NSUF Publications

The atomic structure and chemistry at metal/oxide interfaces play a crucial role in determining their properties. However, studying semi-coherent metal/oxide interfaces that include misfit dislocations through density functional theory (DFT) is often computationally expensive due to the large number of atoms involved, ranging from hundreds to thousands. In this study, we explore solute segregation behavior at the Fe/Y2O3 interface—an important model interface for cladding applications in nuclear fission reactors—by combining DFT calculations with a machine learning (ML) approach. ML models are trained using DFT-calculated segregation energies (ESeg) to identify the key chemical and geometric factors influencing solute segregation at metal/oxide interfaces, revealing the competition between these features in determining ESeg. Moreover, the segregation behavior at a specific Fe/Y2O3 interface is predicted with high accuracy using ML models trained on data from this interface. Furthermore, it is found that the ML models could also predict solute segregation at a different Fe/Y2O3 interface with a new orientation relationship (OR), at a computational cost of less than 1/45 of that required for similar DFT calculations.

Graphite's resilience to high temperatures and neutron damage makes it vital for nuclear reactors, yet irradiation alters its microstructure, degrading key properties. We used small‐ and wide‐angle X‐ray scattering to study neutron‐irradiated fine‐grain nuclear graphite (Grade G347A) across varied temperatures and fluences. Results show significant shifts in internal strain and porosity, correlating with radiation‐induced volume changes. Notably, porosity volume distribution (fractal dimensions) follows non‐monotonic volume changes, suggesting a link to the Weibull distribution of fracture stress.

Molecular sieves such as zeolite‐based materials are ubiquitous in industrial separation processes. However, there is a significant gap in understanding the surface properties and adsorption mechanisms for commercial zeolites, as most research focuses on pure zeolite powders rather than industrially relevant forms. This work addresses this gap in understanding by employing advanced characterization techniques, including positron annihilation spectroscopy, X‐ray diffraction, scanning electron microscopy, X‐ray fluorescence spectroscopy, X‐ray photoelectron spectroscopy, liquid nitrogen sorption, and Fourier‐transform infrared spectroscopy, to investigate the adsorption and desorption behavior of water in commercial zeolite 13X. Our research reveals insights into the pore‐filling mechanisms, the impact of material binders on adsorption properties, and the dynamics of hydration and drying processes for zeolites. Monitoring changes on a minute scale allowed the distinction between fast and slow processes leading to sample drying. The identification of positronium bound to Na⁺ ions indicated that water molecules remain in the vicinity of Na⁺ ions after air‐drying zeolite 13X. These findings highlight the importance of various environmental conditions in restoring zeolite properties to baseline after hydration, with significant implications for optimizing industrial processes. This work sets the direction for further research aimed at developing more efficient and robust separation techniques.

Modern gravitational-wave science demands increasingly accurate and computationally intensive numerical relativity (NR) simulations. The Python-based, open-source NRPy framework generates optimized C/C++ code for NR, including the complete NR code BlackHoles@Home (BH@H), which leverages curvilinear coordinates well-suited to many astrophysical scenarios. Historically, BH@H was limited to single-node OpenMP CPU parallelism. To address this, we introduce superB, an open-source extension to NRPy that enables automatic generation of scalable, task-based, distributed-memory Charm++ code from existing BH@H modules. The generated code partitions the structured grids used by NRPy/BH@H, managing communication between them. Its correctness is validated through bit-identical results with the standard OpenMP version on a single node and via a head-on binary black hole simulation in cylindrical-like coordinates, accurately reproducing quasi-normal modes (up to ℓ = 8 ). The superB/NRPy-generated code demonstrates excellent strong scaling, achieving an ≈45× speedup on 64 nodes (7168 cores) compared to the original single-node OpenMP code for a large 3D vacuum test. This scalable infrastructure benefits demanding simulations and lays the groundwork for future multi-patch grid support, targeting long inspirals, extreme parameter studies, and rapid follow-ups. This infrastructure readily integrates with other NRPy/BH@H-based projects, enabling performant scaling for the general relativistic hydrodynamics code GRoovy, and facilitating future coupling with GPU acceleration via the NRPy-CUDA project.

Burnup estimation in nuclear fuels is vital for evaluating fuel performance, transportation, and safe fuel storage. Accurate assessments of burnup from service period and spent fuels involve tracking the consumption of fissile isotopes of uranium (U) offering a direct insight into energy changes within the fuels especially for thermal spectrum reactors. In current approach, mass spectroscopic technique in atom probe tomography (APT) is utilized for accurate quantification of U isotopes. Quantification of U peaks in mass spectrum is performed on asymmetric shapes due to delayed signals, known as thermal tails, particularly for poorly conducting samples analyzed in laser mode. In this study, we introduce a novel quantification tool for isotopic analysis from APT datasets by developing a fitting algorithm based on shapes of the peaks. A MATLAB-based dynamic peak fitting toolbox is developed and designed to adapt to various peak shapes, ensuring accurate quantification of U isotopes. The effectiveness of this approach is demonstrated in standard Ni-Cr sample, depleted and enriched U samples, and U-based fuels with different burnup levels. The viability of this approach for isotopic quantification is demonstrated on both metallic and ceramic fuels.

The infrared multiphoton dissociation spectrum of the gas‐phase uranyl perchlorate anion ([UO₂(ClO₄)₃]⁻) was measured. The asymmetric uranyl stretch lies somewhere between 930 and 1030 cm⁻¹, though it could not be deconvoluted from a closely located perchlorate stretching mode. The experimentally measured spectrum was in good agreement with spectra calculated using density functional theory with the B3LYP and TPSSh functionals and Grimme's D3 dispersion corrections with Becke–Johnson damping. The calculations suggest that the asymmetric uranyl stretch lies in the range of 970–980 cm⁻¹, about 20–30 cm⁻¹ higher than for the analogous uranyl trinitrato complex and consistent with perchlorate as a weaker ligand than nitrate. Like the uranyl trinitrato anion, fragmentation of [UO₂(ClO₄)₃]⁻ by collision‐induced dissociation resulted in loss of a ClO₃^• radical to form [UO₃(ClO₄)₂]⁻. Infrared multiphoton dissociation measurements of [UO₃(ClO₄)₂]⁻ indicate that the structure of the product ion has an O⁻ ligand bound to the uranyl in a T‐arrangement and two perchlorate ligands, matching the findings from uranyl nitrato complexes. The uranyl stretch in this complex was slightly separated from the perchlorate modes and was measured at 906 cm⁻¹, slightly higher than for the analogous nitrate complex, again consistent with perchlorate as a weaker ligand than nitrate in the gas phase.

The electrodes and solid‐state electrolytes in protonic ceramic electrochemical cells (PCECs) experience significant lattice expansions when exposed to high steam concentrations at elevated temperatures. In this paper, phonon calculations based on a new machine learning potential (MLP) are employed to elucidate the volume expansions of the proton‐conducting PrNi_xCo_1‐xO_3‐δ (PNC) lattices, manifested under a combined influence of oxygen vacancies () and proton uptake () in the bulk at varying Ni/Co occupancies. It is revealed that the Ni/Co occupancy contributes to thermal and chemical expansions differently, where thermal expansions are related to Co occupancy. In contrast, chemical expansions are more closely associated with the Ni occupancy. Both and lead to higher thermal expansions when compared to the pristine PNC. The temperature increase will negatively impact the hydration‐induced chemical expansions. For combined thermal and chemical expansions, it is predicted that the strategies that boost the PCEC's electrochemical performance may harm the electrode–electrolyte interfacial stability, when the Ni occupancy is high, due to severe chemical expansions. Mitigating chemical expansions of the Ni‐abundant PNC will benefit the interfacial stability. The presented computational methods for phonon calculations, based on emerging machine learning interatomic potential techniques are anticipated to have a lasting impact on future PCEC development.

The development of a Pb-shielded fixture for the execution of a small-angle neutron scattering (SANS)-based workflow for interrogation of highly irradiated nuclear materials has been explored. The Pb shielding was specially designed to reduce the detected radioactivity from the specimen during SANS experiments, and the overall configuration is termed shielded magnetic SANS (SM-SANS). Two FeCrAl-based alloys, C35M and 125YF, were examined with the SM-SANS technique using a free-form size distribution locally monodisperse model in both the as-received and irradiated states. Quantitative values derived from the free-form size distribution were compared with atom probe tomography experiments. Microstructural and compositional parameters determined using the two characterization techniques were complements of each other. The results demonstrate that the SM-SANS technique is an effective means of characterizing nanoscale clustering in irradiated material systems and provides new avenues for investigating radioactive material microstructures.

The diffusion of Mg defects in 3C-SiC is studied using the density functional theory. Mg has the highest burn-in rate as a transmutant in 3C-SiC when it is placed in high-energy neutron irradiation environment of a fusion reactor. The presence and evolution of transmutant defects impact thermal and mechanical properties of this important structural material. This study is focused on understanding the structure, stability, and evolution of Mg defects and the interaction of Mg with native defects in 3C-SiC. Our calculations of diffusion coefficients for different Mg defects suggest that Mg is likely to diffuse faster in pristine 3C-SiC than in the damaged one, in agreement with earlier experimental observations.

The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which is well suited for capturing phonons and phonon-related properties. Our approach is based on direct comparisons of the second- and third-order irreducible derivatives (IDs) between an EIP and the Born–Oppenheimer potential within density functional theory (DFT) calculations. IDs fully exploit space group symmetry and allow for training without redundant information. We demonstrate the fidelity of our approach in the context of ThO₂ and UO₂, where we optimize parameters of an embedded-atom method potential in addition to core–shell interactions. Our EIPs provide thermophysical properties in good agreement with DFT and outperform widely utilized EIPs for phonon dispersion and thermal conductivity predictions. Reasonable estimates of thermal expansion and formation energies of Frenkel pairs are also obtained.

We have studied the effects of phonon-boundary scattering on the thermal transport of topological Kondo insulator SmB6. The studies have been performed using the 3ω method across a temperature range 3–300 K. Our results indicate that the thermal conductivity of micro-sized SmB6 is of an order of magnitude smaller than that of a bulk single crystal. Using the Callaway model, we analyzed the low-temperature lattice thermal conductivity of the microcrystal and demonstrated that phonon scattering at the sample boundaries is a major contributor to the thermal resistance in this topological material. Furthermore, our study reveals that the temperature dependence of the lattice thermal conductivity exhibits a double-peak structure, suggesting strong phonon–phonon or phonon–defect interactions in this material, characteristic of resonant scattering. These findings will help in a better understanding of thermal transport in advanced materials and devices at the micro scale.

Lightweight energy storage devices are essential for developing compact wearable and distributed electronics, and additive manufacturing offers a scalable, low‐cost approach to fabricating such devices with complex geometries. However, additive manufacturing of high‐performance, on‐demand energy storage devices remains challenging due to the need for stable, multifunctional nanomaterial inks. Herein, the development of 2‐dimensional (2D) titanium carbide (Ti₃C₂T_x MXene) ink that is compatible with aerosol jet printing for energy storage applications is demonstrated. The developed MXene ink demonstrates long‐term chemical and physical stability, ensuring consistent printability and achieving high‐resolution prints (≈45 µm width lines) with minimal overspray. The high‐resolution aerosol‐jet printed MXene supercapacitor achieves an areal capacitance of 122 mF cm⁻² and a volumetric capacitance of 611 F cm⁻³, placing them among the highest‐performing printed supercapacitors reported to date. These findings highlight the potential of aerosol jet printing with MXene inks for on‐demand, scalable, and cost‐effective fabrication of printed electronic and electrochemical devices.

Next-generation gravitational wave (GW) detectors such as Cosmic Explorer, the Einstein Telescope, and LISA, demand highly accurate and extensive GW catalogs to faithfully extract physical parameters from observed signals. However, numerical relativity (NR) faces significant challenges in generating these catalogs at the required scale and accuracy on modern computers, as NR codes do not fully exploit modern GPU capabilities. In response, we extend NRPy, a Python-based NR code-generation framework, to develop NRPyEllipticGPU—a CUDA-optimized elliptic solver tailored for the binary black hole initial data problem. NRPyEllipticGPU is the first GPU-enabled elliptic solver in the NR community, supporting a variety of coordinate systems and demonstrating substantial performance improvements on both consumer-grade and HPC-grade GPUs. We show that, when compared to a high-end CPU, NRPyEllipticGPU achieves on a high-end GPU up to a sixteenfold speedup in single precision while increasing double-precision performance by a factor of 2–4. This performance boost leverages the GPU’s superior parallelism and memory bandwidth to achieve a compute-bound application and enhancing the overall simulation efficiency. As NRPyEllipticGPU shares the core infrastructure common to NR codes, this work serves as a practical guide for developing full, CUDA-optimized NR codes.

The quantitative analysis of dislocation-type defects in irradiated materials is critical to materials characterization in the nuclear energy industry. The conventional approach of an instrument scientist manually identifying any dislocation defects is both time-consuming and subjective, thereby potentially introducing inconsistencies in the quantification. This work approaches dislocation-type defect identification and segmentation using a standard open-source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on two alloys not represented in the training set. Inference of dislocation defects using transmission electron microscopy on three different irradiated alloys relevant to the nuclear energy industry are examined in this work with widely varying pixel noise levels and with completely unrelated composition and dislocation formations for practical post irradiation examination analysis. Code and models are available at https://github.com/idaholab/PANDA.

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

A large-area event-mode camera system coupled with a $$^{6}$$ LiF-ZnS:Ag scintillator is applied for neutron resonance imaging (NRI) on the energy-resolved neutron imaging (ERNI) flight path, also known as Flight Path 5 (FP5), at the Los Alamos Neutron Science Center (LANSCE). This novel neutron imaging system, featuring a 120 $$\times$$ 120 mm $$^2$$ field of view, efficiently captures resonance information across the entire image in a single acquisition, significantly reducing beam time requirements compared to conventional energy-resolved neutron imaging systems. High-quality neutron radiographs with enhanced spatial resolution are achieved through the reconstruction of neutron events based on observations of individual photons emitted from the scintillator. The system demonstrates reduced background through neutron/gamma discrimination capabilities while maintaining sharpness across a large fields of view. In the measurements presented here, a spatial resolution of approximately 340 $$\mu$$ m was achieved using center-of-gravity photon cluster centroiding. We demonstrate the system’s capability for quantitatively determining isotopic distributions in various thin samples, as well as automatically reconstructing complex scenes with overlapping resonances from diverse samples. These results are obtained using standard data analysis tools, despite the relatively slow $$^{6}$$ LiF-ZnS:Ag scintillator, which may not be optimal for absorption resonance detection. The capabilities demonstrated here offer a valuable, versatile, and cost-effective solution for high spatial and temporal resolution, large field-of-view energy-resolved neutron imaging, with potential applications across various scientific and industrial domains.

A recent experimental study on a spent uranium dioxide (UO2) fuel sample from Belgium Reactor 3 identified a unique pair structure formed by the noble metal phase (NMP) and fission gas [xenon (Xe)] precipitate. However, the fundamental mechanism behind this structure remains unclear. The present study aims to provide an understanding of the interaction between five different metal precipitates [molybdenum (Mo), ruthenium (Ru), palladium (Pd), technetium (Tc), and rhodium (Rh)] and the Xe fission gas atoms in UO2, by using density functional theory (DFT) in combination with the Hubbard U correction to compute the formation energies involved. All DFT + U calculations were performed with occupation matrix control to ensure antiferromagnetic ordering of UO2. The calculated formation and binding energies of the Xe and solid fission products in the NMP reveal that these metal precipitates form stable pair structures with Xe. Notably, the formation energy of Xe–metal pairs is lower than that of the isolated single defects in all instances, with Pd and Mo showing the most favorable binding energy, likely accounting for the observed pair structure formation.

ZrO₂-coated CeO₂ nanospheres show an enhanced oxygen release rate compared to bare CeO₂, leading to improved performance of Pt-based three-way catalysts.

Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.

Uranium mononitride (UN) is a promising advanced nuclear fuel due to its high thermal conductivity and high fissile density. However, many aspects of its mechanical behavior, particularly at reactor-relevant conditions, remain unclear. In this study, molecular dynamics (MD) simulations were employed to investigate the deformation behavior and dislocation motion in UN. We found that the Kocevski potential predicts the principal slip system as 12⟨110⟩{110}, aligning with experimental data. On the other hand, the Tseplyaev potential predicts slip to primarily occur on 12⟨110⟩{111}. MD simulations of stress–strain behavior were used to estimate the nanoindentation hardness, revealing that the Kocevski potential accurately predicts hardness even though it fails to model dynamic plasticity. Complete dislocation mobility functions have been fitted for the edge and screw dislocations in both the thermally activated and phonon-drag regimes. The 300 K linear mobility of the edge dislocation using the Tseplyaev potential was found to be 817 Pa−1·s−1, whereas that of the screw dislocation using the Kocevski potential was found to be 4546 Pa−1·s−1. At intermediate stresses, we observed that the subsonic steady-state motion of the edge dislocation in UN is intermittently interrupted by velocity jumps, reaching the average sound velocity. Finally, the threshold Schmid stress is calculated as 179–197 MPa, which gives an upper-limit estimate of the uniaxial yield stress of polycrystalline UN of 548–603 MPa. These findings, including the fitted dislocation mobility function, provide essential input for future plasticity and dislocation dynamics models of nuclear fuels.

This study presents a novel multiphysics phase-field fracture model to analyze high-burnup uranium dioxide (UO2) fuel behavior under transient reactor conditions. Fracture is treated as a stochastic phase transition, which inherently accounts for the random microstructural effects that lead to variations in the value of fracture strength. Moreover, the model takes into consideration the effects of temperature and burnup on thermal conductivity. Therefore, the model is able to predict crack initiation, propagation, and complex morphologies in response to thermal gradients and stress distributions. Several simulations were conducted to investigate the effects of operational and transient conditions on fracture behavior and the resulting cracking patterns. High-burnup fuels exhibit reduced thermal conductivity, elevating temperature gradients and resulting in extensive radial and circumferential cracks. Transient heating rates and temperatures significantly affect fracture patterns, with higher heating rates generating steeper gradients and more irregular crack trajectories. This approach provides critical insights into fuel integrity during accident scenarios and supports the safety evaluation of extended burnup limits.

Flexible hybrid electronics (FHEs) combine flexible substrates with conventional electronic components, offering increased mechanical stability and reduced size and weight. Laser‐Induced Graphene (LIG) presents a novel approach for patterning flexible devices, characterized by reduced weight, flexibility, and ease of synthesis. This study demonstrates a new process utilizing copper‐plated LIG to create direct‐write, on‐demand flexible electronics. The method incorporates catalytically active Pd nanoparticles into the LIG structure, enabling selective copper‐plating to form circuits. This simplifies the process by eliminating sensitization steps and avoiding challenges of homogeneous deposition found in electroplating. The impact of laser fluence on LIG structure and plating behavior is systematically studied, identifying an optimal fluence of 168 J cm⁻² using a 7 W, 450 nm laser. Samples with this fluence achieve complete copper plating within 20 min, a sheet resistance of 149.9 mΩ □⁻¹, good adhesion, and durability across 10 000 bend cycles. An operational amplifier is produced on a flexible polyimide substrate, demonstrating the feasibility of this process for creating low‐cost FHEs. This research expands LIG applications and establishes the relationship between laser fluence and copper‐plating behavior, opening new opportunities for integrating LIG into flexible electronics.

Monte Carlo codes are naturally suited to act as computational kernels for ray tracing visualization applications because of the similarities between the ray tracing algorithm and the Monte Carlo neutral particle tracking algorithm. This paper presents a simplified ray tracing algorithm suitable for engineering applications, as well as optimizations to make the algorithm more amenable to computation on a GPU. This algorithm was implemented in a GPU-based ray tracing code called MantaRay. This code has been used as a backend kernel in MCVIZ, a real-time, interactive 3D visualization tool for MC21 models. MCVIZ+MantaRay allows for interactive rendering of extremely large models at 1024x768 resolution at framerates of 15 fps and greater.

Monte Carlo neutron transport is often seen as the standard for accurate neutronics simulation of nuclear reactors in steady-state. However, the time dependent equation includes time derivatives of flux and delayed neutron precursors which are difficult to model and tally. In this work, a high-order/low-order approach is adopted that uses the omega method to approximate the time derivatives as frequencies. While this scheme has been previously applied to prescribed transients, thermal feedback is now incorporated to provide a fully self-propagating Monte Carlo transient multiphysics solver which can be applied to transients of several seconds long. Such multiphysics problems are less straight-forward than prescribed transients that can be forced to be axially symmetric or otherwise artificially constrained. This work leverages tally derivatives, which are a Monte Carlo perturbation technique that can identify how a tally will change with respect to a small change in the system, to successfully predict thermal feedback and greatly improve accuracy in an axially non-symmetric, flow-initiated transient. Results show good agreement with a reference solution where very fine outer time steps are used for Monte Carlo calculations.

This work analyses activation calculations for dosimetry materials during a steady-state irradiation in the Transient Reactor Test (TREAT) reactor core. Hence, we developed a workflow based on the Monte Carlo code OpenMC alongside a custom depletion solver. The irradiation-induced activity as a function of time is computed, and several sensitivity studies are performed to evaluate uncertainty. This study has shown activity computations are sensitive to flux amplitude, irradiation time, atoms quantity and microscopic cross sections. Stochastic uncertainties have been propagated to evaluate the activity uncertainty for each dosimetry material. Most uncertainties are below our target of 3%, which demonstrates OpenMC as a powerful predictive and analysis tool. The precise results obtained through this newly developed computation scheme will be used in future experiments to characterize quantities of interest when operating the TREAT reactor in new configurations.

Alkali‐silica reaction (ASR) is an important degradation process that causes volumetric expansion and damage in concrete, and is affected significantly by the local temperature, moisture and stress conditions that often vary across the regions of a structure. Numerical simulation is essential to predict the progression and effects of ASR on the performance of structures. Because of the interactions between thermal and moisture transport and mechanical deformation, it is important for numerical models to represent all these physical phenomena and the coupling between them. Simulations of ASR in reinforced concrete (RC) structures are further complicated by the need to capture interactions between concrete and embedded reinforcing bars. This paper describes the implementation of a scalable, coupled‐physics ASR model for simulating RC structures and assesses the ability of that model to predict ASR‐induced expansion in recent laboratory tests on RC block and beam specimens. These laboratory tests and the simulation approach were selected because of their applicability to RC structural‐scale simulations. This validation study helps builds confidence the ability of this approach to model ASR expansion in large, complex RC structures, which is a current high‐priority need.

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set (n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

The complex multiscale and anisotropic nature of silicon carbide (SiC) ceramic matrix composite (CMC) makes it difficult to accurately model its performance in nuclear applications. The existing models for nuclear grade composite SiC do not account for the microstructural features and how these features can affect the thermal and structural behavior of the cladding and its anisotropic properties. In addition to the microstructural features, the properties of individual constituents of the composites and fiber tow architecture determine the bulk properties. Models for determining the relationship between the individual constituents’ properties and the bulk properties of SiC composites for nuclear applications are absent, although empirical relationships exist in the literature. Here, a hierarchical multiscale modeling approach was presented to address this challenge. This modular approach addressed this difficulty by dividing the various aspects of the composite material into separate models at different length scales, with the evaluated property from the lower-length-scale model serving as an input to the higher-length-scale model. The multiscale model considered the properties of various individual constituents of the composite material (fiber, matrix, and interphase), the porosity in the matrix, the fiber volume fraction, the composite architecture, the tow thickness, etc. By considering inhomogeneous and anisotropic contributions intrinsically, our bottom-up multiscale modeling strategy is naturally physics-informed, bridging constitutive law from micromechanics to meso-mechanics and structural mechanics. The effects that these various physical attributes and thermo-physical properties have on the composite’s bulk thermal properties were easily evaluated and demonstrated through the various analyses presented herein. Since silicon carbide fiber-reinforced SiC CMCs are also promising thermal–structural materials with a broad range of high-end technology applications beyond nuclear applications, we envision that the multiscale modeling method we present here may prove helpful in future efforts to develop and construct reinforced CMCs and other advanced composite nuclear materials, such as MAX phase materials, that can service under harsh environments of ultrahigh temperatures, oxidation, corrosion, and/or irradiation.

Performance of two distinct fuel systems, U-7Mo fuel in Zircaloy (Zry-4) cladding and U-10Mo fuel in aluminum (Al6061-O) cladding was studied. First, a mini plate with Zry-4 cladding from a previous irradiation experiment was evaluated via finite element analysis (FEA). By using the same plate geometry and irradiation conditions, another plate consisting of U-10Mo fuel and Al6061-O cladding was simulated. The results were then comparatively evaluated to explore the feasibility of employing Zry-4 as an alternative cladding. Simulations indicated the Zircaloy cladding plate would operate roughly 50 °C hotter as compared with the Al alloy cladding plate. Larger deformations in the thickness direction for the plate with Zry cladding were noted. It was observed that the postfabrication stresses in the fuel would be relieved quickly in the reactor, regardless of cladding type. Although the fuel stresses would still develop at reactor shutdown, the fuel would be stress-free during the entire irradiation period for both cladding types. At shutdown, the plate with Zry cladding would have higher stresses due to higher operating temperatures. Similarly, the stresses after shutdown are higher in the foil core for the plates with Zry cladding. The Al cladding plate would have higher plastic strains as compared with the Zry cladding plate. The Zry cladding plate is significantly stiffer, causing higher stresses in the fuel zone and at the interface. Overall, employing Zry as an alternate cladding is not expected to produce a more favorable thermomechanical performance as compared to the performance of an Al alloy cladding plate.

Machine learning interatomic potentials (MLIPs) have greatly enhanced molecular dynamics (MD) simulations, achieving near-first-principles accuracy in thermal conductivity studies. In this work, we reveal that this accuracy, observed in BAs and diamond at sub-Debye temperatures, stems from an accidental error cancelation: classical statistics overestimates specific heat while underestimating phonon lifetimes, balancing out in thermal conductivity predictions. However, this balance is disrupted when isotopes are introduced, leading MLIP-based MD to significantly underpredict thermal conductivity compared to experiments and quantum statistics-based Boltzmann transport equation. This discrepancy arises not from classical statistics affecting phonon–isotope scattering rates but from its impact on the interplay between phonon–isotope and phonon–phonon scattering in the normal scattering-dominated BAs and diamond. This work underscores the limitations of MLIP-based MD for thermal conductivity studies at sub-Debye temperatures.

Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.

A streamlined sample preparation method for nanomechanical testing is needed to improve the quality of specimens, reduce the cost, and increase the versatility of specimen fabrication. This work outlines an in-plane liftout focused ion beam (FIB) fabrication procedure to prepare electron-transparent specimens for in situ transmission electron microscopy (TEM) nanomechanical testing. Ion etching and electron backscatter diffraction (EBSD) techniques were used to lift out a [110] oriented grain from a neutron-irradiated bulk X-750 alloy. Careful control of voltages and currents ensured precision. Top surface thinning sweeps prevented resurfacing and redeposition while dog-bone geometries were shaped with a 1:4 gauge width-to-milling pattern diameter ratio. Nanotensile testing in the TEM with a picoindenter allowed for the estimation of an ultimate tensile strength of 2.41 GPa, and inspection revealed a high density of bubbles in the X-750 matrix. The proposed fabrication procedure is significant for preparing samples from radioactive materials, studying complex structures that are orientation-dependent, and analyzing desired planar areas.

The detection of GW170817/AT2017gfo inaugurated an era of multimessenger astrophysics, in which gravitational-wave and multiwavelength photon observations complement one another to provide unique insight into astrophysical systems. A broad theoretical consensus exists, in which the photon phenomenology of neutron star mergers largely rests upon the evolution of the small amount of matter left on bound orbits around the black hole or massive neutron star remaining after the merger. Because this accretion disk is far from inflow equilibrium, its subsequent evolution depends very strongly on its initial state, yet very little is known about how this state is determined. Using both snapshot and tracer particle data from a numerical relativity/MHD simulation of an equal-mass neutron star merger that collapses to a black hole, we show how gravitational forces arising in a nonaxisymmetric, dynamical spacetime supplement hydrodynamical effects in shaping the initial structure of the bound debris disk. The work done by hydrodynamical forces is ∼10 times greater than that due to time-dependent gravity. Although gravitational torques prior to remnant relaxation are an order of magnitude larger than hydrodynamical torques, their intrinsic sign symmetry leads to strong cancellation; as a result, hydrodynamical and gravitational torques have a comparable effect. We also show that the debris disk’s initial specific angular momentum distribution is sharply peaked at roughly the specific angular momentum of the merged neutron star’s outer layers, a few r _g c, and identify the regulating mechanism.

Wafer scale transition metal dichalcogenide films grown by MOCVD using two different chalcogen precursors are assessed for layer homogeneity and quality. These characteristics are then compared to electrical properties on the growth substrate.

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Capillary evolution at bicrystal UO₂ grain boundaries is characterized using in situ transmission electron microscopy. The discontinuous nature of the densification process, both particle rotation and axial strain, along with the large activation stress for densification support a hypothesis that grain boundary strain in UO₂ follows nucleation rate limited kinetics at low to intermediate stresses, that is, less than . The temperature dependence of the average activation stress for sintering agrees well with analysis of bulk sintering data and creep data reported within the literature when analyzed in the context of a grain boundary dislocation nucleation rate limited kinetic model.

Non-oxidative methane dehydro-aromatization reaction can co-produce hydrogen and benzene effectively on a molybdenum-zeolite based thermochemical catalyst, which is a very promising approach for natural-gas upgrading. However, the low methane conversion and aromatics selectivity and weak durability restrain the realistic application for industry. Here, a mechanism for enhancing catalysis activity on methane activation and carbon-carbon bond coupling has been found to promote conversion and selectivity simultaneously by adding platinum–bismuth alloy cluster to form a trimetallic catalyst on zeolite (Pt-Bi/Mo/ZSM-5). This bimetallic alloy cluster has synergistic interaction with molybdenum: the formed CH₃^* from Mo₂C on the external surface of zeolite can efficiently move on for C-C coupling on the surface of Pt-Bi particle to produce C₂ compounds, which are the key intermediates of oligomerization. This pathway is parallel with the catalysis on Mo inside the cage. This catalyst demonstrated 18.7% methane conversion and 69.4% benzene selectivity at 710 °C. With 95% methane/5% nitrogen feedstock, it exhibited robust stability with slow deactivation rate of 9.3% after 2 h and instant recovery of 98.6% activity after regeneration in hydrogen. The enhanced catalytic activity is strongly associated with synergistic interaction with Mo and ligand effects of alloys by extensive mechanism studies and DFT calculation.

Flexible and printed electronics have become increasingly popular as they make possible the production of flexible, low‐cost, multifunction devices that are unachievable through traditional manufacturing methods. The printed films are significantly impacted and thus limited by the existing ink production process. Herein, an alternate technique of generating high‐quality titanium dioxide (TiO₂) nanoparticle ink compatible with aerosol jet printing using laser ablation synthesis in solution (LASiS) is showcased. Dynamic light scattering data and transmission electron microscopy confirm the particle size distribution. UV–vis measurements are performed, and the Beer–Lambert relationship is used to determine the concentration of the generated ink. The inks generated at two different repetition rates are compared: 200 kHz and 1 MHz. X‐Ray diffraction analysis of the aerosol jet printed thin film post‐thermal sintering confirms the grain size and phase purity of the printed thin films. This work demonstrates the effectiveness of the LASiS technique in producing printable nanoparticle inks with little‐to‐no postprocessing.

Stress tests are developed that focus on anode catalyst layer degradation in proton exchange membrane electrolysis due to simulated start-stop operation. Ex situ testing indicates that repeated redox cycling accelerates catalyst dissolution, due to near-surface reduction and the higher dissolution kinetics of metals when cycling to high potentials. Similar results occur in situ, where a large decrease in cell kinetics (>70%) is found along with iridium migrating from the anode catalyst layer into the membrane. Additional processes are observed, however, including changes in iridium oxidation, the formation of thinner and denser catalyst layers, and platinum migration from the transport layer. Increased interfacial weakening is also found, adding to both ohmic and kinetic loss by adding contact resistances and isolating portions of the catalyst layer. Repeated shutoffs of the water flow further accelerate performance loss and increase the frequency of tearing and delamination at interfaces and within catalyst layers. These tests were applied to several commercial catalysts, where higher loss rates were observed for catalysts that contained ruthenium or high metal content. These results demonstrate the need to understand how operational stops occur, to identify how loss mechanisms are accelerated, and to develop strategies to limit performance loss.

Printed electronics have made remarkable progress in recent years and inkjet printing (IJP) has emerged as one of the leading methods for fabricating printed electronic devices. However, challenges such as nozzle clogging, and strict ink formulation constraints have limited their widespread use. To address this issue, a novel nozzle‐free printing technology is explored, which is enabled by laser‐generated focused ultrasound, as a potential alternative printing modality called Shock‐wave Jet Printing (SJP). Specifically, the performance of SJP‐printed and IJP‐printed bottom‐gated carbon nanotube (CNT) thin film transistors (TFTs) is compared. While IJP required ten print passes to achieve fully functional devices with channel dimensions ranging from tens to hundreds of micrometers, SJP achieved comparable performance with just a single pass. For optimized devices, SJP demonstrated six times higher maximum mobility than IJP‐printed devices. Furthermore, the advantages of nozzle‐free printing are evident, as SJP successfully printed stored and unsonicated inks, delivering moderate electrical performance, whereas IJP suffered from nozzle clogging due to CNT agglomeration. Moreover, SJP can print significantly longer CNTs, spanning the entire range of tube lengths of commercially available CNT ink. The findings from this study contribute to the advancement of nanomaterial printing, ink formulation, and the development of cost‐effective printable electronics.

U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

Gaseous fission products from nuclear fission reactions tend to form fission gas bubbles of various shapes and sizes inside nuclear fuel. The behavior of fission gas bubbles dictates nuclear fuel performances, such as fission gas release, grain growth, swelling, and fuel cladding mechanical interaction. Although mechanical understanding of the overall evolution behavior of fission gas bubbles is well known, lacking the quantitative data and high-level correlation between burnup/temperature and microstructure evolution blocks the development of predictive models and reduces the possibility of accelerating the qualification for new fuel forms. Historical characterization of fission gas bubbles in irradiated nuclear fuel relied on a simple threshold method working on low-resolution optical microscopy images. Advanced characterization of fission gas bubbles using scanning electron microscopic images reveals unprecedented details and extensive morphological data, which strains the effectiveness of conventional methods. This paper proposes a hybrid framework, based on digital image processing and deep learning models, to efficiently detect and classify fission gas bubbles from scanning electron microscopic images. The developed bubble annotation tool used a multitask deep learning network that integrates U-Net and ResNet to accomplish instance-level bubble segmentation. With limited annotated data, the model achieves a recall ratio of more than 90%, a leap forward compared to the threshold method. The model has the capability to identify fission gas bubbles with and without lanthanides to better understand the movement of lanthanide fission products and fuel cladding chemical interaction. Lastly, the deep learning model is versatile and applicable to the micro-structure segmentation of similar materials.

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

The carburizing supercritical CO₂ (sCO₂) environment limits the use of lower cost steels in the lower temperature (450–650°C) portions of the sCO₂ Brayton cycle because of concerns about internal carburization and embrittlement. Results on a ferritic–martensitic steel and conventional and advanced austenitic steels at 450–650°C in 30 MPa sCO₂ with and without 1% O₂ and 0.1% H₂O additions have indicated that sCO₂ environments will have lower maximum operating temperatures compared to steam plants. Pack Al and Cr coatings were evaluated at 650°C on T91 and 316H substrates and showed some benefit for up to 2000 h at 650°C, especially without impurities. However, characterization indicated Al₂O₃ was not formed and Cr‐rich carbides formed in the Cr coatings. With the addition of impurities in the sCO₂, the coatings were less protective at 650°C. Subsequent exposures at 600°C in sCO₂ showed similar behavior. Postexposure evaluations included measuring the bulk C content and room temperature tensile properties. Improvements were indicated but the tensile results were complicated by the high temperature pack coating process affecting the substrate properties.

Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO₂) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

Cloud microphysical processes are an important facet of atmospheric modeling, as they can control the initiation and rates of snowfall. Thus, parameterizations of these processes have important implications for modeling seasonal snow accumulation. We conduct experiments with the Weather Research and Forecasting (WRF V4.3.3) Model using three different microphysics parameterizations, including a sophisticated new scheme (ISHMAEL). Simulations are conducted for two cold seasons (2018 and 2019) centered on the Colorado Rockies’ ∼750-km² East River watershed. Precipitation efficiencies are quantified using a drying-ratio mass budget approach and point evaluations are performed against three NRCS SNOTEL stations. Precipitation and meteorological outputs from each are used to force a land surface model (Noah-MP) so that peak snow accumulation can be compared against airborne snow lidar products. We find that microphysical parameterization choice alone has a modest impact on total precipitation on the order of ±3% watershed-wide, and as high as 15% for certain regions, similar to other studies comparing the same parameterizations. Precipitation biases evaluated against SNOTEL are 15% ± 13%. WRF Noah-MP configurations produced snow water equivalents with good correlations with airborne lidar products at a 1-km spatial resolution: Pearson’s r values of 0.9, RMSEs between 8 and 17 cm, and percent biases of 3%–15%. Noah-MP with precipitation from the PRISM geostatistical precipitation product leads to a peak SWE underestimation of 32% in both years examined, and a weaker spatial correlation than the WRF configurations. We fall short of identifying a clearly superior microphysical parameterization but conclude that snow lidar is a valuable nontraditional indicator of model performance.

Proton exchange membrane fuel cells (PEMFCs) are expected to play a pivotal role in decarbonizing the transportation sector, and particularly heavy-duty vehicles (HDVs). However, improvements in durability are needed for PEMFCs to compete with state-of-the-art power sources for HDVs. Here, we examine how catalyst layer (CL) cracks that are engineered affect the CL durability by using patterned silicon templates to control the CL crack density at the micrometer scale. Electrochemical analyses show that the initial PEMFC performance is relatively unaffected by crack density, but the performance after durability testing was strongly affected. Specifically, CLs with high crack density showed higher performance relative to CLs without cracks after application of a carbon corrosion accelerated stress test. Electrochemical analyses coupled with X-ray computed tomography and scanning transmission electron microscopy with energy dispersive X-ray spectroscopy showed that the cracks provide shorter oxygen diffusion pathways to reaction sites, leading to decreased oxygen transport resistance. Additionally, we observed that the catalyst durability is unaffected by cracks. Our results provide a mechanistic explanation of the role of cracks in CL durability.

With increasing concerns about global warming, the push for sustainable and eco-friendly fuels is accelerating. Propane, recognized as liquefied petroleum gas or LPG, has garnered research interest as an alternative fuel due to its notable advantages, including a high-octane rating, reduced greenhouse gas emissions, and potential cost-effectiveness. However, to realize its full potential as an alternative fuel it is essential to develop catalysts that efficiently handle emissions at low temperatures. In our research, we investigated three distinct palladium (Pd)-based three-way catalyst (TWC) formulations (PdRh, Pd-only, and Pd-OSC) to investigate the influence of typical TWC components rhodium (Rh) and oxygen storage components (OSC) in exhaust scenarios relevant to propane-fueled engines. Among these, the formulation containing oxygen storage components (Pd-OSC) showed the highest reactivity for both NO and C3H8 while minimizing performance degradation from hydrothermal aging (HTA). Notably, the temperature of 50% conversion (T50) for propane in the Pd-OSC fresh and HTA sample was lower by 30 °C and 13 °C, respectively, compared to the Pd-only sample, highlighting the role of oxygen storage materials in enhancing catalyst performance, even without dithering. Additionally, N2 physisorption showed that the Pd-OSC sample has a higher surface area and increased pore volume. This underscores the idea that OSC materials not only augment the catalyst’s porosity but also optimize reactant accessibility to active sites, thus elevating catalytic efficiency. In addition to evaluating performance, we further explored the performance and characteristics of the catalysts using catalytic probe reactions, such as water–gas shift and steam reforming reactions.

In this study, a computational fluid dynamics (CFD) model was developed to model the motion of a solid cold cap in a waste glass melter. Forced convection bubblers at the base of the melter release air into the molten glass, which forms large bubbles that travel upward to the cold cap and augment heat transfer from the glass to the cold cap. The CFD model employs the Navier–Stokes equations to solve for the fluctuating flowfield using a rigid body motion dynamic fluid body interaction module. This allows for movement of the floating body in response to the bubbling forces calculated at each time-step. The heat flux delivered to the cold cap by the convective bubbling is studied as a function of the normalized bubbling rate. Results for the moving cold cap are compared with the computed heat flux trends for a stationary cold cap. The heat flux delivered to the cold cap from the molten glass is 25% higher for the case with the moving cold cap as opposed to a stationary cold cap.

Many studies have found that neutron star mergers leave a fraction of the stars’ mass in bound orbits surrounding the resulting massive neutron star or black hole. This mass is a site of r-process nucleosynthesis and can generate a wind that contributes to a kilonova. However, comparatively little is known about the dynamics determining its mass or initial structure. Here we begin to investigate these questions, starting with the origin of the disk mass. Using tracer particle as well as discretized fluid data from numerical simulations, we identify where in the neutron stars the debris came from, the paths it takes in order to escape from the neutron stars’ interiors, and the times and locations at which its orbital properties diverge from those of neighboring fluid elements that end up remaining in the merged neutron star.

In this Letter, we report that the fourth-order interatomic force constants (4th-IFCs) are significantly sensitive to the energy surface roughness of exchange-correlation (XC) functionals in density functional theory calculations. This sensitivity, which is insignificant for the second- (2nd-) and third-order (3rd-) IFCs, varies for different functionals in different materials and can cause misprediction of thermal conductivity by several times of magnitude. As a result, when calculating the 4th-IFCs using the finite difference method, the atomic displacement needs to be taken large enough to overcome the energy surface roughness, in order to accurately predict phonon lifetime and thermal conductivity. We demonstrate this phenomenon on a benchmark material (Si), a high-thermal conductivity material (BAs), and a low thermal conductivity material (NaCl). For Si, we find that the LDA, PBE, and PBEsol XC functionals are all smooth to the 2nd- and 3rd-IFCs but all rough to the 4th-IFCs. This roughness can lead to a prediction of nearly one order of magnitude lower thermal conductivity. For BAs, all three functionals are smooth to the 2nd- and 3rd-IFCs, and only the PBEsol XC functional is rough for the 4th-IFCs, which leads to a 40% underestimation of thermal conductivity. For NaCl, all functionals are smooth to the 2nd- and 3rd-IFCs but rough to the 4th-IFCs, leading to a 70% underprediction of thermal conductivity at room temperature. With these observations, we provide general guidance on the calculation of 4th-IFCs for an accurate thermal conductivity prediction.

SOEC is a promising H₂ generation technology for mitigating climate change. Novel material design and optimization strategies, such as oxygen vacancy chemistries, can enhance SOEC efficiency. In this study, Monte Carlo-ReaxFF and eReaxFF simulations were used to study oxygen vacancies (O_v) and electron migration in BZY20 solid oxide material. Our results shows that O_v migrate towards the surface, increasing surface O_v concentration by 10%. Yttrium restricts electron mobility and functions as an electron trapping site, while Zr accelerates electron mobility and migration. These insights could improve solid-state electrolytes’ electrochemical performance in renewable energy applications.

Metallic fuels have seen increased interest for future sodium fast reactors due to their material properties: high thermal conductivities and advantageous neutronic properties allow for greater fission densities. One drawback to typical metallic fuels is zirconium redistribution, which impacts this advantageous material and its neutronic properties. Unfortunately, the processes behind zirconium migration behavior are understood using first principles, so before these fuels are implemented in future fast reactors, characterization and fuel qualification regimes must be completed. These activities can be supported through the use of robust modeling using the most accurate empirical models currently available to fuel researchers around the world. The tool that allows researchers to model this complex coupled thermo-mechanical behavior and nuclear properties is BISON. Additionally, BISON model parameters need to be compared against PIE measurements. The current work utilizes two fuel pins from EBR-II experiment X441 to optimize various model parameters, including porosity correction factor, thermal conductivity, phase transition temperature, and diffusion coefficient multipliers, before implementing the final model for seven fuel pins with differing characteristics. To properly evaluate the BISON simulations, the results are compared to PIE metallography data for each fuel pin, to ensure the zirconium redistribution is properly reflected in the simulation results. Six out of seven analyzed fuel pins demonstrate good agreement between the metallography images and BISON results, showing alignment of the Zr-rich, Zr-depleted, and moderately Zr-enriched zones at various axial heights along the fuel pins. Further work is needed to refine the model parameters for general pin use.

Nanostructured steels are expected to have enhanced irradiation tolerance and improved strength. However, they suffer from poor microstructural stability at elevated temperatures. In this study, Fe–21Cr–5Al–0.026C (wt%) Kanthal D (KD) alloy belonging to a class of (FeCrAl) alloys considered for accident‐tolerant fuel cladding in light‐water reactors is nanostructured using two severe plastic deformation techniques of equal‐channel angular pressing (ECAP) and high‐pressure torsion (HPT), and their thermal stability between 500–700 °C is studied and compared. ECAP KD is found to be thermally stable up to 500 °C, whereas HPT KD is unstable at 500 °C. Microstructural characterization reveals that ECAP KD undergoes recovery at 550 °C and recrystallization above 600 °C, while HPT KD shows continuous grain growth after annealing above 500 °C. Enhanced thermal stability of ECAP KD is from significant fraction (>50%) of low‐angle grain boundaries (GBs) (misorientation angle 2–15°) stabilizing the microstructure due to their low mobility. Small grain sizes, a high fraction (>80%) of high‐angle GBs (misorientation angle >15°) and accordingly a large amount of stored GB energy, serve as the driving force for HPT KD to undergo grain growth instead of recrystallization driven by excess stored strain energy.

Additive manufacturing techniques are being used more and more to perform the precise fabrication of engineering components with complex geometries. The heterogeneity of additively manufactured microstructures deteriorates the mechanical integrity of products. In this paper, we printed AISI 316L stainless steel using the additive manufacturing technique of laser metal deposition. Both single-phase and dual-phase substructures were formed in the grain interiors. Electron backscatter diffraction and energy-dispersive X-ray spectroscopy indicate that Si, Mo, S, Cr were enriched, while Fe was depleted along the substructure boundaries. In situ micro-compression testing was performed at room temperature along the [001] orientation. The dual-phase substructures exhibited lower yield strength and higher Young’s modulus compared with single-phase substructures. Our research provides a fundamental understanding of the relationship between the microstructure and mechanical properties of additively manufactured metallic materials. The results suggest that the uneven heat treatment in the printing process could have negative impacts on the mechanical properties due to elemental segregation.

Improving utilization, performance, and stability of low iridium (Ir)-loaded anodes is a key goal to enable widespread adoption of polymer electrolyte membrane water electrolysis (PEMWE) for clean hydrogen production. A potential limitation is high ionic or electronic resistance of the anode catalyst layer, which leads to poor catalyst utilization, increased voltage losses, and high local overpotentials that can accelerate degradation. While catalyst layer resistance is relatively well-understood in fuel cells and other porous electrode systems, characterization of these effects is not as well established in PEMWE research. Here we present in-situ methods for measuring catalyst layer resistance in electrolysis cells using a non-faradaic H₂/H₂O condition as well as methods for calculating the associated voltage losses. These methods are applied to anode catalyst layers based on IrO₂ nanoparticles as well as dispersed nano-structured thin film (NSTF) Ir catalysts. Trends with anode catalyst loading and interactions between the porous transport layer and catalyst layer are investigated for IrO₂ anodes. Post-mortem microscopic analysis of durability-tested anodes is also presented, showing uneven degradation of the catalyst layer caused by catalyst layer resistance.

The boron-vacancy spin defect ($${\,{{\mbox{V}}}}_{{{\mbox{B}}}\,}^{-}$$ V B − ) in hexagonal boron nitride (hBN) has a great potential as a quantum sensor in a two-dimensional material that can directly probe various external perturbations in atomic-scale proximity to the quantum sensing layer. Here, we apply first-principles calculations to determine the coupling of the $${\,{{\mbox{V}}}}_{{{\mbox{B}}}\,}^{-}$$ V B − electronic spin to strain and electric fields. Our work unravels the interplay between local piezoelectric and elastic effects contributing to the final response to the electric fields. The theoretical predictions are then used to analyse optically detected magnetic resonance (ODMR) spectra recorded on hBN crystals containing different densities of $${\,{{\mbox{V}}}}_{{{\mbox{B}}}\,}^{-}$$ V B − centres. We prove that the orthorhombic zero-field splitting parameter results from local electric fields produced by surrounding charge defects. This work paves the way towards applications of $${\,{{\mbox{V}}}}_{{{\mbox{B}}}\,}^{-}$$ V B − centres for quantitative electric field imaging and quantum sensing under pressure.

This study involved a Reynolds-averaged Navier-Stokes- (RANS-) based computational fluid dynamics (CFD) analysis of the 37-pin wire-wrapped fuel bundle of the PNC Plant dynamics test loop (PLANDTL) facility. Previously, mainly the hydrodynamic phenomena of the wire-wrapped fuel bundle were analyzed, but the present study additionally included heat transfer analysis through conjugate heat transfer. The main purpose of the study was to benchmark the experimental data of the PLANDTL 37-pin wire-wrapped fuel bundle to investigate the heat transfer phenomena. In addition, the aim was to verify the accuracy of the RANS-based CFD analysis method using the STAR-CCM+ simulation software in comparison with the experimental data. The grid used for verification was an innovative grid system consisting of hexahedra using Fortran-based code. The development of the RANS-based CFD methodology included grid sensitivity analysis, turbulence model sensitivity analysis, and turbulent Prandtl number sensitivity analysis. Information on the temperature, mass flow rate, and area of the CFD results for each subchannel was provided for the top of the heated section and is expected to serve as a reference for future studies aiming to perform the validation and verification of a PLANDTL facility. In addition, the dependence of the peak temperature on the azimuth angle of each pin was analyzed.

Creating pathways to renewable fuels, chemicals, and materials through improved catalyst formulations and integrated process development.

It has been 49 years since the last discovery of a new virus family in the model yeast Saccharomyces cerevisiae. A large-scale screen to determine the diversity of double-stranded RNA (dsRNA) viruses in S. cerevisiae has identified multiple novel viruses from the family Partitiviridae that have been previously shown to infect plants, fungi, protozoans, and insects. Most S. cerevisiae partitiviruses (ScPVs) are associated with strains of yeasts isolated from coffee and cacao beans. The presence of partitiviruses was confirmed by sequencing the viral dsRNAs and purifying and visualizing isometric, non-enveloped viral particles. ScPVs have a typical bipartite genome encoding an RNA-dependent RNA polymerase (RdRP) and a coat protein (CP). Phylogenetic analysis of ScPVs identified three species of ScPV, which are most closely related to viruses of the genus Cryspovirus from the mammalian pathogenic protozoan Cryptosporidium parvum. Molecular modeling of the ScPV RdRP revealed a conserved tertiary structure and catalytic site organization when compared to the RdRPs of the Picornaviridae. The ScPV CP is the smallest so far identified in the Partitiviridae and has structural homology with the CP of other partitiviruses but likely lacks a protrusion domain that is a conspicuous feature of other partitivirus particles. ScPVs were stably maintained during laboratory growth and were successfully transferred to haploid progeny after sporulation, which provides future opportunities to study partitivirus-host interactions using the powerful genetic tools available for the model organism S. cerevisiae.

A new transient interhalogen species (ICl^•−) has been identified in electron pulse irradiated molten chloride salt mixtures. This species has significant implications for the transport of fission-product iodine in molten salt reactor environments.

In the quest of new materials that can withstand severe irradiation and mechanical extremes for advanced applications (e.g. fission & fusion reactors, space applications, etc.), design, prediction and control of advanced materials beyond current material designs become paramount. Here, through a combined experimental and simulation methodology, we design a nanocrystalline refractory high entropy alloy (RHEA) system. Compositions assessed under extreme environments and in situ electron-microscopy reveal both high thermal stability and radiation resistance. We observe grain refinement under heavy ion irradiation and resistance to dual-beam irradiation and helium implantation in the form of low defect generation and evolution, as well as no detectable grain growth. The experimental and modeling results—showing a good agreement—can be applied to design and rapidly assess other alloys subjected to extreme environmental conditions.

Wireless systems are an integral part of aviation. Apart from their apparent use in air-to-ground communication, wireless systems play a crucial role in avionic functions including navigation and landing. An interference-free wireless environment is therefore critical for the uninterrupted operation and safety of an aircraft. Hence, there is an urgency for airport facilities to acquire the capability to continuously monitor aviation frequency bands for real-time detection of interference and anomalies. To meet this critical need, we design and build AviSense, an SDR-based real-time , versatile system for monitoring aviation bands. AviSense detects and characterizes signal activities to enable practical and effective anomaly detection. We identify and tackle the challenges posed by a diverse set of critical aviation bands and technologies. We evaluate our methodology with real-world aviation signal measurements and two custom datasets of anomalous signals. We find that our signal classification capability achieves a true positive rate of ∼99%, with few exceptions, and a false positive rate of less than 4%. We also demonstrate that AviSense can effectively distinguish between different types of anomalies. We build and evaluate a prototype implementation of AviSense that supports distributed monitoring.

Aggregates of organic dyes that exhibit excitonic coupling have a wide array of applications, including medical imaging, organic photovoltaics, and quantum information devices. The optical properties of a dye monomer, as a basis of dye aggregate, can be modified to strengthen excitonic coupling. Squaraine (SQ) dyes are attractive for those applications due to their strong absorbance peak in the visible range. While the effects of substituent types on the optical properties of SQ dyes have been previously examined, the effects of various substituent locations have not yet been investigated. In this study, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) were used to investigate the relationships between SQ substituent location and several key properties of the performance of dye aggregate systems, namely, difference static dipole (Δd), transition dipole moment (μ), hydrophobicity, and the angle (θ) between Δd and μ. We found that attaching substituents along the long axis of the dye could increase μ while placement off the long axis was shown to increase Δd and reduce θ. The reduction in θ is largely due to a change in the direction of Δd as the direction of μ is not significantly affected by substituent position. Hydrophobicity decreases when electron-donating substituents are located close to the nitrogen of the indolenine ring. These results provide insight into the structure–property relationships of SQ dyes and guide the design of dye monomers for aggregate systems with desired properties and performance.

The multiphysics object-oriented simulation environment (moose) is a code package that couples a variety of physics modules, allowing for highly accessible multiphysics simulations. The physics modules include a finite element Navier–Stokes (N–S) module that is designed to solve laminar fluid dynamics problems. The usage of this module in multiple recent studies coupled with the growing interest in moose for usage in nonlight water reactor safety studies by the Nuclear Regulatory Commission (NRC) prompted the authors to investigate the computational fluid dynamics capabilities of moose. A two-dimensional laminar flow past a circular cylinder scenario is simulated in the moose framework to investigate the effectiveness of the N–S module. Simulations assumed an unsteady laminar flow with a Reynolds number of 200. To verify the results from moose, similar simulations were conducted using the well-utilized simulation of turbulent flow in arbitrary regions—computational continuum mechanics C++ (star-ccm+) finite volume code. Results from both codes are also compared to some results from literature. Velocity and pressure profiles of both transient simulations were compared. The numerical and input errors in moose are also visualized with contour plots to qualitatively understand the evolution of the errors across time and space. The comparisons between moose and star-ccm+ showed nearly perfect agreement between the codes for velocity and pressure, especially after the development of the vortex street in later time-steps. The force coefficients showed excellent agreement after the development of the vortex street, but demonstrated notable discrepancies prior to the vortex street development, which is likely due to how each code simulated the approach to the vortex street in earlier time-steps.

The generation of accurate kinetic parameters such as mean generation time Λ and effective delayed neutron fraction β_eff via Monte Carlo codes is established. Employing these in downstream deterministic codes warrants another step to ensure no additional error is introduced by the low-order transport operator when computing forward and adjoint fluxes for bilinear weighting of these parameters. Another complexity stems from applying superhomogenization (SPH) equivalence in non-fundamental mode approximations, where reference and low-order calculations rely on a 3D full core model. In these cases, SPH factors can optionally be computed for only part of the geometry while preserving reaction rates and K-effective, but the impact of such approximations on kinetics parameters has not been thoroughly studied. This paper aims at studying the preservation of bilinearly-weighted quantities in the Serpent–Griffin calculation procedure. Diffusion and transport evaluations of IPEN/MB-01, Godiva, and Flattop were carried out with the Griffin reactor physics code, testing available modeling options using Serpent-generated multigroup cross sections and equivalence data. Verifying Griffin against Serpent indicates sensitivities to multigroup energy grid selection and regional application of SPH equivalence, introducing significant errors; these were demonstrated to be reduced through the use of a transport method together with a finer energy grid.

Dye molecules, arranged in an aggregate, can display excitonic delocalization. The use of DNA scaffolding to control aggregate configurations and delocalization is of research interest. Here, we applied Molecular Dynamics (MD) to gain an insight on how dye–DNA interactions affect excitonic coupling between two squaraine (SQ) dyes covalently attached to a DNA Holliday junction (HJ). We studied two types of dimer configurations, i.e., adjacent and transverse, which differed in points of dye covalent attachments to DNA. Three structurally different SQ dyes with similar hydrophobicity were chosen to investigate the sensitivity of excitonic coupling to dye placement. Each dimer configuration was initialized in parallel and antiparallel arrangements in the DNA HJ. The MD results, validated by experimental measurements, suggested that the adjacent dimer promotes stronger excitonic coupling and less dye–DNA interaction than the transverse dimer. Additionally, we found that SQ dyes with specific functional groups (i.e., substituents) facilitate a closer degree of aggregate packing via hydrophobic effects, leading to a stronger excitonic coupling. This work advances a fundamental understanding of the impacts of dye–DNA interactions on aggregate orientation and excitonic coupling.

Ni-SiOC nanocomposites maintain crystal-amorphous dual-phase nanostructures after high-temperature annealing at different temperatures (600 °C, 800 °C and 1000 °C), while the feature sizes of crystal Ni and amorphous SiOC increase with the annealing temperature. Corresponding to the dual-phase nanostructures, Ni-SiOC nanocomposites exhibit a high strength and good plastic flow stability. In this study, we conducted a He implantation in Ni-SiOC nanocomposites at 300 °C by in-situ transmission electron microscope (TEM) irradiation test. In-situ TEM irradiation revealed that both crystal Ni and amorphous SiOC maintain stability under He irradiation. The 600 °C annealed sample presents a better He irradiation resistance, as manifested by a smaller He-bubble size and lower density. Both the grain boundary and crystal-amorphous phase boundary act as a sink to absorb He and irradiation-induced defects in the Ni matrix. More importantly, amorphous SiOC ceramic is immune to He irradiation damage, contributing to the He irradiation resistance of Ni alloy.

Simulations were conducted using the BISON fuel performance code on an automated process to read initial and operating conditions from two databases—the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor Materials Information System (IMIS) database. These databases contain metallic fuel data from the Experimental Breeder Reactor-II (EBR-II) and the Fast Flux Test Facility (FFTF). The work demonstrates use of an integrated framework to access EBR-II fuel pin data for evaluating fuel performance models contained within BISON to predict fuel performance of next-generation metallic fuel systems. Between IMIS and FIPD, there is enough information to conduct 1,977 unique EBR-II metallic fuel pin histories from 29 different experiments, and 338 pins from FFTF MFF-3 and MFF-5 with varying levels of details between the two databases. Each of these fuel performance histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures, and new model developments in BISON since the initial demonstration of this integrated framework. Fission gas release (FGR), cumulative damage fraction, fuel axial swelling, FCCI wastage thickness, cladding profilometry, and burnup were all simulated in BISON and compared to post-irradiation examination (PIE) results to evaluate BISON fuel performance modeling. Implementation of new fuel performance models into a generic BISON input file coupled with IMIS and FIPD yielded results with a better representation of physics than the initial evaluation of the integrated framework. Cladding profilometry, FGR, and fuel axial swelling were found to be in good agreement with PIE measurements for most of the pins simulated. The chosen mechanical contact solver was found to significantly impact the axial fuel swelling and cladding strain predictions when used in conjunction with the U-Pu-Zr hot-pressing model since it bound the fuel to prevent further swelling and increased hydrostatic stresses. This work suggests that fuel performance modeling in BISON under steady-state conditions represents the PIE data well and should be reassessed when new PIE data become available in IMIS and FIPD databases and when improved physical models to better capture fuel performance are added to BISON.

Growth of wurtzite ScxAl1−xN (x < 0.23) by plasma-assisted molecular-beam epitaxy on c-plane GaN at high temperatures significantly alters the extracted lattice constants of the material due to defects likely associated with remnant phases. In contrast, ScAlN grown below a composition-dependent threshold temperature exhibits uniform alloy distribution, reduced defect density, and atomic-step surface morphology. The c-plane lattice constant of this low-temperature ScAlN varies with composition as expected from previous theoretical calculations and can be used to reliably estimate alloy composition. Moreover, lattice-matched Sc0.18Al0.82N/GaN multi-quantum wells grown under these conditions display strong and narrow near-infrared intersubband absorption lines that confirm advantageous optical and electronic properties.

Increased absorption of optical materials arising from exposure to ionizing radiation must be accounted for to accurately analyze laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on two examples that mimic the diagnostics placed within novel nuclear reactor designs. The analysis is performed on LIBS data measured with 1% Xe gas in an ambient He environment and 1% Eu in a molten LiCl-KCl matrix, along with the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. Significant changes in the number of laser shots required to reach a 3σ detection level are observed for the Eu data, increasing by two orders of magnitude after exposure to a 1.7 × 1017 n/cm2 neutron fluence. For all cases examined, the spectral dependence of absorption results in the introduction of systematic errors. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation leads to statistically significant changes in the temperatures calculated from the Xe II lines and Eu II lines, lowering them from 8000 ± 610 K to 6900 ± 810 K and from 15,800 ± 400 K to 7200 ± 800 K, respectively, for exposure to the 1.7 × 1017 n/cm2 fluence. The temperature range required for a 95% confidence interval for the calculated temperature is also broadened. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation attenuation is relatively small, or through analysis using the iterative Saha–Boltzmann method.