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DOE OSTI2026

SQUID Readout of a High-$Q$ Superconducting $LC$ Resonator

We demonstrate the readout of a superconducting $LC$ resonator, with $Q \approx 2 \times 10^{6}$ at approximately 250 kHz, using a dc SQUID followed by a SQUID series-array amplifier readout chain. We find that $Q$ depends on the SQUID flux-bias point, increasing near the shallow-slope point of the SQUID modulation curve and decreasing near the steep-slope point, consistent with the previously observed SQUID damping effects. From this variation, we infer the SQUID effective input impedance. We further infer the effective temperature of the resonator circuit from the resonance peak in the noise spectrum and show that it also depends on the SQUID flux-bias point, suggesting a possible contribution from SQUID back-action noise. This system provides a working prototype for future experiments based on lumped-element resonators with SQUID readout, in particular low-mass axion searches within the DMRadio program.

FOS: Physical sciences↗

DOE OSTI2023

CFD Simulation of the Dosing Behavior within the Atomic Layer Deposition Feeding System

The effective operation of atomic layer deposition (ALD) feeding system is the premise of realizing specific ALD processes. In the present work, a detailed computational fluid dynamics (CFD) model of the feeding system has been developed and validated, which accounts for the roles of ALD valves and manifolds. A numerical simulation of the compressible fluid flow and heat/mass transfer within the feeding system was conducted. The dosing amounts and the spatiotemporal distributions of the precursors can be accurately predicted using the CFD model, as validated by experimental results. Different precursors, operating conditions, and structures of the feeding system were simulated and analyzed to examine the operating flexibility of the feeding system. The simulation results can be adopted as the upstream boundary conditions for simulations of the ALD process in the reaction chamber. The substrate-scale simulation indicates that the effect of the feeding system on the film deposition is highly related to the surface kinetics of ALD. The present work can serve as a guide for the development and optimization of different ALD-based processes via proper operation and even the design of the feeding system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Analysis of Liquids Using the Submerged Plasma for Isotopic Detection and Elemental Resolution (SPIDER)

We developed a sensor called the Submerged Plasma for Isotopic Detection and Elemental Resolution (SPIDER) probe, which uses an atmospheric pressure glow discharge below the surface of liquids to excite species in the liquid. Through emission spectroscopy of molten salts, liquid metals, and heavy water, we demonstrated the SPIDER probe’s high resolution, accuracy, and versatility. We successfully identified trace concentrations of transition and rare-earth metals in molten salts and detected the isotopic shift of the H β → D β emission line. Our analysis revealed unconventional spectral alkali line shapes, indicating two competing excitation modes: film explosion and droplet vaporization. The film explosion mode, characterized by dense plasma, exhibited self-reversal and broadband continuum emission, while the droplet vaporization mode, associated with diffusive plasma, produced narrow-line emissions. Furthermore, by analyzing circuit transients alongside individual plasma events, we observed that film explosions generate higher currents, likely due to a shorter plasma length as the current preferentially flows through the thin liquid layer. Altogether, our results highlight the SPIDER probe’s efficacy and flexibility, making it well-suited for online material quantification of liquids in extreme environments.

AES↗

DOE OSTI2026

Vanishing dynamic strength measured in a transient high-pressure phase of tin

This work reports two independent experimental estimations of the dynamic strength of the body-centered tetragonal (bct) 𝛾 phase of tin, which occurs at pressures above about 9 GPa and transforms promptly back to the ambient 𝛽 phase upon pressure release. Measuring strength in such a transient high-pressure phase is challenging. One measurement used free-surface Richtmyer-Meshkov instabilities generated with gas gun impact. Another set of measurements used ramp-release loading in a pulsed-power facility. Strength estimations came from comparison to simulations using a comprehensive multiphase modeling framework that includes equation of state, shear moduli, and strength separately for each phase, and treats mixed-phase regions. Both experimental methods found the 𝛾-phase strength to be very low, within experimental uncertainty of zero. The existing literature on other materials, by contrast, almost universally reports higher strength in transient high-pressure phases compared with ambient phases. Recent Molecular-Dynamics simulations on tin in the literature showed almost zero deviatoric strength during a deformation-induced bct → bct transformation in which one of the 𝛾-phase 𝑎 axes “flips” to the 𝑐 axis. This reorientation currently provides the most plausible explanation for the low observed strength in 𝛾-phase tin.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Reconstruction and Dissolution of Copper Catalysts during Electrocatalytic Nitrate Reduction

Copper-based electrocatalysts are widely explored for electrochemical nitrate remediation, yet their stability under operating conditions remains poorly understood. While nanoscale and subnanoscale Cu motifs are known to restructure during the nitrate reduction reaction (NO 3 RR), how these transformations translate into irreversible material loss remains unclear. Here, we quantify Cu dissolution during NO 3 RR as a function of catalyst architecture and electrolyte chemistry using two model systems: single-atom Cu (Cu 1 ) and Cu nanoparticles (Cu NP ). Time-resolved leaching measurements, integrated with in situ X-ray absorption spectroscopy (XAS), reveal measurable Cu loss for both catalysts during NO 3 RR. Dissolution is concentrated at the initiation of electrolysis, coinciding with rapid restructuring, and depends strongly on morphology, with Cu NP consistently exhibiting greater Cu loss than Cu 1 . In situ XAS reveals that Cu 1 forms transient metallic clusters under reduction that largely redisperse upon returning to open-circuit voltage, whereas Cu NP undergoes reduction of an oxidized surface layer accompanied by sustained Cu loss during electrolysis. Notably, the presence of nitrate significantly intensifies restructuring and Cu loss, highlighting the critical role of electrolyte composition. Furthermore, these findings establish a direct link between electrochemical restructuring and Cu dissolution, unveiling electrolyte-dependent interactions as key determinants of catalyst durability in electrochemical nitrate conversion.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Nanohybrid of Cu2O-Ti3C2Tx as a Silver-Free MXene Sorbent for Iodine Gas Capture from Nuclear Waste

The capture of volatile radioiodine from nuclear fuel reprocessing off-gas streams remains a critical challenge due to the high volatility, long half-life of 129I, and biological uptake of iodide from the environment. Although silver-based sorbents provide strong iodine chemisorption, their high cost and regulatory classification as mixed radioactive-hazardous waste motivate the development of alternative materials. Here, we report a silver-free Cu2O-Ti3C2Tx MXene hybrid for iodine gas capture at 150 °C. Structural and compositional analyses confirm the formation of Cu2O nanoparticles on Ti3C2Tx nanosheets and their subsequent conversion to thermodynamically stable CuI upon static iodine gas exposure, achieving an iodine mass loading of up to 1115 mg/g. These results demonstrate the potential of Cu2O-Ti3C2Tx MXene as a copper-based alternative to silver sorbents for elevated-temperature iodine gas capture.

iodine gas capture↗

DOE OSTI2026

Relativistic Magnetohydrodynamic Simulations of Giant Magnetar Bursts

Gradual crustal deformation can generate strongly twisted magnetic fields around magnetars, potentially triggering giant flares with total energies exceeding 10 44 erg. In this letter, we present the first relativistic magnetohydrodynamic simulation of a surface shear-driven magnetar eruption, capturing reconnection-driven plasma heating, the ejection of relativistically hot plasma, and the formation of a hot fireball confined within the inner magnetosphere. We find that magnetic reconnection in the equatorial current sheet launches a hot trailing outflow capable of powering the initial spike observed in giant flares, while simultaneously leaving behind a thermally stratified fireball with sufficient thermal energy to produce the pulsating, decaying tail. Together, these features provide a self-consistent physical framework for understanding the observed energetics of magnetar giant flares. The eruption also expels a magnetically dominated giant plasmoid carrying up to ∼9% of the magnetosphere’s total magnetic energy. Furthermore, our simulation demonstrates how the plasmoid drives the formation of a blast wave—an important ingredient in models linking magnetar eruptions to fast radio bursts.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗

DOE OSTI2024

Determining Stellar Elemental Abundances from DESI Spectra with the Data-driven Payne

Abstract Stellar abundances for a large number of stars provide key information for the study of Galactic formation history. Large spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI) and LAMOST take median-to-low-resolution (R≲ 5000) spectra in the full optical wavelength range for millions of stars. However, the line-blending effect in these spectra causes great challenges for elemental abundance determination. Here we employDD-Payne, a data-driven method regularized by differential spectra from stellar physical models, to the DESI early data release spectra for stellar abundance determination. Our implementation delivers 15 labels, including effective temperatureT eff , surface gravity log g , microturbulence velocityv mic , and the abundances for 12 individual elements, namely C, N, O, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, and Ni. Given a spectral signal-to-noise ratio of 100 per pixel, the internal precisions of the label estimates are about 20 K forT eff , 0.05 dex for log g , and 0.05 dex for most elemental abundances. These results agree with the theoretical limits from the Crámer–Rao bound calculation within a factor of 2. The majority of the accreted halo stars contributed by the Gaia–Enceladus–Sausage are discernible from the disk and in situ halo populations in the resultant [Mg/Fe]–[Fe/H] and [Al/Fe]–[Fe/H] abundance spaces. We also provide distance and orbital parameters for the sample stars, which spread over a distance out to ∼100 kpc. The DESI sample has a significantly higher fraction of distant (or metal-poor) stars than the other existing spectroscopic surveys, making it a powerful data set for studying the Galactic outskirts. The catalog is publicly available.

Astronomy & Astrophysics↗

DOE OSTI2026

Design of a Cryogenic Assembly for Comparing Qubit Infrared Packaging Inefficiencies

It is well known that everything emits infrared (IR) light in the form of thermal radiation. IR radiation on superconducting qubits carries enough energy to cause decoherence and so-called “quasiparticle poisoning.” Therefore, proper IR shielding is needed to keep the qubit in a high coherence state. However, trying to extract IR-specific information from a qubit is difficult, so a microwave kinetic inductance detector (MKID) can be used instead. We show that the IR shielding inefficiencies of MKID packages can only be compared with other packaging inefficiencies. We create a cryogenic assembly CAD design, consisting of magnetic shield cans, a copper mounting plate and mounts, and an MKID enclosure, which thermalizes the assembly to dilution refrigerator temperatures. Phase 1 of this design involves using a narrowband IR laser source to test the IR shielding of packaging at the laser’s wavelength. When the best IR shielding packaging is determined, the superconducting qubit can replace the MKID, allowing the qubit to avoid decoherence due to IR radiation and boost its performance and coherence lifetime.

Mailliard, Robert J. [Fermilab] (ORCID:00090000277↗

DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Polymer-assisted transfer of MBE-grown van der Waals materials

Molecular beam epitaxy (MBE) has been used to create high-quality, large-scale two-dimensional van der Waals (2D vdW) materials. However, due to the strong adhesion between the substrate and deposited materials, the peel-off and dry transfer of MBE-grown vdW films onto other substrates has been challenging. This limits the study and use of MBE films for heterogeneous integration including stacked and twisted heterostructures. In this work, we develop a polymer-assisted dry transfer method and successfully perform full-film transfer of various MBE-grown 2D vdW materials including transition metal dichalcogenides (TMD), topological insulators (TI) and 2D magnets. In particular, we transfer air-sensitive 2D magnets, characterize their magnetic properties, and compare them with as-grown materials. The results show that the transfer technique does not degrade the magnetic properties, with the Curie temperature and hysteresis loops exhibiting similar behaviors after the transfer. Our results enable further development of heterogeneous integration of 2D vdW materials based on MBE growth.

Li, Ziling [The Ohio State University]↗

DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗

DOE OSTI2026

Reaction Optimization for Enzymatic Deconstruction of Industrially Relevant Nylon Composites

Plastics such as polyamides (PAs) possess unique physicochemical properties that make them indispensable in modern society. However, their energy‐intensive production and challenging end‐of‐life management highlight the urgent need for efficient recycling or remanufacturing solutions. Enzymatic depolymerization offers a promising route toward circular recycling, yet remains constrained by limited enzyme characterization, lack of validation under industrially relevant conditions and substrates, and overall performance. Here, we optimized the reaction conditions for three recently discovered nylon‐degrading enzymes. One of them, Nyl12, achieved product titers with PA6 and PA66 that exceed previously reported values, without enzyme engineering or substrate pretreatment. We further demonstrated the scalability of the process and its application to complex PA‐based materials used in microelectronic components. Analysis of substrate features, including surface area and particle size, revealed key parameters governing enzymatic activity and provided a framework for future pretreatment and process optimization efforts. In combination, these efforts provide a new benchmark for enzymatic nylon recycling.

nylon↗

DOE OSTI2024

Localizing the non-thermal X-ray emission of PSR J2229+6114 from its multi-wavelength pulse profiles

Context.Pulsars are detected over the whole electromagnetic spectrum, from radio wavelengths up to very high energies, in the GeV-TeV range. While the radio emission site for young pulsars is well constrained to occur at altitudes about several percent of the light-cylinder radius andγ-ray emission is believed to be produced in the striped wind, outside the light cylinder, the non-thermal X-ray production site remains unknown. Aims.The aim of this Letter is to localize the non-thermal X-ray emission region based on a multi-wavelength pulse profile fitting for PSR J2229+6114, which stands as a particularly good candidate given its high X-ray brightness. Methods.Based on the geometry deduced from the joint radio andγ-ray pulse profiles, we fixed the magnetic axis inclination angle and the line-of-sight (LoS) inclination angle. However, we left the region of X-ray emission unlocalized, setting it somewhere between the surface and the light cylinder. We localized this region and its extension by fitting the X-ray pulse profile as observed by the NICER,NuSTAR, and RXTE telescopes in the ranges of 2–7 keV, 3–10 keV, and 9.4–22.4 keV, respectively. Results.We constrained the non-thermal X-ray emission to arise from altitudes between 0.2 r L and 0.55 r L wherer L is the light-cylinder radius. The magnetic obliquity is approximatelyα ≈ 45° −50° and the LoS inclination angle isζ ≈ 32° −48°. Conclusions.This Letter is among the first works to offer a tight constraint on the location of non-thermal X-ray emission from pulsars. We plan to apply this procedure to several other promising candidates to confirm this new result.

Astronomy & Astrophysics↗

DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗

DOE OSTI2026

Tunable magnetic excitations in the honeycomb antiferromagnet (Co,Ni)⁢TiO3

The solid solution of the honeycomb antiferromagnet (AFM) Co0.5⁢Ni0.5⁢TiO3 (CNTO) was synthesized by mixing a 1:1 molar ratio of CoTiO3 (CTO) and NiTiO3 (NTO). Powder neutron scattering was used to determine the structure and magnetic spectrum, where the nuclear and magnetic structures resemble those of the end members. The Néel temperature (T𝑁 = 27 ± 1 K), the ordered magnetic moment (M = 2.29 ± 0.03 𝜇𝐵 per magnetic ion), and lattice parameters are intermediate between those of the two. From inelastic neutron scattering, it is observed that the magnon density of states (MDOS) extends up to 13 meV, and the interaction can be described by an XXZ-type magnetic Hamiltonian. Above 15 meV, spin-orbit excitons (SOEs) are observed similarly to those in CTO, but with modified spin-orbit crystal-field splitting because of the substitution of Ni at Co sites, and the disorder induced by the Ni substitution modifies the spin-orbit crystal field. Thus, the magnetic dynamics of the CNTO cannot be described as a simple average of the two parent compounds.

Rathnayaka, Srimal [University of Virginia, Charlo↗

DOE OSTI2025

Socio-Techno-Economic Feasibility of Deep Geothermal with EGS for Residential District Heating in the Northeastern United States

In this paper, we present socio-techno-economic simulation results for enhanced geothermal system (EGS) district heating systems in the northeastern United States. We applied the geospatial Distributed Geothermal Market Demand (dGeo) tool coupled with GEOPHIRES to evaluate the feasibility of geothermal deep direct-use, utilizing EGS reservoirs for providing residential heating with district heating systems. The results were combined with JEDI results to assess social impacts. Simulation results assuming doublet EGS reservoirs with target production temperature of 80 degrees C and newly constructed district heating systems indicate a wide range in system levelized cost of heat (LCOH) values ranging from ~$15/MMBtu to over $1,000/MMBtu, with most the attractive regions urban areas with high total thermal demand and high thermal demand density. We estimate about 60 GWth in potential installed capacity with LCOH values under $50/MMBtu and 100 GWth in potential capacity with LCOH values under $100/MMBtu, respectively.

15 GEOTHERMAL ENERGY↗