Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The table presented below suggests the basic information that should be covered in a facility NMAC Plan for an NMAC program that is designed for nuclear security. The topics are appropriate for and should be addressed by all facilities in their NMAC Plans. They are appropriate for NMAC Plans for nuclear power plants, research reactors, fuel manufacturing facilities, facilities that produce medical isotopes, and other facilities. The difference is in the intensity with which the various measures are applied and the thoroughness of the description of the application (i.e., the program requirements). The robustness of a facility NMAC program and the content of its NMAC Plan should be graded in accordance with the type of facility and the category of its nuclear material.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This report summarizes the international collaborations conducted by Sandia funded by the US Department of Energy Office (DOE) of Nuclear Energy (DOE-NE) Spent Fuel and Waste Science & Technology (SFWST) as part of the Sandia National Laboratories Salt R&D and Salt International work packages. This report satisfies the level-three milestone M3SF-23SN010303062. Several stand-alone sections make up this summary report, each completed by the participants. The sections discuss granular salt reconsolidation (KOMPASS), engineered barriers (RANGERS), numerical model comparison (DECOVALEX) and an NEA Salt Club working group on the development of scenarios as part of the performance assessment development process. Finally, we summarize events related to the US/German Workshop on Repository Research, Design and Operations.

Motion primitives allow for application of discrete search algorithms to rapidly produce trajectories in complex continuous space. The maneuver automaton (MA) provides an elegant formulation for creating a primitive library based on trims and maneuvers. However, performance is fundamentally limited by the contents of the primitive library. If the library is too sparse, performance can be poor in terms of path cost, whereas a library that is too large can increase run time. This work outlines new methods for using genetic algorithms to prune a primitive library. The proposed methods balance the path cost and planning time while maintaining the reachability of the MA. The genetic algorithm in this paper evaluates and mutates populations of motion primitive libraries to optimize both objectives. Here, we illustrate the performance of these methods with a simulated study using a nonlinear medium-fidelity F-16 model. We optimize a library with the presented algorithm for obstacle-free navigation and a nap-of-the-Earth navigation task. In the obstacle-free navigation task, we show a tradeoff of a 10.16% higher planning cost for a 96.63% improvement in run time. In the nap-of-the-Earth task, we show a tradeoff of a 9.712% higher planning cost for a 92.06% improvement in run time.

Understanding and controlling nanoscale interface phenomena, such as band bending and secondary phase formation, is crucial for electronic device optimization. In granular metal (GM) studies, where metal nanoparticles are embedded in an insulating matrix, the importance of interface phenomena is frequently neglected. Here, we demonstrate that GMs can serve as an exemplar system for evaluating the role of secondary phases at interfaces through a combination of x-ray photoemission spectroscopy (XPS) and electrical transport studies. We investigated SiN_x as an alternative to more commonly used oxide-insulators, as SiN_x-based GMs may enable high temperature applications when paired with refractory metals. Comparing Co-SiN_x and Mo-SiN_x GMs, we found that, in the tunneling-dominated insulating regime, Mo-SiN_x had reduced metal-silicide formation and orders-of-magnitude lower conductivity. XPS measurements indicate that metal-silicide and metal-nitride formation are mitigatable concerns in Mo-SiN_x. Given the metal-oxide formation seen in other GMs, SiN_x is an appealing alternative for metals that readily oxidize. Furthermore, SiN_x provides a path to metal-nitride nanostructures, potentially useful for various applications in plasmonics, optics, and sensing.

Recent work on atomic-precision dopant incorporation technologies has led to the creation of both boron and aluminum δ -doped layers in silicon with densities above the solid solubility limit. We use density functional theory to predict the band structure and effective mass values of such δ layers, first modeling them as ordered supercells. Structural relaxation is found to have a significant impact on the impurity band energies and effective masses of the boron layers, but not the aluminum layers. However, disorder in the δ layers is found to lead to a significant flattening of the bands in both cases. We calculate the local density of states and doping potential for these δ -doped layers, demonstrating that their influence is highly localized with spatial extents at most 4 nm. We conclude that acceptor δ -doped layers exhibit different electronic structure features dependent on both the dopant atom and spatial ordering. This suggests prospects for controlling the electronic properties of these layers if the local details of the incorporation chemistry can be fine-tuned.

While radiation is known to degrade AlGaN/GaN high-electron-mobility transistors (HEMTs), the question remains on the extent of damage governed by the presence of an electrical field in the device. In this study, we induced displacement damage in HEMTs in both ON and OFF states by irradiating with 2.8 MeV Au4+ ion to fluence levels ranging from 1.72 × 10 10 to 3.745 × 10 13 ions cm−2, or 0.001-2 displacement per atom (dpa). Electrical measurement is done in situ, and high-resolution transmission electron microscopy (HRTEM), energy dispersive x-ray (EDX), geometrical phase analysis (GPA), and micro-Raman are performed on the highest fluence of Au4+ irradiated devices. The selected heavy ion irradiation causes cascade damage in the passivation, AlGaN, and GaN layers and at all associated interfaces. After just 0.1 dpa, the current density in the ON-mode device deteriorates by two orders of magnitude, whereas the OFF-mode device totally ceases to operate. Moreover, six orders of magnitude increase in leakage current and loss of gate control over the 2-dimensional electron gas channel are observed. GPA and Raman analysis reveal strain relaxation after a 2 dpa damage level in devices. Significant defects and intermixing of atoms near AlGaN/GaN interfaces and GaN layer are found from HRTEM and EDX analyses, which can substantially alter device characteristics and result in complete failure.

Experiments with trapped ions and neutral atoms typically employ optical modulators in order to control the phase, frequency, and amplitude of light directed to individual atoms. These elements are expensive, bulky, consume substantial power, and often rely on free-space I/O channels, all of which pose scaling challenges. To support many-ion systems like trapped-ion quantum computers or miniaturized deployable devices like clocks and sensors, these elements must ultimately be microfabricated, ideally monolithically with the trap to avoid losses associated with optical coupling between physically separate components. In this work we design, fabricate, and test an optical modulator capable of monolithic integration with a surface-electrode ion trap. These devices consist of piezo-optomechanical photonic integrated circuits configured as multi-stage Mach-Zehnder modulators that are used to control the intensity of light delivered to a single trapped ion on a separate chip. We use quantum tomography employing hundreds of multi-gate sequences to enhance the sensitivity of the fidelity to the types and magnitudes of gate errors relevant to quantum computing and better characterize the performance of the modulators, ultimately measuring single qubit gate fidelities that exceed 99.7%.

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. In this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

The Sandia Mechanics Challenge (SMC) provides the solid-mechanics community a forum for assessing its ability to predict mechanical behavior in structures and materials through a blind, round-robin format. Computationalists are asked to predict the behavior of an unfamiliar geometry given experimental calibration data, their predictions are compared to experimental measurements of the SMC scenario, and then the participants assess and compare their approaches, documenting their findings. The SMC broadens the scope of Sandia-hosted benchmarking problems that previously focused on ductile failure through the Sandia Fracture Challenges, enabling an enduring, community-wide self-assessment of predictive capabilities for a variety of mechanics topics. The SMC is part of the Structural Reliability Partnership, which offers other benchmarking challenges hosted by several participating institutions.

This work explores the influence of blend composition, network architecture, and hydrogen bonding on the material properties of crosslinked epoxy networks, focusing on the glass transition temperature (Tg) and Young’s modulus (Y). We used coarse-grained molecular dynamics simulations to simulate varying compositions of stiff and flexible components in epoxy monomer blends with varying excess of curative. We find that, without hydrogen bonding, networks of any composition show a monotonically increasing Tg with decreasing excess curative, consistent with theory. In contrast, we find that when hydrogen bonding is introduced, the binary blend networks show significant enhancement in Tg for lightly crosslinked systems. This result contributes to an explanation of the anomalous Tg behavior observed experimentally in these systems. We further find that Y is generally enhanced by hydrogen bonds, especially below Tg, demonstrating that hydrogen bonding has a significant influence on mechanical properties and can allow access to other desirable dynamic behavior, especially self-healing.

Researchers have the potential to be exposed to a wide variety of hazards inherent to the equipment they use and maintain. When equipment does not function as expected, researchers sometimes reach out to their vendors for assistance. Early diagnostic or troubleshooting interactions between researcher and vendor are often conducted over the telephone and can lead to researchers performing work outside of their area of expertise and exposure to unknown hazards. This type of interaction significantly contributed to an incident where during diagnostic activities a researcher accidentally contacted, and discharged, a capacitor in an X-ray diffraction instrument. While this incident did not produce a serious injury, if the capacitor discharge path had occurred hand-to-hand across the heart, a serious injury may have been possible.

We report significantly enhanced device performance in long wavelength interband cascade lasers (ICLs) by employing a recently proposed innovative quantum well (QW) active region containing strained InAsP layers. These ICLs were able to operate at wavelengths near 14.4 μm, the longest ever demonstrated for III-V interband lasers, implying great potential of ICLs to cover an even wider wavelength range. Also, by applying the aforesaid QW active region configuration on ICLs at relatively short wavelengths, ICLs were demonstrated at a low threshold current density (e.g., 13 A/cm2 at 80 K) and at temperatures up to 212 K near 12.4 μm, more than 50 K higher than the previously reported ICLs with the standard W-shape QW active region at similar wavelengths. This suggests that the QW active region with InAsP layers can be used to improve device performance at the shorter wavelengths.

Magnetized liner inertial fusion (MagLIF) is a magneto-inertial-fusion concept that is studied on the 20-MA, 100-ns rise time Z Pulsed Power Facility at Sandia National Laboratories. Given the relative success of the platform, there is a wide interest in studying the scaled performance of this concept at a next-generation pulsed-power facility that may produce peak currents upward of 60 MA. An important aspect that requires more research is the instability dynamics of the imploding MagLIF liner, specifically how instabilities are initially seeded. It has been shown in magnetized 1-MA thin-foil liner Z-pinch implosion simulations that a Hall interchange instability (HII) effect can provide an independent seeding mechanism for helical magneto-Rayleigh–Taylor instabilities. Here in this paper, we explore this instability at higher peak currents for MagLIF using 2D discontinuous Galerkin PERSEUS simulations, an extended magneto-hydrodynamics code, which includes Hall physics. Our simulations of scaled MagLIF loads show that the growth rate of the HII is invariant to the peak current, suggesting that studies at 20-MA are directly relevant to 60-MA class machines.

This memorandum investigates the Callahan crushed salt constitutive model developed and used at Sandia National Laboratories for geomechanics applications. The formulation is reviewed, calibration against a novel long-term experiment with complex loading history is performed and validation against other experiments is attempted. Areas of improvement and deficiencies are identified that support the need for an alternative or updated constitutive model for crushed salt.

Electronic structure calculations on small systems such as H2, H2O, LiH, and BeH2 with chemical accuracy are still a challenge for the current generation of noisy intermediate-scale quantum (NISQ) devices. One of the reasons is that due to the device limitations, only minimal basis sets are commonly applied in quantum chemical calculations, which allows one to keep the number of qubits employed in the calculations at a minimum. However, the use of minimal basis sets leads to very large errors in the computed molecular energies as well as potential energy surface shapes. One way to increase the accuracy of electronic structure calculations is through the development of small basis sets better suited for quantum computing. In this work, we show that the use of adaptive basis sets, in which exponents and contraction coefficients depend on molecular structure, provides an easy way to dramatically improve the accuracy of quantum chemical calculations without the need to increase the basis set size and thus the number of qubits utilized in quantum circuits. As a proof of principle, we optimize an adaptive minimal basis set for quantum computing calculations on an H2 molecule, in which exponents and contraction coefficients depend on the H-H distance, and apply it to the generation of H2 potential energy surface on IBM-Q quantum devices. The adaptive minimal basis set reaches the accuracy of the double-zeta basis sets, thus allowing one to perform double-zeta quality calculations on quantum devices without the need to utilize twice as many qubits in simulations. This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.

Earlier studies have proven how ducted fuel injection (DFI) substantially reduces soot for low- and mid-load conditions in heavy-duty engines, without significant adverse effects on other emissions. Nevertheless, no comprehensive DFI study exists showing soot reductions at high- and full-load conditions. This study investigated DFI in a single-cylinder, 1.7-L, optical engine from low- to full-load conditions with a low-net-carbon fuel consisting of 80% renewable diesel and 20% biodiesel. Over the tested load range, DFI reduced engine-out soot by 38.1-63.1% compared to conventional diesel combustion (CDC). This soot reduction occurred without significant detrimental effects on other emission types. Thus, DFI reduced the severity of the soot-NOx tradeoff at all tested conditions. While DFI delivered considerable soot reductions in the present study, previous DFI studies at low- and mid-load conditions delivered larger soot reductions (>90%) compared to CDC operation at the same conditions. Therefore, the DFI configuration used here has been deemed nonoptimal (in terms of parameters such as the injector-spray and piston geometries), and several improvements are recommended for future studies with high-load DFI. These improvements include employing better spray-duct alignment, a deeper piston bowl with a smaller injector umbrella angle, and a fuel injector that opens and closes faster. The study also suggests future research to make DFI ready for commercialization, such as metal-engine tests to ensure desirable DFI performance over an engine's complete speed/load map. Overall, this study supports the continued development and commercialization of DFI to meet upcoming emissions regulations for heavy-duty vehicles. Specifically, multicylinder engine experiments and CFD simulations should be utilized to optimize the performance and clarify the full potential of DFI.

Here, we report deposition of hematite/Pd thin films on silicon wafers via radio frequency (RF) magnetron sputtering and subsequent characterization for future in situ neutron reflectometry studies. Following deposition, the hematite/Pd thin films were characterized as prepared and after annealing in air for 2h at 400, 500, and 600 °C, respectively. Raman spectroscopy, grazing incidence x-ray diffraction, and neutron reflectometry (NR) were used to characterize the structure and chemical compositions of the thin films. The results indicate that pure α-Fe₂O₃ (hematite) films were produced, free from other iron oxide phases and impurities. NR data reveal that one intermediate layer between the Pd layer and the hematite layer was formed during sputtering deposition processes. The fitted scattering length density (SLD) of the as-deposited hematite layer is 70% of the theoretical SLD value, indicating that the grains are loosely packed in the RF-deposited hematite films. After annealing at elevated temperatures, the hematite films show increased SLD values but remain comparable to that of preannealed.

Physical fatigue can have adverse effects on humans in extreme environments. Therefore, being able to predict fatigue using easy to measure metrics such as heart rate (HR) signatures has potential to have an impact in real-life scenarios. We apply a functional logistic regression model that uses HR signatures to predict physical fatigue, where physical fatigue is defined in a data-driven manner. Data were collected using commercially available wearable devices on 47 participants hiking the 20.7-mile Grand Canyon rim-to-rim trail in a single day. Fitted model provides good predictions and interpretable parameters for real-life application.

On a high level, the larger project in question, HydroGEN, aims to develop software used for finding equations of state (EOS) to optimize catalyst configuration for H₂ production through water splitting. In particular, this summer project focused on solving the nonlinear equations used in fitting the equations. This problem involved using Python to solve a linear system with nonlinear constraints. In order for this to be achieved, Pyomo was used to build a model and the solver Ipopt, interior point optimizer, was used. Pyomo is a Python-based language developed at Sandia; it is an optimization modeling language. Rather than solving the entire problem at once, a toy problem was created, simplifying the problem down to the most important focus. This problem had a known solution, comparable to the calculated solution to assess accuracy and as progress was made towards finding solutions, complexity was gradually added to the problem. After building and solving the toy problem, it was found that it gave reasonably accurate solutions, better compared to the two existing solvers previously used with this project in terms of functionality. The solver is now ready for implementation into the project’s main software.

While land-based wind energy has become economically competitive with traditional energy generation sources in the U.S., offshore wind is not. For floating offshore wind this difference is even more substantial where the levelized cost of energy (LCOE) is projected to be around 3-5 times more expensive than land-based wind. The turbine capital costs represent around 50% of the LCOE for land-based wind sites, but the increased system costs for floating offshore wind reduce this to 20%. The platform and mooring costs are the single largest contributor to the LCOE for floating offshore wind where their mass must counteract the overturning moment caused by the turbine’s thrust force. Despite the high costs of the platform and relatively low cost of the turbine, current offshore wind turbines are designed essentially the same as for land-based sites. Reducing the LCOE is the greatest challenge to realize the benefits of sustained development of floating offshore wind in the U.S. Reducing the complicated system costs of floating offshore wind will enable the industry to continue to grow and outpace current projections if reduced cost curves can be reached. The ideal wind energy system would remove all mass and cost that is not directly capturing energy from the wind. For floating offshore wind energy systems, this objective is even more significant as increased mass above the water level must be supported by larger and more expensive floating platforms. For this reason, vertical-axis wind turbines (VAWTs) are ideal for floating offshore sites and have several advantages over horizontal-axis wind turbines (HAWTs) at this scale. Large VAWTs offer opportunities for improved energy capture over HAWTs as single units and with reduced wind plant aerodynamic losses through enhanced wake recovery. Additionally, the platform-level placement of the VAWT drivetrain greatly reduces the demands placed on the floating platform and its mass and cost. The ARCUS vertical-axis wind turbine concept (U.S. 11,421,650 B2) is an iteration beyond traditional Darrieus-type VAWTs that replaces the rigid tower with blades that are bent into shape and held in place with tensioned center supports, like a bow. The ARCUS design has been shown to further decrease the VAWT rotor mass properties, with a 50% reduction over traditional Darrieus VAWTs quantified in the ATLANTIS program. The ARCUS VAWT’s efficient use of material for the rotor and turbine support structures combined with its lowered center of gravity enables a tension-leg platform (TLP) with simplified installation procedures. TLPs have been an emerging platform architecture in the Oil and Gas industry and demonstrated to have the lowest hull mass requirements while maintaining stability with minimal roll and pitch deflections in operation. A 22 MW ARCUS turbine has been designed with a three-column TLP that enables quayside integration of the turbine while maintaining system stability during tow-out and installation and having optimal mass and cost properties. A comprehensive analysis shows the optimal ARCUS TLP system design minimizes LCOE through efficient material usage and increased energy capture to yield a competitive LCOE estimate of $\$$55/MWh. A comparison with a reference HAWT, having the same swept area, quantifies the advantages that helped to produce this improved LCOE for the ARCUS concept: (1) 30% reduction in total turbine mass, (2) 70% reduction in turbine center of gravity, and (3) 45% increase in energy production over what is optimal for a HAWT. Intellectual property has been generated through the ATLANTIS program providing opportunities to further reduce the LCOE and improve the performance of the ARCUS turbine and TLP system, expanding the list of innovations to support commercial development of the ARCUS concept.

A predictive thermodynamic model is utilized for the calculation of fuel properties of oxymethylene dimethyl ethers (OME_3–4), surrogates for gasoline, diesel and aviation fuel, as well as alcohol blends with gasoline and diesel. The alcohols used for these blends are methanol, ethanol, propanol, butanol and pentanol; their mixing ratio ranges from 10 to 50% by volume. The model is based on the Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) equation of state (EoS) and Vapor Liquid Equilibrium (VLE) calculations at constant temperature, density and composition. The model includes the association term, with the assumption of two association sites (2B scheme), to enable the modeling of alcohols. The pure-component parameters are estimated based on the Group Contribution (GC) method of various sources, as well as a parametrization model specifically designed for the case of OME_3–4. The results of the computational model for the density, vapor pressure and distillation curves at various conditions, including high-pressure, high-temperature (HPHT), are compared to experimental and computational data available in the literature. In the cases where no measurements are available for the surrogates, experimental data for the corresponding target fuel are used, taking into consideration the inherent deviation in properties between real and surrogate fuel. Overall, the results are in good agreement with the data from the literature, with the average deviation not exceeding 12% for temperature (Kelvin) on the distillation curves, 10% for density and 46% for vapor pressure and the general trend being captured successfully. The use of different pure component parameter estimation techniques can further improve the prediction quality in the cases of OME3–4 and the aviation fuel surrogate, especially for the vapor pressure, leading to an average deviation lower than 18%. These results demonstrate the predictive capabilities of the model, which extend to a wide range of fuel types and pressure/temperature conditions. Through this investigation, the present work aims to establish the limits of applicability of this thermodynamic property prediction methodology.

Porous liquids (PLs) based on the zeolitic imidazole framework ZIF-8 are attractive systems for carbon capture since the hydrophobic ZIF framework can be solvated in aqueous solvent systems without porous host degradation. However, solid ZIF-8 is known to degrade when exposed to CO2 in wet environments, and therefore the long-term stability of ZIF-8-based PLs is unknown. Through aging experiments, the long-term stability of a ZIF-8 PL formed using the water, ethylene glycol, and 2-methylimidazole solvent system was systematically examined, and the mechanisms of degradation were elucidated. The PL was found to be stable for several weeks, with no ZIF framework degradation observed after aging in N2 or air. However, for PLs aged in a CO2 atmosphere, formation of a secondary phase occurred within 1 day from the degradation of the ZIF-8 framework. From the computational and structural evaluation of the effects of CO2 on the PL solvent mixture, it was identified that the basic environment of the PL caused ethylene glycol to react with CO2 forming carbonate species. These carbonate species further react within the PL to degrade ZIF-8. The mechanisms governing this process involves a multistep pathway for PL degradation and lays out a long-term evaluation strategy of PLs for carbon capture. Additionally, it clearly demonstrates the need to examine the reactivity and aging properties of all components in these complex PL systems in order to fully assess their stabilities and lifetimes.

Here, using a quantum computer to speed up one step in a textbook approach to generating random numbers proves to be a savvy strategy, and one that could make good use of quantum computers that will be available in the near future.

Here, the effects of gamma and proton irradiation, and of forward bias minority carrier injection, on photo-response were investigated for InAsSb/AlAsSb pBn mid-wave infrared (MWIR) detectors with an engineered majority-carrier barrier. Room-temperature gamma irradiation had an insignificant effect on 77 K photo-response. Gamma irradiation at 77 K detector temperature, however, decreased in situ photo-response by 19% after a cumulative dose of ~ 500 krad(Si). Subsequent forward bias minority carrier injection had no effect on photo-response. The 77 K detectors irradiated with 30 MeV protons up to 2 Mrad(Si) had photo-response degraded by up to 70%, but here forward bias minority carrier (hole) injection caused up to 12% recovery that persisted more than 30 min. These results suggest a mitigation strategy for maintaining the photo-response of similar detectors in radiation environments that cause displacement damage defects.

Astra, deployed in 2018, was the first petascale supercomputer to utilize processors based on the ARM instruction set. The system was also the first under Sandia's Vanguard program which seeks to provide an evaluation vehicle for novel technologies that with refinement could be utilized in demanding, large-scale HPC environments. In addition to ARM, several other important first-of-a-kind developments were used in the machine, including new approaches to cooling the datacenter and machine. This article documents our experiences building a power measurement and control infrastructure for Astra. While this is often beyond the control of users today, the accurate measurement, cataloging, and evaluation of power, as our experiences show, is critical to the successful deployment of a large-scale platform. While such systems exist in part for other architectures, Astra required new development to support the novel Marvell ThunderX2 processor used in compute nodes. In addition to documenting the measurement of power during system bring up and for subsequent on-going routine use, we present results associated with controlling the power usage of the processor, an area which is becoming of progressively greater interest as data centers and supercomputing sites look to improve compute/energy efficiency and find additional sources for full system optimization.

Vanadium dioxide (VO2) undergoes a metal-insulator phase transition at ∼70 °C and has attracted substantial interest for potential applications in electronics, including those in neuromorphic computing. The vanadium-oxygen system has a rather complicated phase diagram, and controlling the stoichiometry and the phase of thin films of vanadium oxides is a well-known challenge. We explore the novel combination of two methods of VO2 thin film deposition using off-axis RF magnetron sputtering on (100)- and (111)-oriented yttria-stabilized zirconia (YSZ) substrates: reactive sputtering of vanadium in an oxygen environment and sputtering of vanadium metal followed by oxidation to VO2. Interestingly, the reactive sputtering process on both substrate orientations yields the metastable semiconducting VO2 (B) phase, which is structurally stabilized by the YSZ surface. The metal sputtering and oxidation process on YSZ produces mainly the equilibrium monoclinic (or M1) phase of VO2 that exhibits a metal-insulator transition. Using this method, we obtained thin films of (010)-textured polycrystalline VO2 (M1) that show a metal-insulator transition with an on/off ratio larger than 1000.

The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ∼5 kJ molH2−1) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ molH2−1) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.

The Proliferation Resistance Optimization (PRO-X) program is actively supporting the design of new-build nuclear systems by identifying intrinsic characteristics or design choices to minimize the potential for diversion or production of weapons-usable nuclear material. The PRO-X program looks to optimize the safety, security, and performance of the fuel cycle infrastructure for a wide array of reactor types, including research reactors, small modular reactors (SMRs), and large advanced reactors (ARs).

Ga₂O₃ films were deposited on (100) MgAl₂O₄ spinel substrates at 550, 650, 750, and 850 °C using metal-organic chemical vapor deposition and investigated using x-ray diffraction and transmission electron microscopy. A phase-pure γ-Ga₂O₃-based material having an inverse spinel structure was formed at 850 °C; a mixture of the γ-phase and β-Ga₂O₃ was detected in films grown at 750 °C. Only β-Ga₂O₃ was determined in the films deposited at 650 and 550 °C. A β- to γ-phase transition occurred from the substrate/film interface during growth at 750 °C. The growth and stabilization of the γ-phase at the outset of film growth at 850 °C was affected by the substantial Mg and Al chemical interdiffusion from the MgAl₂O₄ substrate observed in the energy-dispersive x-ray spectrum. Further, atomic-scale investigations via scanning transmission electron microscopy of the films grown at 750 and 850 °C revealed a strong tetrahedral site preference for Ga and an octahedral site preference for Mg and Al. It is postulated that the occupation of these atoms in these particular sites drives the β-Ga₂O₃ to γ-phase transition and markedly enhances the thermal stability of the latter phase at elevated temperatures.

Here, phase formation and stability of five component compositionally complex rare earth zirconates (5RE₂Zr₂O₇) were investigated by X-ray diffraction and electron microprobe analysis. Zirconates with different rare earth compositions (LaNdSmEuDy, LaNdSmEuYb, LaNdEuErYb, LaNdDyErYb, SmEuDyYHo, LaYHoErYb, and DyYHoErYb) were synthesized at 1700°C and 2000°C by the solid-state method to investigate the effect of A-site site disorder (δ_A) on phase stability. Increased site disorder results from mixed cation occupancy with localized crystallographic strain and bond disorder. Compositions LaNdSmEuDy (δ_A = 4.6) and LaNdSmEuYb (δ_A = 6.0) produced a single pyrochlore phase and compositions SmDyYHoErYb (δ_A = 2.8), LaYHoErYb (δ_A = 6.2), and DyYHoErYb (δ_A = 1.7) produced a single fluorite phase. High δ_A compositions LaNdEuErYb (δ_A = 6.9) and LaNdDyErYb (δ_A = 7.2) produced a pyrochlore and fluorite phase mixture at 1700°C. Single phase was obtained for the latter composition at 2000°C. Of the single phase compositions calcined at 1700°C, LaNdSmEuYb and LaYHoErYb (both with largest δ_A) showed decomposition to mixed fluorite and pyrochlore phases during lower temperature anneals, indicating entropic stabilization. Comparison with prior work shows a temperature dependence of the critical δ_A for phase stability, and compositions near it are expected to be entropy stabilized.

The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment-friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind-integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast-acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39-bus test system.

Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

Modern density functional theory (DFT) is a powerful tool for accurately predicting self-consistent material properties such as equations of state, transport coefficients and opacities in high energy density plasmas, but it is generally restricted to conditions of local thermodynamic equilibrium (LTE) and produces only averaged electronic states instead of detailed configurations. We propose a simple modification to the bound-state occupation factor of a DFT-based average-atom model that captures essential non-LTE effects in plasmas—including autoionization and dielectronic recombination—thus extending DFT-based models to new regimes. Finally, we then expand the self-consistent electronic orbitals of the non-LTE DFT-AA model to generate multi-configuration electronic structure and detailed opacity spectra. This article is part of the theme issue ‘Dynamic and transient processes in warm dense matter’.

Abstract not provided.