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.
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 SiNx as an alternative to more commonly used oxide-insulators, as SiNx-based GMs may enable high temperature applications when paired with refractory metals. Comparing Co-SiNx and Mo-SiNx GMs, we found that, in the tunneling-dominated insulating regime, Mo-SiNx 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-SiNx. Given the metal-oxide formation seen in other GMs, SiNx is an appealing alternative for metals that readily oxidize. Furthermore, SiNx provides a path to metal-nitride nanostructures, potentially useful for various applications in plasmonics, optics, and sensing.
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.
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 (δA) on phase stability. Increased site disorder results from mixed cation occupancy with localized crystallographic strain and bond disorder. Compositions LaNdSmEuDy (δA = 4.6) and LaNdSmEuYb (δA = 6.0) produced a single pyrochlore phase and compositions SmDyYHoErYb (δA = 2.8), LaYHoErYb (δA = 6.2), and DyYHoErYb (δA = 1.7) produced a single fluorite phase. High δA compositions LaNdEuErYb (δA = 6.9) and LaNdDyErYb (δA = 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 δA) 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 δA for phase stability, and compositions near it are expected to be entropy stabilized.
We present the characterization of several atmospheric aerosol analogs in a tabletop chamber and an analysis of how the concentration of NaCl present in these aerosols influences their bulk optical properties. Atmospheric aerosols (e.g., fog and haze) degrade optical signal via light–aerosol interactions causing scattering and absorption, which can be described by Mie theory. This attenuation is a function of the size distribution and number concentration of droplets in the light path. These properties are influenced by ambient conditions and the droplet’s composition, as described by Köhler theory. It is therefore possible to tune the wavelength-dependent bulk optical properties of an aerosol by controlling droplet composition. We present experimentation wherein we generated multiple microphysically and optically distinct atmospheric aerosol analogs using salt water solutions with varying concentrations of NaCl. The results demonstrate that changing the NaCl concentration has a clear and predictable impact on the microphysical and optical properties of the aerosol
Outdoor cultivation is commonly used to produce algal biomass for a variety of bioproducts including food, feed, fuel, pharmaceuticals, and nutraceuticals. Outdoor cultivation ponds are highly susceptible to pest pressures that may lead to periods of low productivity or even entire loss of the algal crop. Therefore, there is a need for rapid, real-time tracking of pests for early intervention to mitigate crop loss. Herein, we describe the development of a field deployable, low-cost qPCR assay for detecting both known and novel pests of a farmed eukaryotic alga species, Nannochloropsis sp. We performed a proximity guided metagenome deconvolution approach (ProxiMeta™) to discover novel pests that temporally correspond to periods of reduced pond productivity. This approach provided high-quality metagenome assemblies that were used to design qPCR probes to detect specific pests of interest. The portable qPCR assay, designed to be deployed at remote field locations, enables low-cost surveillance with a rapid (2 h) turn-around time. Frequent sampling allows for early detection and prompts intervention strategies to remedy infected ponds to minimize crop loss. The qPCR assay was used to successfully detect a known predatory bacterium within the order Bdellovibrionales both in the lab and at a remote field location. Furthermore, we assembled the genome of two novel, site-specific pests in the Saprospiraceae family and successfully designed qPCR probes that differentially detected their presence in two different pond locations. Ultimately, this assay has the potential to monitor multiple pests simultaneously and tailor targets to match likely pest infections that differ across geographical locations, helping to mitigate crop loss on a large scale.
Dynamic compression studies have been used to study the nucleation kinetics of water to ice VII for decades. Diagnostics such as photon Doppler velocimetry, transmission loss, and imaging have been used to measure pressure/density, and phase fraction, while temperature has remained the difficult thermodynamic property to quantify. In this work, we measured pressure/density and implemented a diagnostic to measure the temperature. In doing so the temperature shows quasi-isentropically compressed liquid water forms ice at pressures below the previously defined metastable limit, and the liquid phase is not hypercoooled as previously thought above that limit. Instead, the latent heat raises the temperature to the liquid-ice-VII melt line, where it remains with increasing pressure. We propose a hypothesis to corroborate these results with previous work on dynamic compression freezing. These results provide constraints for nucleation models, and suggest this technique be used to investigate phase transitions in other materials.
Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.
