERGY STORAGE↗
DOE OSTI2026

Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Water Temperature, Prey Concentration and Salmonid Density Influence Daily Growth of Wild Juvenile Salmonids in Tributaries of the Upper Salmon River, Idaho ( USA )

ABSTRACT Theory, experiments and field studies indicate that the somatic growth rate of freshwater consumers is shaped by the individual, additive and multiplicative effects of multiple factors, including consumer size and condition, temperature, prey resources and biotic interactions. While our understanding of how these factors affect wild populations of freshwater consumers is improving, the topic remains poorly studied, especially with respect to mobile species. Here, we report on an 8‐year, seven‐stream ( n = 49 stream‐year combinations) observational study examining the individual and interactive effects of invertebrate prey concentration (F, mg/m 3 ), mean daily water temperature (T, °C) and juvenile Chinook salmon ( Oncorhynchus tshawytscha ) density (D, fish/100 m 2 ) on summer daily growth rates (%/d) of mobile, anadromous, juvenile Chinook salmon (age‐0+, n = 382) and sub‐yearling (age‐0+, n = 61) and yearling (age‐1+) steelhead trout ( O. mykiss , n = 70) rearing in cold (mean daily summer: 12.1°C, range: 4.2°C–16.7°C) mountain tributaries of the Salmon River basin in central Idaho (USA). AIC c model selection indicated that daily juvenile salmonid growth positively correlated with water temperature, prey biomass concentration, local juvenile Chinook density and the interaction between water temperature and food but with species and age‐specific differences. Water temperature was a covariate in all top‐ranked models, with daily growth (%/day) rate increasing (0.05%–0.23%/d) linearly with mean daily summer water temperature. In addition to a direct positive relationship with daily growth rate, there was evidence that prey concentration positively interacted with water temperature to accelerate daily growth (F × T). The positive relationship between juvenile salmonid daily growth rate and juvenile Chinook density is difficult to explain and could result from confounding factors. The individual success observed in these streams may contribute to population‐level benefits for the focal consumers, as prey‐rich, warm summers may result in larger individuals with higher energy reserves at the end of the summer/autumn growing season, contributing to improved overwinter survival. Our results, taken in combination with evidence from models, experiments and observational studies, have climate change implications. Current and predicted increases in water temperature will necessitate higher rates of prey consumption by aquatic ectothermic consumers to offset accelerated metabolic demands. Thus, to improve the resilience of mobile freshwater consumers in a warming climate, we suggest that natural resource managers not only consider physical and chemical habitat conditions but also biotic conditions, including the spatiotemporal quantity and quality of prey resources.

Kiffney, Peter. M. [Fish Ecology, Northwest Fisher↗
DOE OSTI2025

Kinetic Simulations of the Kruskal–Schwarzschild Instability in Accelerating Striped Outflows: Dynamics and Energy Dissipation

Astrophysical relativistic outflows are launched as Poynting-flux dominated, yet the mechanism governing efficient magnetic dissipation, which powers the observed emission, is still poorly understood. We study magnetic energy dissipation in relativistic "striped" jets, which host current sheets separating magnetically dominated regions with opposite field polarity. The effective gravity force g in the rest frame of accelerating jets drives the Kruskal–Schwarzschild instability (KSI), a magnetic analog of the Rayleigh–Taylor instability. By means of 2D and 3D particle-in-cell simulations, we study the linear and nonlinear evolution of the KSI. The linear stage is well described by linear stability analysis. The nonlinear stages of the KSI generate thin (skin-depth-thick) current layers, with length comparable to the dominant KSI wavelength. There, the relativistic drift-kink mode and the tearing mode drive efficient magnetic dissipation. The dissipation rate can be cast as an increase in the effective width Δ eff of the dissipative region, which follows dΔ eff /dt ≃ 0.05 $\sqrt{Δ_{eff} g}$. Our results have important implications for the location of the dissipation region in gamma-ray burst and active galactic nuclei jets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine Learning-Driven Conservative-to-Primitive Conversion in Hybrid Piecewise Polytropic and Tabulated Equations of State

We present a novel machine learning (ML)-based method to accelerate conservative-to-primitive inversion, focusing on hybrid piecewise polytropic and tabulated equations of state. Traditional root-finding techniques are computationally expensive, particularly for large-scale relativistic hydrodynamics simulations. To address this, we employ feedforward neural networks (NNC2PS and NNC2PL), trained in PyTorch (2.0+) and optimized for GPU inference using NVIDIA TensorRT (8.4.1), achieving significant speedups with minimal accuracy loss. The NNC2PS model achieves 𝐿 1 and 𝐿 ∞ errors of 4.54 × 10 −7 and 3.44 × 10−6, respectively, while the NNC2PL model exhibits even lower error values. TensorRT optimization with mixed-precision deployment substantially accelerates performance compared to traditional root-finding methods. Specifically, the mixed-precision TensorRT engine for NNC2PS achieves inference speeds approximately 400 times faster than a traditional single-threaded CPU implementation for a dataset size of 1,000,000 points. Ideal parallelization across an entire compute node in the Delta supercomputer (dual AMD 64-core 2.45 GHz Milan processors and 8 NVIDIA A100 GPUs with 40 GB HBM2 RAM and NVLink) predicts a 25-fold speedup for TensorRT over an optimally parallelized numerical method when processing 8 million data points. Moreover, the ML method exhibits sub-linear scaling with increasing dataset sizes. We release the scientific software developed, enabling further validation and extension of our findings. By exploiting the underlying symmetries within the equation of state, these findings highlight the potential of ML, combined with GPU optimization and model quantization, to accelerate conservative-to-primitive inversion in relativistic hydrodynamics simulations.

conservative-to-primitive conversion↗
DOE OSTI2024

Guided Mode Evolution and Ionization Injection in Meter-Scale Multi-GeV Laser Wakefield Accelerators

Here, we show that multi-GeV laser wakefield electron accelerators in meter-scale, low density hydrodynamic plasma waveguides operate in a new nonlinear propagation regime dominated by sustained beating of lowest order modes of the ponderomotively modified channel; this occurs whether or not the injected pulse is linearly matched to the guide. For a continuously doped gas jet, this emergent mode beating effect leads to axially modulated enhancement of ionization injection and a multi-GeV energy spectrum of multiple quasimonoenergetic peaks; the same process in a locally doped jet produces single multi-GeV peaks with <10% energy spread. A three-stage model of drive laser pulse evolution and ionization injection characterizes the beating effect and explains our experimental results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Photon phase-space dynamics in a plasma wakefield accelerator

Frequency up-shifting of laser light in a beam-driven plasma wakefield has the potential to provide high-intensity sources of short wavelength radiation. Simulations have demonstrated that a laser pulse can undergo large frequency shifts, limited only by the drive beam energy, when the plasma density is tailored to match the accelerating phase of the wake to the group velocity of the pulse. Here, we study the dynamical evolution of photons in the 1D1P phase-space vicinity of the plasma wake-phase matching condition. Numerical calculations using a photon kinetic model are validated by direct comparison with 1D and quasi-3D particle-in-cell simulations. These calculations form the basis of a linear theory of the photon dynamics which reveals several important results, including scalings for the properties of the witness pulse and a self-similar solution for the photon phase-space dynamics. One prediction of the analytic theory is that the pulse can be compressed indefinitely, though the amount of compression would ultimately be limited by practical constraints. These results suggest that photon acceleration can provide a novel source of sub-femtosecond, short wavelength radiation.

XUV generation↗
DOE OSTI2025

Artificial intelligence time series forecasting for feed-forward laser stabilization

Laser plasma accelerators, typically operating at 1–10 Hz repetition rates, have the ability to produce high-quality electron beams in compact, all-optical-driven configurations, with the electron beams uniquely suited for a wide variety of accelerator-based applications. However, fluctuations and drifts in the laser delivery to the meter-scaled and below plasma target (the electron beam source) will translate into electron beam source variations that can limit their utility for demanding applications like light sources or linear colliders. Commercially available active feedback laser stabilization systems are intrinsically bandwidth limited due to their integration with multi-inch corrective mirror mounts which minimizes their effectiveness. In this manuscript, we present a Neural Network time series forecaster that can predict laser position fluctuations of the laser delivery to the final target well ahead of a future laser shot. The Root-Mean-Square-Error (RMSE) of the prediction accuracy was < 2 μ m for a 1 / e 2 beam radius of 34 μ m . Our feed-forward approach serves as a first-step in circumventing the bandwidth limitations imposed by the currently available stabilization systems since it allows for mirrors to be moved into position ahead of time to offset the predicted future position drift. This will help advance laser plasma accelerator research by providing greater robustness and stability needed for its applications.

Berger, Curtis↗
DOE OSTI2024

A Broadband Mechanically Tuned Superconducting Cavity Design Suitable for the Fermilab Main Injector

Radio Frequency superconductivity has been a mainstay of accelerator science for decades. However, its benefits have yet to be applied to proton synchrotrons with demanding tuning requirements. For example, the Main Injector (MI), Fermilab's high-energy proton synchrotron, currently utilizes 20+ ferrite-loaded cavities for a targeted 1.2 s acceleration cycle. Harnessing the extremely high gradients associated with superconductivity, the required number of cavities could be reduced by an order of magnitude, dramatically lowering operational power requirements even with cryogenic considerations. Additionally, the current plans for the Fermilab accelerator complex evolution initiative involve almost doubling the number of cavities in MI if the same designs are to be used, further highlighting the potential benefits of superconductivity. These advantages are attractive, but to date, no tunable superconducting cavity suitable for MI has been proposed due to the incompatibility of conventional broadband tuning methods with superconductivity. Here, we present a tunable superconducting cavity concept capable of record-breaking performance. Tuning will be accomplished by using high-speed linear actuators to vary the insertion depth of metallic plungers into the cavity volume. This tuning concept is theoretically viable with currently available technology and will be fully compatible with a superconducting cavity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A Mechanically Tuned Superconducting Main Injector Cavity

Radio Frequency (RF) superconductivity has been a mainstay of accelerator science for decades. However, its benefits have yet to be applied to proton synchrotrons with demanding tuning requirements. For example, the Main Injector (MI), Fermilab's high-energy proton synchrotron, currently utilizes 20+ ferrite-loaded cavities for a targeted 1.2 s acceleration cycle. Harnessing the extremely high gradients associated with superconductivity, the required number of cavities could be reduced by an order of magnitude, dramatically lowering operational power requirements even with cryogenic considerations. Additionally, the current plans for the Fermilab Accelerator Complex Evolution (ACE) initiative involve almost doubling the number of cavities in MI if the same designs are to be used, further highlighting the potential benefits of superconductivity. These advantages are attractive, but to date, no tunable superconducting cavity suitable for MI has been proposed due to the incompatibility of conventional broadband tuning methods with superconductivity. Here, we present a tunable superconducting cavity concept capable of record-breaking performance. Tuning will be accomplished by using high-speed linear actuators to vary the insertion depth of metallic plungers into the cavity volume. This tuning concept is theoretically viable with currently available technology and will be fully compatible with a superconducting cavity.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Structure and Flow-Viscosity of Filled-Polymer-Based 3D Printing Ink: Exploration through Coarse-Grained Molecular Dynamics

The addition of nanofiller particles to a polymer matrix has long been known to enhance or modify the composite’s mechanical and rheological properties. However, quantitatively capturing such changes with molecular level simulations remains computationally challenging. Toward that goal, we performed coarse-grained molecular dynamics of a nanocomposite system at a fixed (25 vol %) filler loading under nonspecific, weak polymer–filler interactions representative of a broad class of technologically important materials. We report several interesting results, including: (1) the equilibrium chain-configuration remains Gaussian-like as in an unfilled melt; (2) smaller filler particles display a stronger tendency to cluster; (3) larger fillers act as plasticizers by reducing the entanglement density and accelerating the chain mobility; and (4) fillers enhance the tensile response modulus, with the effect being stronger for larger particles. We also simulate cluster breakup, yielding, and elongational flow under an applied time-linear tensile strain and study the flow viscosity as a function of filler-size and chain-length.

Materials science↗
DOE OSTI2025

Analysis of the linear and nonlinear stability of Alfven eigenmodes and fish-bones in JET DT discharges: mode identification and shear flows generation

The plasma in future nuclear fusion reactors will be heated by neutral beam injectors (NBIs) and high frequency electromagnetic waves as well as fusion born alpha particles. Energetic particles (EPs), with energies up to two orders of magnitude larger than the thermal plasma, can trigger EP driven modes and induce harmful EP losses, reducing the plasma heating efficiency and the economical viability of the reactor. The present study is dedicated to analyze the Alfven Eigenmode (AE) activity in JET D–T discharges, the closest experiment to reactor-like operation performed until now. There, EP driven modes are induced by the combined effect of tangential NBIs and ion cyclotron resonance heating (ICRH) driven EP. Linear and nonlinear simulations are performed with the gyro-fluid FAR3d code to analyze the AE activity observed in the discharge 99896. The linear simulations reproduce the unstable n = 3 to 5 toroidal AEs (TAE) at the inner plasma region observed in the experiment, triggered by highly energetic passing deuterium populations injected by the tangential NBIs, further accelerated by the effect of the ICRH up to 1 MeV. In addition, fish-bones triggered by energetic trapped hydrogen induced by the ICRH are also reproduced. On the other hand, the alpha particles density is too small to destabilize AEs in the experiment. Nonetheless, increasing artificially the alpha density by one order of magnitude, an n = 1 beta induced AE can be destabilized in the inner plasma region. Nonlinear simulations indicate the generation of zonal structures during the AE/fish-bone saturation phase. TAE and fish-bones causes a rather weak increase of the passing D and trapped H EP (around 2%), respectively. Shear flows and zonal currents are generated during the saturation of TAE and fish-bones. Nonlinear simulations performed for D–T and pure deuterium thermal plasma indicate AE/fish-bone activity is weaker and shear flows are less intense in the pure deuterium case, trends consistent with the experimental observations that also indicates a deterioration of the thermal plasma confinement. Therefore, both numerical studies and experimental evidence indicate the generation of shear flows by AE/fish-bones could be connected with an improvement of the thermal plasma confinement.

AE↗
DOE OSTI2025

Mixed gain detector configurations for time-resolved X-ray solution scattering

X-ray detection at X-ray free-electron lasers is challenging in part due to the XFEL's extremely short and intense X-ray pulses. Experimental measurements are further complicated by the large fluctuations inherent to the self-amplified spontaneous emission process producing the X-rays. At the Linac Coherent Light Source the ePix10ka2M detector offers multiple gain modes, and auto-ranging between these, to increase the dynamic range while retaining low noise. For diffuse scattering techniques, such as time-resolved X-ray solution scattering, where the shape of the scattering pattern largely does not change between exposures, a fixed mix of different gain modes offers many of the same advantages as auto-ranging. We find that configuring individual ASICs in separate gain modes does not impact the intensity linearity of the gain response and has a limited effect on the effective dynamic range in regions with different gain mode settings while avoiding the complexities of auto-ranging. Small (<5%) non-linear gain contributions arise when pixels on the same ASIC are configured in different gain modes. We present a configuration scheme that is designed to select the optimal mixed gain configuration to minimize effects of saturation in the high-/medium-gain region, while maximizing the number of pixels with higher gain to improve the signal-to-noise ratio.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗