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DOE OSTI2026

Studies of laser stimulated photodetachment from nanoparticles for particle charge measurements

Determining nanoparticle charge is more challenging than that for microparticles due to change in the particle size during the synthesis substantial plasma property variations, and difficulties in visualizing individual particles, rendering conventional microparticle charge diagnostics ineffective in dusty plasma. In this work, we utilized laser-stimulated photodetachment (LSPD) to deduce the mean charge of nanoparticles. Nanoparticles were grown in an Ar/C 2 H 2 mixture using a capacitively coupled RF discharge and the LSPD induced changes in the electron current monitored by a cylindrical Langmuir probe. LSPD signals were obtained and analyzed across different dust growth phases. The prolonged decay of electron current pulses was attributed to the presence of residual negative ions, caused by the effective electrostatic trapping of these ions and the potential post—LSPD re-formation of new ones. The particle charge was estimated by combining the laser-stimulated photodetachment signal from the probe with the dust density obtained from laser-light extinction using the measured nanoparticle size distribution. For a nanoparticles size range of approximately 100–250 nm and mean diameter of $d$ p ∼154.37 nm, the effective mean charge was estimated to be $\langle$$Q$$\rangle$ d $≈$ 37 elementary charge units. The measured charge values are lower than those predicted by orbital motion limited theory, which may be attributed to significant electron depletion in the nanodusty plasma. LSPD results in Ar/C 2 H 2 nano-dusty plasma confirm the applicability of this method for estimating individual nanoparticle charges. However, it has also been demonstrated that electron detachment from residual background negative ions can influence the detachment current decay and must be carefully considered.

dust particle charge↗

DOE OSTI2025

Thermal Resilience of Buildings and Communities: A Multistakeholder Review of Metrics and Approaches

Increasing temperature-related hazards require a collective effort to assess and enhance the thermal resilience of buildings and communities to protect occupants’ safety and minimize property or infrastructure damage. However, limited coordination across stakeholders and lack of standardized procedures for resilience assessment undermine the effectiveness of extreme temperature mitigation and adaptation strategies across the building life cycle. This review examines the current literature on resilience metrics to address thermal stress and risk due to extreme indoor environments. Stakeholders of thermal resilience include architects and engineers, occupants, property owners, real estate developers, urban planners, and policymakers. Additionally, motivations for measuring thermal resilience are emphasized, such as safeguarding occupant health and survivability, protecting property, and ensuring business continuity during extreme weather events. This review provides actionable insights and identifies future research needs for enhancing resilience through tailored metrics for stakeholders during the planning, design, construction, operation, and retrofitting phases of buildings and communities.

building life cycle↗

DOE OSTI2025

Crystallographic stability of the BCC phase during quenching of metastable beta titanium alloy Ti-5553 and comparison of structural criteria for the predictive capability of α” martensite

This work evaluates the compositional standard currently developed for Ti-5553 powder and explores the metastable β region for sensitivity to martensitic formation during rapid quenching from the melt. Ti-5553 is a beta-stabilized titanium alloy that is increasingly being used in additive manufacturing applications. A series of alloys within the Ti-5553 compositional space was processed using two-piston spat quenching to perform rapid solidification and quenching for each of these alloy compositions. Typical empirical models such as molybdenum equivalency are not able to fully separate the retained BCC and martensitic compositions. Further, the use of thermodynamic data to estimate the transformation energy needed to form martensite can differentiate the alloys and provide a metric to further develop compositional limits for metastable beta titanium alloy development.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Constraints on Covariant Dark-Matter–Nucleon Effective Field Theory Interactions from the First Science Run of the LUX-ZEPLIN Experiment

The LUX-ZEPLIN (LZ) experiment is a dual-phase xenon time project chamber operating in the Sanford Underground Research Facility in South Dakota, USA. We report on the results of a relativistic extension to the nonrelativistic effective field theory (NREFT) from a 5.5 t fiducial mass and 60 live days of exposure. We present constraints on couplings from covariant interactions arising from the coupling of vector, axial currents, and electric dipole moments of the nucleon to the magnetic and electric dipole moments of the weakly interacting massive particle which cannot be described by recasting previous results described by an NREFT. Using a profile-likelihood ratio analysis, in an energy region between 0 keV nr to 270 keV nr , we report 90% confidence level exclusion limits on the coupling strength of five interactions in both the isoscalar and isovector bases. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Fast and Simple Ag/Cu Ion Exchange on Cu Foil for Anode-Free Lithium-Metal Batteries

Lithium-metal batteries with zero excess lithium on the anode side paired with a fully lithiated cathode are regarded as a form of the highest energy-density configuration. Unfortunately, the continuous lithium loss over cycling from a limited amount of lithium reservoir significantly degrades the overall cell performance in the anode-free system. To mitigate the deterioration, modifying the current collector for enhanced lithium cycling is an indispensable route. Here, we apply a Ag/Cu ion exchange to precipitate micro-sized Ag particles on the Cu current collector to enhance the lithium reversibility via (de)alloying process. Further, we show a smoother morphology of lithium upon alloying, which leads to a lowered nucleation potential as well as increased average Coulombic efficiency in Li||Cu cells regardless of electrolyte formulation. The preferred lithium adsorption on Ag and AgLi over Cu is demonstrated with density functional theory calculations, which supports that Li is forming a gamma-phase alloy in the last stage rather than being deposited beneath the alloy. Lastly, this simple Cu foil modification enhances lithium reversibility and reduces its nucleation barrier, thus mitigating the capacity fade of a Cu||LiFePO 4 with reduced polarization.

25 ENERGY STORAGE↗

DOE OSTI2025

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.

Brozak, Samantha Jean [Sandia National Laboratorie↗

DOE OSTI2022

The Donnan potential revealed

Selective transport of solutes across a membrane is critical for many biological, water treatment and energy conversion and storage systems. When a charged membrane is equilibrated with an electrolyte, an unequal distribution of ions arises between phases, generating the so-called Donnan electrical potential at the solution/membrane interface. The Donnan potential results in the partial exclusion of co-ion, providing the basis of permselectivity. Although there are well-established ways to indirectly estimate the Donnan potential, it has been widely reported that it cannot be measured directly. Here we report the first direct measurement of the Donnan potential of an ion exchange membrane equilibrated with salt solutions. Our results highlight the dependence of the Donnan potential on external salt concentration and counter-ion valence, and show a reasonable agreement with current theoretical models of IEMs, which incorporate ion activity coefficients. By directly measuring the Donnan potential, we eliminate ambiguities that arise from limitations inherent in current models.

25 ENERGY STORAGE↗

DOE OSTI2023

Cross Sections for Coherent Elastic and Inelastic Neutrino-Nucleus Scattering

The prospects of extracting new physics signals in coherent elastic neutrino–nucleus scattering (CEνNS) processes are limited by the precision with which the underlying nuclear structure physics, embedded in the weak nuclear form factor, is known. We present calculations of charge and weak nuclear form factors and CEνNS cross sections on 12C, 16O, 40Ar, 56Fe and 208Pb nuclei. We obtain the proton and neutron densities, and charge and weak form factors by solving Hartree–Fock (HF) equations with a Skyrme (SkE2) nuclear potential. We validate our approach by comparing 208Pb and 40Ar charge form factor predictions with available elastic electron scattering data. Since CEνNS experiments at stopped-pion sources are also well suited to measure inelastic charged–current and neutral–current neutrino–nucleus cross sections, we also present calculations for these processes, incorporating a continuum Random Phase Approximation (CRPA) description on top of the HF–SkE2 picture of the nucleus. Providing both coherent as well as inelastic cross sections in a consistent framework, we aim at obtaining a reliable and detailed comparison of the strength of these processes in the energy region below 100 MeV. Furthermore, we attempt to gauge the level of theoretical uncertainty pertaining to the description of the 40Ar form factor and CEνNS cross sections by comparing relative differences between recent microscopic nuclear theory and widely-used phenomenological form factor predictions. Future precision measurements of CEνNS will potentially help in constraining these nuclear structure details that will in turn improve prospects of extracting new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Dimensional crossover in spin Hall oscillators

Auto-oscillations of magnetization driven by direct spin current have been previously observed in multiple quasi-zero-dimensional (0D) ferromagnetic systems such as nanomagnets and nanocontacts. Recently, it was shown that pure spin Hall current can excite coherent auto-oscillatory dynamics in quasi-one-dimensional (1D) ferromagnetic nanowires but not in quasi-two-dimensional (2D) ferromagnetic films. Here we study the 1D to 2D dimensional crossover of current-driven magnetization dynamics in wire-based Pt/Ni 80 Fe 20 bilayer spin Hall oscillators via varying the wire width. We find that increasing the wire width results in an increase of the number of excited auto-oscillatory modes accompanied by a decrease of the amplitude and coherence of each mode. We also observe a crossover from a hard to a soft onset of the auto-oscillations with increasing wire width. The amplitude of auto-oscillations rapidly decreases with increasing temperature suggesting that interactions of the phase-coherent auto-oscillatory modes with incoherent thermal magnons play an important role in suppression of the auto-oscillatory dynamics. Our measurements in this work set the upper limit on the dimensions of an individual spin Hall oscillator and elucidate the mechanisms leading to suppression of coherent auto-oscillations with increasing oscillator size.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?

Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li 2 , Li 3 , and Li 4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Analysis of ultrafast magnetization switching dynamics in exchange-coupled ferromagnet–ferrimagnet heterostructures

Magnetization switching in ferromagnets has so far been limited to the current-induced spin–orbit–torque effects. Recent observation of helicity-independent all-optical magnetization switching (HI-AOS) in an exchange-coupled ferromagnet–ferrimagnet (FM-FEM) heterostructures expanded the range and applicability of such ultrafast heat-driven magnetization switching. Here we report the element-resolved HI-AOS dynamics of such an exchange-coupled system, using a modified microscopic three-temperature model. We have studied the effect of (i) the Curie temperature of the FM, (ii) FEM composition, (iii) the long-range Ruderman–Kittel–Kasuya–Yosida (RKKY) exchange-coupling strength, and (iv) the absorbed optical energy on the element-specific time-resolved magnetization dynamics. The phase-space of magnetization illustrates how the RKKY coupling strength and the absorbed optical energy influence the switching time. Our analysis demonstrates that the threshold switching energy depends on the composition of the FEM and the switching time depends on the Curie temperature of the FM as well as RKKY coupling strength. Importantly, this simulation anticipates new insights into developing faster and more energy-efficient spintronics devices.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Development of an Energy-Efficient and High-Productivity Ammonia Recovery and Removal Process Using Resin-Wafer Electrodeionization

As efforts to develop various energy resources, ammonia energy is emerging as a promising carbon-free fuel alternative. Recovering high-concentration ammonia and ammonium from wastewater potentially offers significant environmental and economic benefits. However, research on recovery technologies for industries such as semiconductors remains limited. This study develops an efficient, energy-saving ammonia recovery technology using Resin Wafer Electrodeionization (RW-EDI), specifically for the semiconductor industry. RW-EDI shows promise for recovering ammonia from high-concentration wastewater. By optimizing parameters, such as voltage and initial concentration, a balance between productivity and energy consumption is achieved. Results indicate that ammonium and fluoride ion transport kinetics are similar, with minimal competition and selectivity values between 0.95 and 1.08. As the initial ammonia/ammonium concentration increases from 500 to 8000 ppm, reaction rate constants and the overall mass transfer coefficient decrease. Increasing the voltage can enhance mass transfer and eliminate barriers. Ion transport primarily occurs in the RW solid phase, accounting for 93.89% of the total current. Additionally, RW-EDI shows superior specific energy consumption within a concentration range of 500 ppm to 8000 ppm, outperforming technologies like electrodialysis by reducing specific energy consumption from 8−15 kWh/kg-NH 4 + to 1.2−2.5 kWh/kg-NH 4 + . This study highlights RW-EDI’s potential for ammonia recovery, providing valuable insights for future applications in wastewater treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Shot noise and universal Fano factor as a characterization of strongly correlated metals

Shot noise measures out-of-equilibrium current fluctuations and is a powerful tool to probe the nature of current-carrying excitations in quantum systems. Recent shot-noise measurements in the heavy-fermion strange metal YbRh 2 Si 2 exhibit a strong suppression of the Fano factor ( F )—the ratio of the current noise to the average current in the dc limit. This system is representative of metals in which electron correlations are extremely strong. Here we carry out the first theoretical study on the shot noise of diffusive metals in the regime of strong correlations. A Boltzmann-Langevin equation formulation is constructed in a quasiparticle description in the presence of strong correlations. We find that F = 3 / 4 in such a correlation regime. Thus, we establish the aforementioned Fano factor as universal to Fermi liquids, and we show that the Fano factor suppression observed in experiments on YbRh 2 Si 2 necessitates a loss of the quasiparticles. Our work opens the door to systematic theoretical studies of shot noise as a means of characterizing strongly correlated metallic phases and materials. Published by the American Physical Society 2024

Wang, Yiming (ORCID:0000000277582921)↗

DOE OSTI2021

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Data-driven picosecond X-ray imaging for quantitative plasma-induced shock characterization

Imaging dynamic events, especially shockwave behavior, is key to advancing high-energy-density (HED) research. Recent advances in fourth- and fifth-generation X-ray light sources allow for high-resolution imaging of fast phenomena, but limited beam time necessitates maximizing data acquisition. We present a benchtop-scale pulsed plasma device submerged in liquid heptane, capable of generating dynamic events at rates exceeding 10 Hz, supporting the field’s data-driven goals by producing large, high-quality imaging datasets. Using X-ray phase contrast imaging (XPCI) at the Advanced Photon Source, we imaged weak shockwaves (Mach ~ 1.2) in heptane interacting with plasma-induced cavitation bubbles, causing deviation from Rankine-Hugoniot behavior; to our knowledge, this represents the first direct imaging of such interaction. Our quantitative analysis offers insight into weak shock phenomena and energy-focusing applications in pulsed plasmas. These results highlight the potential for large datasets to advance dynamic HED research at current light source facilities, and have implications for fields such as inertial confinement fusion, plasma-enhanced chemical processing, and biomedical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Soft x-ray high-harmonic generation in an anti-resonant hollow core fiber driven by a 3 μ m ultrafast laser

High-harmonic upconversion driven by a mid-infrared femtosecond laser can generate coherent soft x-ray beams in a tabletop-scale setup. Here, we report on a compact ytterbium-pumped optical parametric chirped pulse amplifier (OPCPA) laser system seeded by an all-fiber front-end and employing periodically poled lithium niobate (PPLN) nonlinear media operated near the pulse fluence limits of current commercially available PPLN crystals. The OPCPA delivers 3 µm wavelength pulses with 775 µJ energy at 1 kHz repetition rate, with transform-limited 120 fs pulse duration, diffraction-limited beam quality, and ultrahigh 0.33% rms energy stability over >18 h. Using this laser, we generate soft x-ray high harmonics (HHG) in argon gas by focusing into a low-loss, high-pressure gas-filled anti-resonant hollow core fiber (ARHCF), generating coherent light at photon energies up to the argon L-edge (250 eV) and carbon K-edge (284 eV), with high beam quality and ∼1% rms energy stability. This work demonstrates soft x-ray HHG in a high-efficiency guided-wave phase matched geometry, overcoming the high losses inherent to mid-IR propagation in unstructured waveguides, or the short interaction lengths of gas cells or jets. The ARHCF can operate in the long term without damage and with the repetition rate, stability, and robustness required for demanding applications in spectromicroscopy and imaging. Finally, we discuss routes for further optimizing the soft x-ray HHG flux by driving He at higher laser intensities using either the signal (1.5 μm) or idler wavelengths (3 μm).

Femtosecond lasers↗

DOE OSTI2023

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

36 MATERIALS SCIENCE↗