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DOE OSTI2025

Conceptual Designs for Irradiation Creep Testing of SiC in HFIR

Understanding irradiation creep of nuclear fuel cladding is important to properly size the initial fuel-cladding gap and understand when pellet-cladding contact is expected to occur due to a combination of fuel swelling and cladding creep-down. Irradiation creep also plays a role in relaxing stresses that develop in-pile. Silicon carbide fiber–reinforced silicon carbide matrix (SiC/SiC) composites are the leading long-term accident-tolerant fuel cladding concept for light-water reactors (LWRs). Although some limited data are available regarding irradiation creep of the individual constituents (fibers, matrix), data regarding irradiation creep of SiC/SiC composites are currently insufficient. Additional data regarding irradiation creep compliance and the rupture lifetime (combination of creep and slow crack growth) are needed to understand material limitations. This work describes the design and development of two irradiation vehicles that are being pursued for testing SiC/SiC concepts in the High Flux Isotope Reactor (HFIR). The first is a passive experiment, referred to as the PRECISE experiment, that leverages the constant coolant pressure of HFIR to compress a metallic bellows and provide a well-characterized load to drive creep in a SiC/SiC dog bone specimen. The total creep strain would be quantified post-irradiation by measuring dimensional changes of the specimen length as well as local dimensional changes within the gauge region. Non-stressed specimens would also be irradiated under the same conditions to provide an indication of dimensional changes due to radiation-induced swelling in the absence of creep. A second, more complex experiment, referred to as the INSITE experiment, is being designed in parallel that would use pneumatics to pressurize a metal bellows and linear variable differential transformers (LVDTs) to measure the specimen displacement in situ during irradiation. Such an experiment would provide significantly more data regarding the evolution of the creep compliance as a function of dose and applied stress within a single experiment but would require significantly more development time and cost to execute. The primary concern with the INSITE experiment is the accuracy, reliability, and expected lifetime of the LVDTs during irradiation at elevated temperatures. Efforts are being made to adjust the experiment design and operating procedure to limit LVDT temperatures and mitigate or otherwise compensate for uncertainties due to factors such as temperature fluctuations, creep in the surrounding structural materials, and drift of the LVDTs. This work describes the experiment designs, thermal and structural analysis that were performed to ensure that the desired temperature and stress conditions can be achieved, some initial sensitivity analyses to predict the evolution of the radiation-induced specimen displacements, and potential sources of uncertainty in the measurements. Out-of-pile testing is being performed in parallel to confirm that the test trains achieve the expected stress states in the specimens and do not result in prohibitive stress concentrators (e.g., in the grip regions) that might risk pre-mature failure. The PRECISE experiments are proceeding toward fabrication and assembly with HFIR insertion planned during fiscal year 2026. The INSITE experiment is progressing toward out-of-pile demonstrations, which will provide more conclusive evidence regarding the feasibility of executing these tests in HFIR or whether alternative displacement monitoring techniques may need to be considered.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

SAXS Assistant: Automated SAXS analysis for structural discovery in biologics and polymeric nanoparticles

Small-angle x-ray scattering (SAXS) is a powerful technique for assessing macromolecular structure. High-throughput SAXS is limited by the time-consuming and, at times, subjective nature of SAXS data interpretation. Here, we present SAXS Assistant, a Python-based script that streamlines SAXS data analysis to extract features for machine learning (ML) and key structural parameters, including the Guinier radius of gyration (R g ), pair distance distribution function (PDDF)-derived R g , maximum particle dimension (D max ), and Kratky plots. The script builds upon BioXTAS RAW and validates reliability via Guinier/PDDF R g agreement, an important indicator of well-measured data sets. For assistance in D max estimation, a multilayer perceptron regressor was trained with 1940 data files from the Small Angle Scattering Biological Data Bank. The model achieved a test set performance R 2 = 0.90 and mean absolute error = 11.7 Å. Training exclusively with experimental data translates analyses from researchers, including experts in the field, to the ML model, which helps assess D max estimations from PDDF. Gaussian mixture model clustering was implemented to classify profiles into structural classes based on entries in the Small Angle Scattering Biological Data Bank. Users may therefore assess the similarity between experimental samples and known biomolecular shapes within the mapped repository entries. This probabilistic clustering aids in quantifying information from Kratky and generating shape-descriptive features. SAXS Assistant accelerates SAXS data analysis through enforced quality control, ML-ready outputs, and flags for low-confidence results. In addition to providing the ability to analyze large data sets at high throughput, this tool is versatile and may serve researchers in both biological and synthetic polymer research fields.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Correcting for Selection Biases in the Determination of the Hubble Constant from Time-Delay Cosmography

The time delay between multiple images of strongly lensed quasars has been used to infer the Hubble constant. The primary systematic uncertainty for time-delay cosmography is the mass-sheet transform (MST), which preserves the lensing observables while altering the inferred ⁠H 0 . The TDCOSMO collaboration used velocity dispersion measurements of lensed quasars and lensed galaxies to infer that mass sheets are present, which decrease the inferred H 0 by 8 per cent. Here, we test the assumption that the density profiles of galaxy–galaxy and galaxy–quasar lenses are the same. We use a composite star-plus-dark-matter mass profile for the parent deflector population and model the selection function for galaxy–galaxy and galaxy–quasar lenses. We find that a power-law density profile with an MST is a good approximation to a two-component mass profile around the Einstein radius, but we find that galaxy–galaxy lenses have systematically higher mass-sheet components than galaxy–quasar lenses. For individual systems, λ int correlates with the ratio of the half-light radius and Einstein radius of the lens. By propagating these results through the TDCOSMO hierarchical inference code, we find that H 0 is lowered by a further 3 per cent. Using a more recent measurement of velocity dispersions and our fiducial model for selection biases, we infer H 0 = 66 ± 4 (stat) ± 1 (model sys) ± 2 (measurement sys) km s -1 Mpc -1 for the TDCOSMO plus SLACS data set. The first residual systematic error is due to plausible alternative choices in modelling the selection function, and the second is an estimate of the remaining systematic error in the measurement of velocity dispersions for SLACS lenses. Accurate time-delay cosmography requires precise velocity dispersion measurements and accurate calibration of selection biases.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Crystal Prediction and Design of Tunable Light Emission in BTB–Based Metal–Organic Frameworks

Metal-organic frameworks (MOFs) have recently been shown to exhibit unique mechanisms of luminescence based on charge transfer between structural units in the framework. These MOFs have the potential to be structural tuned for targeted emission with little or no metal participation. Here a computationally led, material design and synthesis methodology is presented that elucidates the mechanisms of light emission in interpenetrated structures comprised of metal centers (M = In, Ga, InGa, InEu) and BTB (1,3,5-Tris(4-carboxyphenyl)benzene) linkers, forming unique luminescent M-BTB MOF frameworks. Gas phase and periodic electronic structure calculations indicate that the intensity of the emission and the wavelength are overwhelmingly controlled by a combination of the number of interacting stacked linkers and their interatomic spacings, respectively. In the MOF, the ionic radii of the metal centers primarily control the expansion or shrinkage of the linker stacking distances. Experimentally, multiple M-BTB-based MOFs are synthesized and their photoluminescence was tested. Experiments validated the modeling by confirming that shifts in the crystal structure result in variations in light emission. Through this material design method, the mechanisms of tuning luminescence properties in interpenetrated M-BTB MOFs have been identified and applied to the design of MOFs with specific wavelength emission based on their structure.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multi-frame x-ray radiography and image tracking for quantification of expansion in laser-driven tin ejecta microjets

One regime of experimental particle-laden flow study involves ejecta microjets—often defined as a stream of micrometer-scale particles generated through shock interaction with a non-uniform surface and generally travel above 1 km/s. In order to capture the change in characteristics as a function of propagation time, we apply a multi-frame x-ray radiography platform to observe and track the jet transport dynamics. A synchrotron x-ray source allows us to perform quantitative analyses and comparisons between the eight images captured by the imaging system. Observation of a single jet through time allows the use of a cross correlation algorithm to independently track various regions within the jet and quantify the jet expansion over time using normalized area and normalized areal density values. Through a comparison with the calculated values of ballistic transport, these findings show less expansion than expected for ballistically transporting particles. In conclusion, this work combines multi-frame synchrotron radiography with image tracking to establish a foundation for future studies on jet transport and particle interaction dynamics.

Sun, Yuchen [Lawrence Livermore National Laborator↗

DOE OSTI2026

Oil-impregnated densified wood veneer with high electrical insulation enabled by nanosized oil channels

Growing energy demands and renewable integration are stressing the aging power grid infrastructure. Lignocellulosic oil-impregnated paper is widely used in power transformers but suffers from critical limitations, such as low dielectric strength, mechanical strength, and thermal conductivity, causing premature transformer failures. Here, we demonstrate a superior electrically insulating oil-impregnated paper design using the naturally anisotropic structure of densified wood veneer to achieve nanosized channels of oil that efficiently disrupt electrical breakdown pathways. The developed oil-impregnated densified wood (ODW) creates aligned cellulose fibers with 166 ± 87–nanometer oil nanochannels, achieving record dielectric strength of 105 kilovolts per millimeter. The structure also delivers a mechanical strength of up to 384 megapascals and a thermal conductivity of 0.33 watts per meter per kelvin, enabling enhanced longevity upon thermal aging tests. The ODW could replace conventional transformer insulation to enhance power transformer performance and improve lifetime. Moreover, its anisotropic oil-filled nanochannel design offers a general strategy for hybrid dielectrics in medium- and high-voltage applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Workflow for evaluating enzyme immobilization and performance for continuous flow manufacturing

Enzymes have shown promise in various industries due to their functional specificity, catalytic efficiency, and environmental sustainability. These biological catalysts can be a pivotal component of manufacturing pipelines like continuous flow chemistry. For this, there exists a need to robustly immobilize enzymes on solid supports and assess the effects of the solid supports on catalytic performance and stability. Here, we use an industrially relevant model enzyme, C. ensiformis (Jack bean) urease, to demonstrate immobilization and assess performance in the context of continuous flow manufacturing. Various immobilization strategies were screened focusing on immobilization efficiency, protocol simplicity, and urease biocatalyst kinetics. Based on this, CDI-agarose and NHS-agarose resins were identified as the best-performing immobilization strategies for urease. CDI-agarose-urease and NHS-agarose-urease were then scaled up and applied to a large-scale continuous flow reactor to evaluate product yields, operational stability, and long-term stability. These experiments identified differences in stability and performance depending on the immobilization method tested. This highlights the importance of screening immobilization methods and subsequent enzyme performance for each candidate biocatalyst used in manufacturing to promote optimal performance and stability. As such, this work provides a framework for evaluating enzyme biocatalyst immobilization approaches to improve performance and enable transition into industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

1,10-Phenanthroline-Iron Complex-Derived Fe-N-C Electrocatalysts: Enhanced Oxygen Reduction Activity and Stability Through Synthesis Tuning

The development of electrocatalysts composed of earth-abundant elements is essential for advancing the commercial application of Proton Exchange Membrane Fuel Cells (PEMFC). Among these, single-atom electrocatalysts, such as Fe-N-C, show great promise for the oxygen reduction reaction (ORR). This study aims to improve the ORR activity and stability of Fe-N-C electrocatalysts by fine-tuning the straightforward 1,10-phenanthroline-iron complexation synthesis method. Key parameters, including iron-to-phenanthroline ratio, carbon powder surface area, and pyrolysis temperature were systematically varied to evaluate their influence on the resulting electrocatalysts. The findings of this study revealed that the electrocatalysts synthesized with 1,10-phenanthroline (Phen) and high-surface-area Black Pearls (BP) possessed much better ORR activity than electrocatalysts prepared by using Vulcan carbon (lower surface area). Interestingly, electrocatalysts prepared with BP, but with a non-bidentate nitrogen-containing ligand molecule, such as imidazole, showed a much poorer activity, as the resulting material predominantly consisted of inactive structures, such as encapsulated iron nanoparticles and iron oxide, as evidenced by HR-TEM, EXAFS, and XRD. Therefore, the results suggest that only the synergistic combination of the bidentate ligand phenanthroline (Phen) and the high-surface-area carbon support (BP) favored the formation of ORR-active Fe-N-C single-atom species upon pyrolysis. The study also unveiled a significant enhancement in electrocatalyst stability during accelerated durability tests (and air storage) as the pyrolysis temperature was increased from 700 to 1300 °C, albeit at the expense of ORR activity, likely resulting from the generation of iron particles. Pyrolysis at 1050 °C yielded the electrocatalyst with the most favorable balance of activity and stability in rotating disk measurements, while maintaining moderate durability under PEM fuel cell operation. The insights obtained in this study may guide the development of more active efficient and durable electrocatalysts, synthesized via a simple method using earth-abundant elements, for application in PEMFC cathodes.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Hydraulic constraints to stomatal conductance in flooded trees

Stomatal closure is a pervasive response among trees exposed to flooded soil. We tested whether this response is caused by reduced hydraulic conductance in the soil-to-leaf hydraulic continuum (k total ), and particularly by reduced root hydraulic conductance (k root ), which has been widely hypothesized. We tracked stomatal conductance at the leaf level (g s ) and canopy scale (G s ) along with physiological conditions in two temperate tree species, Magnolia grandiflora and Quercus virginiana, that were subjected to flood and control conditions in a greenhouse experiment. Flooding reduced g s , G s , k root and k total . Path analysis showed strong support for direct effects of k total on g s and for flood duration on k total , but not k root on k total . A process-based model that accounted for the k total reduction predicted the timeseries of G s in flood and control treatment trees reasonably well (predicted versus observed G s R 2 = 0.80 and 0.51 for M. grandiflora and Q. virginiana, respectively). However, accounting only for k root reduction in flooded trees was insufficient for predicting observed G s reduction. Together, these results suggest that hydraulic constraints were not limited to roots and highlight the need to account for flooding effects on k total when projecting forest ecosystem function using process-based models.

Plant stress↗

DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

14 SOLAR ENERGY↗

DOE OSTI2025

Algae Biofilm Produced from Anaerobic Digester Wastewater Demonstrated as Biofertilizer for Dwarf Wheat

Aims: To correlate yield of dwarf wheat fertilized with algae biofilm produced using anaerobic digester effluent (digestate) from a municipal water resource recovery facility as nutrient source and compare with yield using non-algae biofilm based fertilizers and a negative control. Study Design: Greenhouse testing for measurement of biomass yields and statistical analysis of wheat yield data results. Place and Duration of Study: Department of Biological Engineering Algae Processing and Products Facility greenhouse between March and June, 2024. Methodology: Yield of a dwarf cultivar of red spring wheat was measured as a function of four treatments including three fertilizers and a negative control: (1) algae biofilm cultivated on anaerobic digester effluent, (2) positive control of Osmocote commercial slow release fertilizer, (3) anaerobic digester biosolids, and (4) negative control with no fertilizer addition. Results: Average yield values (gm) for the four treatments were: (1) 1.25, (2) 0.99, (3) 0.72, and (4) 0.52, respectively. Algae biofilm fertilizer performed significantly better than Osmocote with P = .002, anaerobic digester biosolids with P = .05, and no fertilizer addition with P = .05. Struvite, a slow release fertilizer, was observed to be associated with the algae biofilm as a precipitate of the high concentrations of nutrients within the anaerobic digester water. Struvite (MgNH4PO46H2O) releases plant available nitrogen and phosphorus over time and is biologically precipitated due to the increase in solution pH as a result of photosynthesis by the algae biofilm. Conclusion: Algae biofilm cultivated on anaerobic digester wastewater can serve as a biofertilizer for recycling nutrients within the biosphere for sustainable nutrient management.

09 BIOMASS FUELS↗

DOE OSTI2024

Measurement report: A comparison of ground-level ice-nucleating-particle abundance and aerosol properties during autumn at contrasting marine and terrestrial locations

Abstract. Ice-nucleating particles (INPs) are an essential class of aerosols found worldwide that have far-reaching but poorly quantified climate feedback mechanisms through interaction with clouds and impacts on precipitation. These particles can have highly variable physicochemical properties in the atmosphere, and it is crucial to continuously monitor their long-term concentration relative to total ambient aerosol populations at a wide variety of sites to comprehensively understand aerosol–cloud interactions in the atmosphere. Hence, our study applied an in situ forced expansion cooling device to measure ambient INP concentrations and test its automated continuous measurements at atmospheric observatories, where complementary aerosol instruments are heavily equipped. Using collocated aerosol size, number, and composition measurements from these sites, we analyzed the correlation between sources and abundance of INPs in different environments. Toward this aim, we have measured ground-level INP concentrations at two contrasting sites, one in the Southern Great Plains (SGP) region of the United States with a substantial terrestrially influenced aerosol population and one in the Eastern North Atlantic Ocean (ENA) region with a primarily marine-influenced aerosol population. These measurements examined INPs mainly formed through immersion freezing and were performed at a ≤ 12 min resolution and with a wide range of heterogeneous freezing temperatures (Ts above −31 °C) for at least 45 d at each site. The associated INP data analysis was conducted in a consistent manner. We also explored the additional offline characterization of ambient aerosol particle samples from both locations in comparison to in situ data. From our ENA data, on average, INP abundance ranges from ≈ 1 to ≈ 20 L−1 (−30 °C ≤ T ≤ −20 °C) during October–November 2020. Backward air mass trajectories reveal a strong marine influence at ENA with 75.7 % of air masses originating over the Atlantic Ocean and 96.6 % of air masses traveling over open water, but analysis of particle chemistry suggests an additional INP source besides maritime aerosols (e.g., sea spray aerosols) at ENA. In contrast, 90.8 % of air masses at the SGP location originated from the North American continent, and 96.1 % of the time, these air masses traveled over land. As a result, organic-rich SGP aerosols from terrestrial sources exhibited notably high INP abundance from ≈ 1 to ≈ 100 L−1 (−30 °C ≤ T ≤ −15 °C) during October–November 2019. The probability density function of aerosol surface area-scaled immersion freezing efficiency (ice nucleation active surface site density; ns) was assessed for selected freezing temperatures. While the INP concentrations measured at SGP are higher than those of ENA, the ns(T) values of SGP (≈ 105 to ≈ 107 m−2 for −30 °C ≤ T ≤ −15 °C) are reciprocally lower than ENA for approximately 2 orders of magnitude (≈ 107 to ≈ 109 m−2 for −30 °C ≤ T ≤ −15 °C). The observed difference in ns(T) mainly stems from varied available aerosol surface areas, Saer, from two sites (Saer,SGP > Saer,ENA). INP parameterizations were developed as a function of examined freezing temperatures from SGP and ENA for our study periods.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Physical patterning of high-Q superconducting niobium resonators via ion beam etching

The development of superconducting quantum circuits increasingly involves the exploration of chemically distinct materials and complex multilayered structures. Accelerating this trend may benefit from low-damage, materials-agnostic patterning techniques that are compatible with a broad range of materials. Here, in this work, we investigate the utility of low-energy ion beam etching (IBE), a physical patterning technique, as an alternative to reactive ion etching for fabricating low-loss superconducting resonators. We use niobium (Nb) resonators as a test platform, leveraging their well-characterized performance metrics for benchmarking. To address IBE-induced surface redeposition, we introduce an in situ aluminum capping layer combined with targeted post-fabrication chemical treatment. This strategy yields resonators with internal quality factors as high as 6 × 10 5 in the single-photon regime at 50 mK. These results establish low-energy IBE as a promising patterning technique for superconducting devices, with the potential to accelerate development across chemically diverse and multilayered material platforms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB + QRPA calculations with the Gogny interaction

The zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of-the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98 Zr . This region includes a benchmarking case and offers insights into nuclear deformation phenomena. Here, to investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the quasiparticle random phase approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98 Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the 𝐾 𝜋 = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the 𝐾 𝜋 = 0 − and 𝐾 𝜋 = 1 − components. Intrinsic transition densities and associated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e., we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Coupling between collective modes in the deformed 98 Zr nucleus: Insights from consistent HFB+QRPA calculations with the Gogny interaction

The Zirconium isotopes exhibit structural properties that present multiple challenges to nuclear theory. Investigations of the coupling present within isoscalar modes and within isovector modes are scarce but important for advancing our understanding of the microscopic picture of nuclei. To explore some of these underlying coupling features, and to test the predictive power of a state-of the-art nuclear structure approach, we provide a detailed analysis of the properties of 90,96,98Zr. This region includes a benchmarking case and offers insights into nuclear deformation phenomena. To investigate the coupling between collective modes in deformed nuclei, we focused our analysis on the ground and excited-state properties of these isotopes, employing a consistent approach with the axially-symmetric deformed Hartree-Fock-Bogoliubov (HFB) and the Quasiparticle Random Phase Approximation (QRPA) framework, both using the Gogny D1M force. This approach effectively describes both low-lying and giant-resonance states. We devoted special attention to the deformed 98Zr nucleus, where we confirm the existence of coupling between monopole and quadrupole excitations through the K π = 0 + QRPA components and demonstrate an analogous dipole-octupole coupling through the K π = 0 − and K π = 1 − components. Intrinsic transition densities and associ ated radial projections illustrate the coupling. Our work complements and extends earlier studies carried out using density-functional-based methods and notably, we included the complete Coulomb interaction also in the pairing fields, i.e. we treat terms exactly that are approximated in typical calculations that use the Gogny D1 and D2 interaction families.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗