Publications

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A series of nanopillar compression tests were performed on tungsten as a function of temperature using in situ transmission electron microscopy with localized laser heating. Surface oxidation was observed to form on the pillars and grow in thickness with increasing temperature. Deformation between 850◦C and 1120◦C is facilitated by long-range diffusional transport from the tungsten pillar onto adjacent regions of the Y2O3-stabilized ZrO2 indenter. The constraint imposed by the surface oxidation is hypothesized to underly this mechanism for localized plasticity, which is generally the so-called whisker growth mechanism. The results are discussed in context of the tungsten fuzz growth mechanism in He plasma-facing environments. The two processes exhibit similar morphological features and the conditions under which fuzz evolves appear to satisfy the conditions necessary to induce whisker growth.

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Leakage pathways through caprock lithologies for underground storage of CO2 and/or enhanced oil recovery (EOR) include intrusion into nano-pore mudstones, flow within fractures and faults, and larger-scale sedimentary heterogeneity (e.g., stacked channel deposits). To assess multiscale sealing integrity of the caprock system that overlies the Morrow B sandstone reservoir, Farnsworth Unit (FWU), Texas, USA, we combine pore-to-core observations, laboratory testing, well logging results, and noble gas analysis. A cluster analysis combining gamma ray, compressional slowness, and other logs was combined with caliper responses and triaxial rock mechanics testing to define eleven lithologic classes across the upper Morrow shale and Thirteen Finger limestone caprock units, with estimations of dynamic elastic moduli and fracture breakdown pressures (minimum horizontal stress gradients) for each class. Mercury porosimetry determinations of CO2 column heights in sealing formations yield values exceeding reservoir height. Noble gas profiles provide a “geologic time-integrated” assessment of fluid flow across the reservoir-caprock system, with Morrow B reservoir measurements consistent with decades-long EOR water-flooding, and upper Morrow shale and lower Thirteen Finger limestone values being consistent with long-term geohydrologic isolation. Together, these data suggest an excellent sealing capacity for the FWU and provide limits for injection pressure increases accompanying carbon storage activities.

Probabilistic and Bayesian neural networks have long been proposed as a method to incorporate uncertainty about the world (both in training data and operation) into artificial intelligence applications. One approach to making a neural network probabilistic is to leverage a Monte Carlo sampling approach that samples a trained network while incorporating noise. Such sampling approaches for neural networks have not been extensively studied due to the prohibitive requirement of many computationally expensive samples. While the development of future microelectronics platforms that make this sampling more efficient is an attractive option, it has not been immediately clear how to sample a neural network and what the quality of random number generation should be. This research aimed to start addressing these two fundamental questions by examining basic “off the shelf” neural networks can be sampled through a few different mechanisms (including synapse “dropout” and neuron “dropout”) and examine how these sampling approaches can be evaluated both in terms of evaluating algorithm effectiveness and the required quality of random numbers.

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In this project, our goal was to develop methods that would allow us to make accurate predictions about individual differences in human cognition. Understanding such differences is important for maximizing human and human-system performance. There is a large body of research on individual differences in the academic literature. Unfortunately, it is often difficult to connect this literature to applied problems, where we must predict how specific people will perform or process information. In an effort to bridge this gap, we set out to answer the question: can we train a model to make predictions about which people understand which languages? We chose language processing as our domain of interest because of the well- characterized differences in neural processing that occur when people are presented with linguistic stimuli that they do or do not understand. Although our original plan to conduct several electroencephalography (EEG) studies was disrupted by the COVID-19 pandemic, we were able to collect data from one EEG study and a series of behavioral experiments in which data were collected online. The results of this project indicate that machine learning tools can make reasonably accurate predictions about an individual?s proficiency in different languages, using EEG data or behavioral data alone.

Machine-learned models, specifically neural networks, are increasingly used as “closures” or “constitutive models” in engineering simulators to represent fine-scale physical phenomena that are too computationally expensive to resolve explicitly. However, these neural net models of unresolved physical phenomena tend to fail unpredictably and are therefore not used in mission-critical simulations. In this report, we describe new methods to authenticate them, i.e., to determine the (physical) information content of their training datasets, qualify the scenarios where they may be used and to verify that the neural net, as trained, adhere to physics theory. We demonstrate these methods with neural net closure of turbulent phenomena used in Reynolds Averaged Navier-Stokes equations. We show the types of turbulent physics extant in our training datasets, and, using a test flow of an impinging jet, identify the exact locations where the neural network would be extrapolating i.e., where it would be used outside the feature-space where it was trained. Using Generalized Linear Mixed Models, we also generate explanations of the neural net (à la Local Interpretable Model agnostic Explanations) at prototypes placed in the training data and compare them with approximate analytical models from turbulence theory. Finally, we verify our findings by reproducing them using two different methods.

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Wellbore integrity is a significant problem in the U.S. and worldwide, which has serious adverse environmental and energy security consequences. Wells are constructed with a cement barrier designed to last about 50 years. Indirect measurements and models are commonly used to identify wellbore damage and leakage, often producing subjective and even erroneous results. The research presented herein focuses on new technologies to improve monitoring and detection of wellbore failures (leaks) by developing a multi-step machine learning approach to localize two types of thermal defects within a wellbore model, a prototype mechatronic system for automatically drilling small diameter holes of arbitrary depth to monitor the integrity of oil and gas wells in situ, and benchtop testing and analyses to support the development of an autonomous real-time diagnostic tool to enable sensor emplacement for monitoring wellbore integrity. Each technology was supported by experimental results. This research has provided tools to aid in the detection of wellbore leaks and significantly enhanced our understanding of the interaction between small-hole drilling and wellbore materials.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy Office of Nuclear Energy, Office of Spent Fuel and Waste Disposition (SFWD), has been conducting research and development on generic deep geologic disposal systems (i.e., geologic repositories). This report describes specific activities in the Fiscal Year (FY) 2021 associated with the Geologic Disposal Safety Assessment (GDSA) Repository Systems Analysis (RSA) work package within the SFWST Campaign. The overall objective of the GDSA RSA work package is to develop generic deep geologic repository concepts and system performance assessment (PA) models in several host-rock environments, and to simulate and analyze these generic repository concepts and models using the GDSA Framework toolkit, and other tools as needed.

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Currently a set of 71 radionuclides are accounted for in off-site consequence analysis for LWRs. Radionuclides of dose consequence are expected to change for non-LWRs, with radionuclides of interest being type-specific. This document identifies an expanded set of radionuclides that may need to be accounted for in multiple non-LWR systems: high temperature gas reactors (HTGRs); fluoride-salt-cooled high-temperature reactors (FHRs); thermal-spectrum fluoride-based molten salt reactors (MSRs); fast-spectrum chloride-based MSRs; and, liquid metal fast reactors with metallic fuel (LMRs) Specific considerations are provided for each reactor type in Chapter 2 through Chapter 5, and a summary of all recommendations is provided in Chapter 6. All identified radionuclides are already incorporated within the MACCS software, yet the development of tritium-specific and carbon-specific chemistry models are recommended.

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As the seismic monitoring community advances toward detecting, identifying, and locating ever-smaller natural and anthropogenic events, the need is constantly increasing for higher resolution, higher fidelity data, models, and methods for accurately characterizing events. Local-distance seismic data provide robust constraints on event locations, but also introduce complexity due to the significant geologic heterogeneity of the Earth’s crust and upper mantle, and the relative sparsity of data that often occurs with small events recorded on regional seismic networks. Identifying the critical characteristics for improving local-scale event locations and the factors that impact location accuracy and reliability is an ongoing challenge for the seismic community. Using Utah as a test case, we examine three data sets of varying duration, finesse, and magnitude to investigate the effects of local earth structure and modeling parameters on local-distance event location precision and accuracy. We observe that the most critical elements controlling relocation precision are azimuthal coverage and local-scale velocity structure, with tradeoffs based on event depth, type, location, and range.

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This report summarizes the activities performed as part of the Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) Grand Challenge LDRD project. We provide an overview of the research done in this project, including work on cyber emulation, uncertainty quantification, and optimization. We present examples of integrated analyses performed on two case studies: a network scanning/detection study and a malware command and control study. We highlight the importance of experimental workflows and list references of papers and presentations developed under this project. We outline lessons learned and suggestions for future work.

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This project developed prototype germanium telluride switches, which can be used in RF applications to improve SWAP (size, weight, and power) and signal quality in RF systems. These switches can allow for highly reconfigurable systems, including antennas, communications, optical systems, phased arrays, and synthetic aperture radar, which all have high impact on current National Security goals for improved communication systems and communication technology supremacy. The final result of the project was the demonstration of germanium telluride RF switches, which could act as critical elements necessary for a single chip RF communication system that will demonstrate low SWAP and high reconfigurability

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We present a new experimental methodology for detailed experimental investigations of depolymerization reactions over solid catalysts. This project aims to address a critical need in fundamental research on chemical upcycling of polymers – the lack of rapid, sensitive, isomerselective probing techniques for the detection of reaction intermediates and products. Our method combines a heterogeneous catalysis reactor for the study of multiphase (gas/polymer melt/solid) systems, coupled to a vacuum UV photoionization time-of-flight mass spectrometer. This apparatus draws on our expertise in probing complex gas-phase chemistry and enables highthroughput, detailed chemical speciation measurements of the gas phase above the catalyst, providing valuable information on the heterogeneous catalytic reactions. Using this approach, we investigated the depolymerization of high-density polyethylene (HDPE) over Ir-doped zeolite catalysts. We showed that the product distribution was dominated by low-molecular weight alkenes with terminal C=C double bonds and revealed the presence of many methyl-substituted alkenes and alkanes, suggesting extensive methyl radical chemistry. In addition, we investigated the fundamental reactivity of model oligomer molecules n-butane and isobutane over ZSM-5 zeolites. We demonstrated the first direct detection of methyl radical intermediates, confirming the key role of methyl in zeolite-catalyzed activation of alkanes. Our results show the potential of this experimental method to achieve deep insight into the complex depolymerization reactions and pave the way for detailed mechanistic studies, leading to increased fundamental understanding of key processes in chemical upcycling of polymers.

Identification and characterization of underground events from surface or remote data requires a thorough understanding of the rock material properties. However, material properties usually come from borehole data, which is expensive and not always available. A potential alternative is to use topographic characteristics to approximate the strength, but this has never been done before quantitatively. Here we present the results from the first steps towards this goal. We have found that there are strong correlations between compressive and tensile strengths and slopes, but these correlations vary depending on data analysis details. Rugosity may be better correlated to strength than slope values. More comprehensive analyses are needed to fully understand the best method of predicting strength from topography for this area. We also found that misalignment of multiple GIS datasets can have a large influence on the ability to make interpretations. Lastly, these results will require further study in a variety of climatic conditions before being applicable to other sites.

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This report provides a summary of notes for building and running the Sandia Computational Engine for Particle Transport for Radiation Effects (SCEPTRE) code. SCEPTRE is a general- purpose C++ code for solving the linear Boltzmann transport equation in serial or parallel using unstructured spatial finite elements, multigroup energy treatment, and a variety of angular treatments including discrete ordinates (Sn) and spherical harmonics (Pn). Either the first-order form of the Boltzmann equation or one of the second-order forms may be solved. SCEPTRE requires a small number of open-source Third Party Libraries (TPL) to be available, and example scripts for building these TPL are provided. The TPL needed by SCEPTRE are Trilinos, Boost, and Netcdf. SCEPTRE uses an autotools build system, and a sample configure script is provided. Running the SCEPTRE code requires that the user provide a spatial finite-elements mesh in Exodus format and a cross section library in a format that will be described. SCEPTRE uses an xml-based input, and several examples will be provided.

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Metal oxides have been an attractive option for a range of applications, including hydrogen sensors, microelectronics, and catalysis, due to their reactivity and tunability. The properties of metal oxides can vary greatly on their precise surface structure; however, few surface science techniques can achieve atomistic-level determinations of surface structure, and fewer yet can do so for insulator surfaces. Low energy ion beam analysis offers a potential insulator-compatible solution to characterizing the surface structure of metal oxides. As a feasibility study, we apply low energy ion beam analysis to investigate the surface structure of a magnetite single crystal, Fe₃O₄(100). We obtain multi-angle maps using both forward-scattering low energy ion scattering (LEIS) and backscattering impact-collision ion scattering spectroscopy (ICISS). Both sets of experimental maps have intensity patterns that reflect the symmetries of the Fe₃O₄(100) surface structure. However, analytical interpretation of these intensity patterns to extract details of the surface structure is significantly more complex than previous LEIS and ICISS structural studies of one-component metal crystals, which had far more symmetries to exploit. To gain further insight into the surface structure, we model our experimental measurements with ion-trajectory tracing simulations using molecular dynamics. Our simulations provide a qualitative indication that our experimental measurements agree better with a subsurface cation vacancy model than a distorted bulk model.

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The twenty-seven critical experiments in this series were performed in 2020 in the SCX at the Sandia Pulsed Reactor Facility. The experiments are grouped by fuel rod pitch. Case 1 is a base case with a pitch of 0.8001 cm and no water holes in the array. Cases 2 through 6 have the same pitch as Case 1 but contain various configurations with water holes, providing slight variations in the fuel-to-water ratio. Similarly, Case 7 is a base case with a pitch of 0.854964 cm and no water holes in the array. Cases 8 through 11 have the same pitch as Case 7 but contain various configurations with water holes. Cases 12 through 15 have a pitch of 1.131512 cm and differ according to the number of water holes in the array, with Case 12 having no water holes. Cases 16 through 19 have a pitch of 1.209102 cm and differ according to number of water holes in the array, with Case 16 having no water holes. Cases 20 through 23 have a pitch of 1.6002 cm and differ according to number of water holes in the array, with Case 20 having no water holes. Cases 24 through 27 have a pitch of 1.709928 cm and differ according to number of water holes in the array, with Case 24 having no water holes. As the experiment case number increases, the fuel-to-water volume ratio decreases.

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The purpose of this report is to document improvements in the simulation of commercial vacuum drying procedures at the Nuclear Energy Work Complex at Sandia National Laboratories. Validation of the extent of water removal in a dry spent nuclear fuel storage system based on drying procedures used at nuclear power plants is needed to close existing technical gaps. Operational conditions leading to incomplete drying may have potential impacts on the fuel, cladding, and other components in the system. A general lack of data suitable for model validation of commercial nuclear canister drying processes necessitates additional, well-designed investigations of drying process efficacy and water retention. Scaled tests that incorporate relevant physics and well-controlled boundary conditions are essential to provide insight and guidance to the simulation of prototypic systems undergoing drying processes.

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The AXIOM-Unfold application is a computational code for performing spectral unfolds along with uncertainty quantification of the photon spectrum. While this code was principally designed for spectral unfolds on the Saturn source, it is also relevant to other radiation sources such as Pithon. This code is a component of the AXIOM project which was undertaken in order to measure the time-resolved spectrum of the Saturn source; to support this, the AXIOM-Unfold code is able to process time-dependent dose measurements in order to obtain a time-resolved spectrum. This manual contains a full description of the algorithms used by the method. The code features are fully documented along with several worked examples.

Using an optical microscopy setup adapted to in-situ studies of ice formation at ambient pressure, we examined a specific multicomponent mineral, microcline, with the ultimate aim of gaining a more realistic understanding of ice nucleation in Earth’s atmosphere. We focused on a perthitic feldspar, microcline, to test the hypothesis that co-existence in some feldspars of K-rich and Na-rich phases are contributing to enhanced ice nucleation. On a sample deliberately chosen to contain lamella, a typical perthitic microstructure, and flat surface regions next to each other, we performed a series of ice formation experiments. We found microcline to promote ice formation, causing a large number of ice nucleation events at around - 27°C. The number of ice nuclei decreased from experimental run to experimental run, indicating surface aging upon repeated exposure to humidity. An analysis of 10 experimental runs of identical conditions did not reveal an obvious enhancement of ice formation at the lamellar microstructure. Instead, we find efficient nucleation at various surface sites that produce orientationally aligned ice crystallites with asymmetric shape. Based on this observation we propose that surface steps running along select directions produce microfacets of an orientation that is favorable to enhanced ice nucleation, similar to previously reported for K-rich feldspars.

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This report investigates the use of unsupervised probabilistic learning techniques for the analysis of hypersonic trajectories. The algorithm first extracts the intrinsic structure in the data via a diffusion map approach. Using the diffusion coordinates on the graph of training samples, the probabilistic framework augments the original data with samples that are statistically consistent with the original set. The augmented samples are then used to construct conditional statistics that are ultimately assembled in a path-planing algorithm. In this framework the controls are determined stage by stage during the flight to adapt to changing mission objectives in real-time. A 3DOF model was employed to generate optimal hypersonic trajectories that comprise the training datasets. The diffusion map algorithm identfied that data resides on manifolds of much lower dimensionality compared to the high-dimensional state space that describes each trajectory. In addition to the path-planing worflow we also propose an algorithm that utilizes the diffusion map coordinates along the manifold to label and possibly remove outlier samples from the training data. This algorithm can be used to both identify edge cases for further analysis as well as to remove them from the training set to create a more robust set of samples to be used for the path-planing process.

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for the compilers to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNNs spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution.

This project aimed to identify the performance-limiting mechanisms in mid- to far infrared (IR) sensors by probing photogenerated free carrier dynamics in model detector materials using scanning ultrafast electron microscopy (SUEM). SUEM is a recently developed method based on using ultrafast electron pulses in combination with optical excitations in a pump- probe configuration to examine charge dynamics with high spatial and temporal resolution and without the need for microfabrication. Five material systems were examined using SUEM in this project: polycrystalline lead zirconium titanate (a pyroelectric), polycrystalline vanadium dioxide (a bolometric material), GaAs (near IR), InAs (mid IR), and Si/SiO 2 system as a prototypical system for interface charge dynamics. The report provides detailed results for the Si/SiO 2 and the lead zirconium titanate systems.

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In this LDRD project, we developed a versatile capability for high-resolution measurements of electron scattering processes in gas-phase molecules, such as ionization, dissociation, and electron attachment/detachment. This apparatus is designed to advance fundamental understanding of these processes and to inform predictions of plasmas associated with applications such as plasma-assisted combustion, neutron generation, re-entry vehicles, and arcing that are critical to national security. We use innovative coupling of electron-generation and electron-imaging techniques that leverages Sandia’s expertise in ion/electron imaging methods. Velocity map imaging provides a measure of the kinetic energies of electrons or ion products from electron scattering in an atomic or molecular beam. We designed, constructed, and tested the apparatus. Tests include dissociative electron attachment to O2 and SO2, as well as a new method for studying laser-initiated plasmas. This capability sets the stage for new studies in dynamics of electron scattering processes, including scattering from excited-state atoms and molecules.

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Defects in materials are an ongoing challenge for quantum bits, so called qubits. Solid state qubits—both spins in semiconductors and superconducting qubits—suffer from losses and noise caused by two-level-system (TLS) defects thought to reside on surfaces and in amorphous materials. Understanding and reducing the number of such defects is an ongoing challenge to the field. Superconducting resonators couple to TLS defects and provide a handle that can be used to better understand TLS. We develop noise measurements of superconducting resonators at very low temperatures (20 mK) compared to the resonant frequency, and low powers, down to single photon occupation.

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Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

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The DOE-NE NWM Cloud was designed to be a generic set of tools and applications for any nuclear waste management program. As policymakers continue to consider approaches that emphasize consolidated interim storage and transportation of spent nuclear fuel, a gap analysis of the tools and applications provided for spent nuclear fuel and high-level radioactive waste disposal in comparison those needed for siting, licensing, and developing a consolidated interim storage facility and/or for a transportation campaign will help prepare DOE for implementing such potential policy direction. This report evaluates the points of alignment and potential gaps between the applications on the NWM Cloud that supported SNF disposal project, and the applications needed to address QA requirements and for other project support needs of an SNF storage project.

Within the past half-decade, it has become overwhelmingly clear that suppressing the spread of deliberate false and misleading information is of the utmost importance for protecting democratic institutions. Disinformation has been found to come from both foreign and domestic actors, but the effects from either can be disastrous. From the simple encouragement of unwarranted distrust to conspiracy theories promoting violence, the results of disinformation have put the functionality of American democracy under direct threat. Present scientific challenges posed by this problem include detecting disinformation, quantifying its potential impact, and preventing its amplification. We present a model on which we can experiment with possible strategies toward the third challenge: the prevention of amplification. This is a social contagion network model, which is decomposed into layers to represent physical, ''offline'', interactions as well as virtual interactions on a social media platform. Along with the topological modifications to the standard contagion model, we use state-transition rules designed specifically for disinformation, and distinguish between contagious and non-contagious infected nodes. We use this framework to explore the effect of grassroots social movements on the size of disinformation cascades by simulating these cascades in scenarios where a proportion of the agents remove themselves from the social platform. We also test the efficacy of strategies that could be implemented at the administrative level by the online platform to minimize such spread. These top-down strategies include banning agents who disseminate false information, or providing corrective information to individuals exposed to false information to decrease their probability of believing it. We find an abrupt transition to smaller cascades when a critical number of random agents are removed from the platform, as well as steady decreases in the size of cascades with increasingly more convincing corrective information. Finally, we compare simulated cascades on this framework with real cascades of disinformation recorded on Whatsapp surrounding the 2019 Indian election. We find a set of hyperparameter values that produces a distribution of cascades matching the scaling exponent of the distribution of actual cascades recorded in the dataset. We acknowledge the available future directions for improving the performance of the framework and validation methods, as well as ways to extend the model to capture additional features of social contagion.

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The typical topology optimization workflow uses a design domain that does not change during the optimization process. Consequently, features of the design domain, such as the location of loads and constraints, must be determined in advance and are not optimizable. A method is proposed herein that allows the design domain to be optimized along with the topology. This approach uses topology and shape derivatives to guide nested optimizers to the optimal topology and design domain. The details of the method are discussed, and examples are provided that demonstrate the utility of this approach.

Organisms can synthesize biomaterials incorporating an array of naturally occurring elements while overcoming challenges and insults. Although, it is known that most cellular biomaterials are synthesized in specialized cellular compartments, there are knowledge gaps about how organic/inorganic biomaterial synthesis is orchestrated inside cells. In addition, there is great potential in understanding how individual monomers can self-assembly into organized patterns to form responsive biomaterials. Forisomes are a natural responsive biomaterial found in legume plants that serve as a plug sieve element in the plant phloem that undergo anisotropic conformational changes by rapid (<1 s) ATP-independent from condensed spindle to plug-like form, triggered by the influx of Ca²⁺. Addressing principles of forisome synthesis and assembly will determine how biomaterials containing inorganic elements self-assemble and conduct chemical modification to produce biomaterials or undergo biomineralization. We employ transcription and translation (TXTL) using cell-free expression systems for forisome monomer expression, self-assembly, and pattern probing. We conducted experiments to precisely control forisome proteins synthesis of various monomers SEO1, SEO2, SEO3, and SEO4 to explore self- assembly. We demonstrate forisome self-assembly of the SEO monomers is possible and indicate unique monomer fluorescent labeling patterns that require additional analysis. We investigated locations and linkers for adding tetracysteine tag fluorophore probes to determine impacts of self-assembly and anisotropic conformational changes.

Most earth materials are anisotropic with regard to seismic wave-speeds, especially materials such as shales, or where oriented fractures are present. However, the base assumption for many numerical simulations is to treat earth materials as isotropic media. This is done for simplicity, the apparent weakness of anisotropy in the far field, and the lack of well-characterized anisotropic material properties for input into numerical simulations. One approach for addressing the higher complexity of actual geologic regions is to model the material as an orthorhombic medium. We have developed an explicit time-domain, finite-difference (FD) algorithm for simulating three-dimensional (3D) elastic wave propagation in a heterogeneous orthorhombic medium. The objective of this research is to investigate the errors and biases that result from modeling a non-isotropic medium as an isotropic medium. This is done by computing “observed data” by using synthetic, anisotropic simulations with the assumption of an orthorhombic, anisotropic earth model. Green’s functions for an assumed isotropic earth model are computed and then used an inversion designed to estimate moment tensors with the “observed” data. One specific area of interest is how shear waves, which are introduced in an anisotropic model even for an isotropic explosion, affect the characterization of seismic sources when isotropic earth assumptions are made. This work is done in support of the modeling component of the Source Physics Experiment (SPE), a series of underground chemical explosions at the Nevada National Security Site (NNSS).

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Cybersecurity for industrial control systems is an important consideration that advance reactor designers will need to consider. How cyber risk is managed is the subject of on-going research and debate in the nuclear industry. This report seeks to identify potential cyber risks for advance reactors. Identified risks are divided into absorbed risk and licensee managed risk to clearly show how cyber risks for advance reactors can potentially be transferred. Absorbed risks are risks that originate external to the licensee but may unknowingly propagate into the plant. Insights include (1) the need for unification of safety, physical security, and cybersecurity risk assessment frameworks to ensure optimal coordination of risk, (2) a quantitative risk assessment methodology in conjunction with qualitative assessments may be useful in efficiently and sufficiently managing cyber risks, and (3) cyber risk management techniques should align with a risked informed regulatory framework for advance reactors.

Ship tracks are quasi-linear cloud patterns produced from the interaction of ship emissions with low boundary layer clouds. They are visible throughout the diurnal cycle in satellite images from space-borne assets like the Advanced Baseline Imagers (ABI) aboard the National Oceanic and Atmospheric Administration Geostationary Operational Environmental Satellites (GOES-R). However, complex atmospheric dynamics often make it difficult to identify and characterize the formation and evolution of tracks. Ship tracks have the potential to increase a cloud's albedo and reduce the impact of global warming. Thus, it is important to study these patterns to better understand the complex atmospheric interactions between aerosols and clouds to improve our climate models, and examine the efficacy of climate interventions, such as marine cloud brightening. Over the course of this 3-year project, we have developed novel data-driven techniques that advance our ability to assess the effects of ship emissions on marine environments and the risks of future marine cloud brightening efforts. The three main innovative technical contributions we will document here are a method to track aerosol injections using optical flow, a stochastic simulation model for track formations and an automated detection algorithm for efficient identification of ship tracks in large datasets.

We describe a novel pulsed magnetic gradiometer based on the optical interference of sidebands generated using two spatially separated alkali vapor cells. In contrast to traditional magnetic gradiometers, our approach provides a direct readout of the gradient field without the intermediate step of subtracting the outputs of two spatially separated magnetometers. Operation of the gradiometer in multiple field orientations is discussed. The noise floor is measured as low as 25$\frac{fT}{\sqrt{Hz-cm}}$ in a room without magnetic shielding.

The NVBL Viral Fate and Transport Team includes researchers from eleven DOE national laboratories and is utilizing unique experimental facilities combined with physics-based and data-driven modeling and simulation to study the transmission, transport, and fate of SARSCoV-2. The team was focused on understanding and ultimately predicting SARS-CoV-2 viability in varied environments with the goal of rapidly informing strategies that guide the nation’s resumption of normal activities. The primary goals of this project include prioritizing administrative and engineering controls that reduce the risk of SARS-CoV-2 transmission within an enclosed environment; identifying the chemical and physical properties that influence binding of SARS-CoV-2 to common surfaces; and understanding the contribution of environmental reservoirs and conditions on transmission and resurgence of SARS-CoV-2.

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This project sought to develop a fundamental understanding of the mechanisms underlying a newly observed enhanced germanium (Ge) diffusion process in silicon germanium (SiGe) semiconductor nanostructures during thermal oxidation. Using a combination of oxidationdiffusion experiments, high resolution imaging, and theoretical modeling, a model for the enhanced Ge diffusion mechanism was proposed. Additionally, a nanofabrication approach utilizing this enhanced Ge diffusion mechanism was shown to be applicable to arbitrary 3D shapes, leading to the fabrication of stacked silicon quantum dots embedded in SiGe nanopillars. A new wet etch-based method for preparing 3D nanostructures for highresolution imaging free of obscuring material or damage was also developed. These results enable a new method for the controlled and scalable fabrication of on-chip silicon nanostructures with sub-10 nm dimensions needed for next generation microelectronics, including low energy electronics, quantum computing, sensors, and integrated photonics.

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Highlights: Battery energy storage may improve energy efficiency and reliability of hybrid energy systems composed by diesel and solar photovoltaic power generators serving isolated communities.In projects aiming update of power plants serving electrically isolated communities with redundant diesel generation, battery energy storage can improve overall economic performance of power supply system by reducing fuel usage, decreasing capital costs by replacing redundant diesel generation units, and increasing generator system life by shortening yearly runtime.Fast-acting battery energy storage systems with grid-forming inverters might have potential for improving drastically the reliability indices of isolated communities currently supplied by diesel generation. Abstract: This paper will highlight unique challenges and opportunities with regard to energy storage utilization in remote, self-sustaining communities. The energy management of such areas has unique concerns. Diesel generation is often the go-to power source in these scenarios, but these systems are not devoid of issues. Without dedicated maintenance crews as in large, interconnected network areas, minor interruptions can be frequent and invasive not only for those who lose power, but also for those in the community that must then correct any faults. Although the immediate financial benefits are perhaps not readily apparent, energy storage could be used to address concerns related to reliability, automation, fuel supply concerns, generator degradation, solar utilization, and, yes, fuel costs to name a few. These ideas are shown through a case study of the Levelock Village of Alaska. Currently, the community is faced with high diesel prices and a difficult supply chain, which makes temporary loss of power very common and reductions in fuel consumption very impactful. This study will investigate the benefits that an energy storage system could bring to the overall system life, fuel costs, and reliability of the power supply. The variable efficiency of the generators, impact of startup/shutdown process, and low-load operation concerns are considered. The technological benefits of the combined system will be explored for various scenarios of future diesel prices and technology maintenance/replacement costs as well as for the avoidance of power interruptions that are so common in the community currently. Graphic abstract: [Figure not available: see fulltext.] Discussion: In several cases, energy storage can provide a means to promote energy equity by improving remote communities’ power supply reliability to levels closer to what the average urban consumer experiences at a reduced cost compared to transmission buildout. Furthermore, energy equity represents a hard-to-quantify benefit achieved by the integration of energy storage to isolated power systems of under-served communities, which suggests that the financial aspects of such projects should be questioned as the main performance criterion. To improve battery energy storage system valuation for diesel-based power systems, integration analysis must be holistic and go beyond fuel savings to capture every value stream possible.

This report details the measured x-ray characteristics radiating from laser targets on 7 shots at Lawrence Livermore National Laboratory's National Ignition Facility performed June 30 - July 2, 2020. Generally, the x-ray sources were designed to serve as meaningful drivers of a variety of Radiation Effects Sciences response experiments. Specifically, on these 7 shots, the measured x-ray output characteristics and the response experiments serve as a useful data set for validating performance and informing further development of Sandia National Laboratories' EMPIRE code. We present x-ray measurements and analyses relevant to simulating the System-Generated Electro Magnetic Pulse experiments that also occurred on these shots (described elsewhere). Sufficient detail is provided so that a motivated, but novice, x-ray source user could produce some of these analysis results from raw data on similar x-ray shots.

Gamma irradiation is a process that uses Cobalt60 radionuclide produced artificially in nuclear reactors to irradiate a variety of items using gamma radiation. A key characteristic of gamma irradiation is its high penetration capability and the fact that it can modify physical, chemical, and biological properties of the irradiated materials.

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Sandia National Laboratories is investigating scalable architectural simulation capabilities with a focus on simulating and evaluating highly scalable supercomputers for high performance computing applications. There is a growing demand for RTL model integration to provide the capability to simulate customized node architectures and heterogeneous systems. This report describes the first steps integrating the ESSENTial Signal Simulation Enabled by Netlist Transforms (ESSENT) tool with the Structural Simulation Toolkit (SST). ESSENT can emit C++ models from models written in FIRRTL to automatically generate components. The integration workflow will automatically generate the SST component and necessary interfaces to ’plug’ the ESSENT model into the SST framework.

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Though the method-of-moments implementation of the electric-field integral equation plays an important role in computational electromagnetics, it provides many code-verification challenges due to the different sources of numerical error and their possible interactions. Matters are further complicated by singular integrals, which arise from the presence of a Green's function. In this report, we document our research to address these issues, as well as its implementation and testing in Gemma.

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Seven generation III+ and generation IV nuclear reactor types, based on twelve reactor concepts surveyed, are examined using functional decomposition to extract relevant operational technology (OT) architecture information. This information is compared to existing nuclear power plants (NPPs) OT architectures to highlight novel and emergent cyber risks associated with next generation NPPs. These insights can help inform operational technology architecture requirements that will be unique to a given reactor type. Next generation NPPs have streamlined OT architectures relative to the current generation II commercial NPP fleet. Overall, without compensatory measures that provide sufficient and efficient cybersecurity controls, next generation NPPs will have increased cyber risk. Verification and validation of cyber-physical testbeds and cyber risk assessment methodologies may be an important next step to reduce cyber risk in the OT architecture design and testing phase. Coordination with safety requirements can result in OT architecture design being an iterative process.

While it is likely practically a bad idea to shrink a transistor to the size of an atom, there is no arguing that it would be fantastic to have atomic-scale control over every aspect of a transistor – a kind of crystal ball to understand and evaluate new ideas. This project showed that it was possible to take a niche technique used to place dopants in silicon with atomic precision and apply it broadly to study opportunities and limitations in microelectronics. In addition, it laid the foundation to attaining atomic-scale control in semiconductor manufacturing more broadly.

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The adoption of distributed photovoltaic (PV) systems grew significantly in recent years. Market projections anticipate future growth for both residential and commercial installations. To understand grid impacts associated with distributed PV, useful hosting capacity studies require accurate representations of the spatial distribution of PV adoptions. Prediction of PV locations and numbers depends on median income data, building use zoning maps, and permit records to understand existing trends and predict future adoption rates and locations throughout an entire city. Using the PV adoption data, advanced and realistic simulations were performed to capture the distributed PV impacts on the grid. Also, using graph theory community detection hundreds of neighborhood microgrids can be discovered for the entire city by identifying densely connected loads that are sparsely connected to other communities. Then, based on the PV adoption predictions, this work identified the contribution of PV within each of the newly discovered graph theory defined microgrid communities.

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All disciplines that use models to predict the behavior of real-world systems need to determine the accuracy of the models’ results. Techniques for verification, validation, and uncertainty quantification (VVUQ) focus on improving the credibility of computational models and assessing their predictive capability. VVUQ emphasizes rigorous evaluation of models and how they are applied to improve understanding of model limitations and quantify the accuracy of model predictions.

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An anti-viral coating for the nose & throat is needed to quickly address the spread of COVID-19 infections and to aid future pandemics. Current nasal delivery systems are typically a 1-spray homogenous solution, which is convenient but may not be as efficient or durable as a multi-spray solution that contains a binding layer to anchor the anti-viral components. Many of the current nasal solutions being investigated to aid in pandemic response have solution-based mechanisms of action and are quickly cleared from the nose/mouth limiting their efficacy lifetime. A multi-spray approach utilizing strong intermolecular forces between polymeric materials and anti-viral agents to provide a robust biocompatible coating is expected to have increased physical and chemical properties to combat viral infection. This work evaluated which tailored biocompatible materials are needed to generate a layered system to combat viral infection. This layered system takes advantage of electrostatic interactions to bind anti-viral components, shown in Figure 1. Proof of concept success was demonstrated through cell toxicity studies and anti-viral assays on both the individual layer components and the complete layered system. Project work began with chemical modifications of Xantham Gum to hydrolyzing xanthan gum to give a negatively charged polymer with varying degrees of ionic character and investigate bonding efficiencies. However, this was abandoned in favor of comparing COTS available materials listed in Table 1 (Results section). Concentration and adhesion studies were performed on layers created using the listed polymeric materials and anti-viral agents. Finally, biocompatibility of materials, layers, and coating system were confirmed through cytotoxicity studies and the efficacy of the anti-viral properties tested with phi6 bacteriophage as a SARS-CoV2 surrogate.

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The ability to model ductile rupture in metal parts is critical in highly stressed applications. The initiation of a ductile fracture is a function of the plastic strain, the stress state, and stress history. This paper develops a ductile rupture failure surface for PH13-8Mo H950 steel using the Xue-Wierzbicki failure model. The model is developed using data from five tensile specimen tests conducted at -40⁰C and 20⁰C. The specimens are designed to cover a Lode parameter range of 0 and 1 with a stress triaxiality range from zero in pure shear to approximately 1.0 in tension. The failure surface can be implemented directly into the finite element code or used as a post processing check.

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This report presents the results of the “Foundations of Rigorous Cyber Experimentation” (FORCE) Laboratory Directed Research and Development (LDRD) project. This project is a companion project to the “Science and Engineering of Cyber security through Uncertainty quantification and Rigorous Experimentation” (SECURE) Grand Challenge LDRD project. This project leverages the offline, controlled nature of cyber experimentation technologies in general, and emulation testbeds in particular, to assess how uncertainties in network conditions affect uncertainties in key metrics. We conduct extensive experimentation using a Firewheel emulation-based cyber testbed model of Invisible Internet Project (I2P) networks to understand a de-anonymization attack formerly presented in the literature. Our goals in this analysis are to see if we can leverage emulation testbeds to produce reliably repeatable experimental networks at scale, identify significant parameters influencing experimental results, replicate the previous results, quantify uncertainty associated with the predictions, and apply multi-fidelity techniques to forecast results to real-world network scales. The I2P networks we study are up to three orders of magnitude larger than the networks studied in SECURE and presented additional challenges to identify significant parameters. The key contributions of this project are the application of SECURE techniques such as UQ to a scenario of interest and scaling the SECURE techniques to larger network sizes. This report describes the experimental methods and results of these studies in more detail. In addition, the process of constructing these large-scale experiments tested the limits of the Firewheel emulation-based technologies. Therefore, another contribution of this work is that it informed the Firewheel developers of scaling limitations, which were subsequently corrected.

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Sandia National Laboratories continued evaluation of the total system performance assessment (TSPA) for License Application (LA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY21 task included continued operation of the cluster; maintenance of the TSPA-LA models (with GoldSim 9.60.300); continued assessment of the status of the Infiltration Model; (a process model that feeds the TSP -LA) and preliminary assessments of the Unsaturated Zone Flow Model and the Saturated Zone Flow and Transport Model Abstraction (process models that feed the TSPA-LA). The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analyses.

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This document is a summary of electric vehicle (EV) experiences in Hawaii. It is meant to be informative but does not present any new technical analysis except for the development of key lessons learned that could be applied in similar contexts. The electrification of transportation is essential for Hawaii's energy goal. An electrification of transportation strategy complements other energy policy goals, increases clean energy impacts, and provides customer value. By the end of 2020, there were over 12,000 EVs registered in Hawaii (about 1 percent of all cars). That number is expected to grow, based on the results from recent surveys and studies in Hawaii. Surveys pointed out the need for more charging stations, especially in places where people do business or park for long periods of the day. Participation in controlled charging programs should have attractive incentives since a majority of EV owners would not be willing to interrupt their EV charging for demand response. Various studies have confirmed the EV potential in Hawaii. For example, the JUMPSmart Maui demonstration project, a public-private partnership with Japan, helped to establish the EV charging station infrastructure in Maui and provided important information about charging behaviors. A critical backbone study commissioned by the utility recommended that 3,600 public chargers be installed by 2030 on the five islands, which confirms the need for infrastructure improvements expressed in earlier surveys. The process that emerged in Hawaii can be an example to other locations, which could heed the lessons from Hawaii's EV experiences: The importance of an overarching energy goal/objective based on a shared vision; planning and pilot projects; a strategic plan (roadmap) leveraging on initial experiences; evaluation of the effectiveness/success of actions; fine-tuning as needed; close regulatory oversight and stakeholder participation.

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton’s method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic ℎ-adaptivity and dynamic load balancing are some of Aria’s more advanced capabilities.

Aria is a Galerkin finite element based program for solving coupled-physics problems described by systems of PDEs and is capable of solving nonlinear, implicit, transient and direct-to-steady state problems in two and three dimensions on parallel architectures. The suite of physics currently supported by Aria includes thermal energy transport, species transport, and electrostatics as well as generalized scalar, vector and tensor transport equations. Additionally, Aria includes support for manufacturing process flows via the incompressible Navier-Stokes equations specialized to a low Reynolds number (Re < 1) regime. Enhanced modeling support of manufacturing processing is made possible through use of either arbitrary Lagrangian-Eulerian (ALE) and level set based free and moving boundary tracking in conjunction with quasi-static nonlinear elastic solid mechanics for mesh control. Coupled physics problems are solved in several ways including fully-coupled Newton’s method with analytic or numerical sensitivities, fully-coupled Newton-Krylov methods and a loosely-coupled nonlinear iteration about subsets of the system that are solved using combinations of the aforementioned methods. Error estimation, uniform and dynamic ℎ-adaptivity and dynamic load balancing are some of Aria’s more advanced capabilities.

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A six-month research effort has advanced the hybrid kinetic-fluid modeling capability required for developing non-thermal warm x-ray sources on Z. The three particle treatments of quasi-neutral, multi-fluid, and kinetic are demonstrated in 1D simulations of an Ar gas puff. The simulations determine required resolutions for the advanced implicit solution techniques and debug hybrid particle treatments with equation-of-state and radiation transport. The kinetic treatment is used in preliminary analysis of the non-Maxwellian nature of a gas target. It is also demonstrates the sensitivity of the cyclotron and collision frequencies in determining the transition from thermal to non-thermal particle populations. Finally, a 2D Ar gas puff simulation of a Z shot demonstrates the readiness to proceed with realistic target configurations. The results put us on a very firm footing to proceed to a full LDRD which includes continued development transition criteria and x-ray yield calculation.

Physical protection of public buildings has long been a concern of police and security services where a balance of facility security and personnel safety is vital. Due to the nature of public spaces, the use of permanently installed and deploy-on-demand physical barrier systems must be safe for the legitimate occupants and visitors of that space. Such systems must seek to mitigate the personal and organizational consequences of unintentionally seriously injuring or killing an innocent bystander by slamming a heavy, rigid, and quick-deploying barrier into place. Consideration and implementation of less-than-lethal technologies is necessary to reduce risk to visitors and building personnel. One potential barrier solution is a fast-acting, high-strength, composite airbag barrier system for doorways and hallways to quickly deploy a less-than-lethal barrier at entry points as well as isolate intruders who have already gained access. This system is envisioned to be stored within an architecturally attractive selectively frangible shell that could be permanently installed at a facility or installed in remote or temporary locations as dictated by risk. The system would be designed to be activated remotely (hardwired or wireless) from a Central Alarm Station (CAS) or other secure location.

This report summarizes GADRAS methods and gamma spectrometry rodeo uranium isotopic analysis results for high energy resolution H3D M400 cadmium zinc telluride (CZT) and ORTEC Micro Detective high-purity germanium (HPGe) spectra of uranium isotopic standards collected at Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL) over a two-year measurement campaign. During the campaign, measurements were performed with the detectors unshielded, side shielded, and collimated. In addition, measurements of the uranium isotopic standards were performed unshielded and shielded.

We present an overview of the design and development of optical self-emission and debris imaging diagnostics for the Z Machine at Sandia National Laboratories. These diagnostics were designed and implemented to address several gaps in our understanding of visibly emitting phenomenon on Z and the post-shot debris environment. Optical emission arises from plasmas that form on the transmission line that delivers energy to Z loads and on the Z targets themselves; however, the dynamics of these plasmas are difficult to assess without imaging data. Addressing this, we developed a new optical imager called SEGOI (Self-Emission Gated Optical Imager) that leverages the eight gated optical imagers and two streak cameras of the Z Line VISAR system. SEGOI is a low cost, side-on imager with a 1 cm field of view and 30-50 µm spatial resolution, sensitive to green light (540-600 nm). This report outlines the design considerations and development of this diagnostic and presents an overview of the first diagnostic data acquired from four experimental campaigns. SEGOI was fielded on power flow experiments to image plasmas forming on and between transmission lines, on an inertial confinement fusion experiment called the Dynamic Screw Pinch to image low density plasmas forming on return current posts, on an experiment designed to measure the magneto Rayleigh-Taylor instability to image the instability bubble trajectory and self-emission structures, and finally on a Magnetized Liner Inertial Fusion (MagLIF) experiment to image the emission from the target. The second diagnostic developed, called DINGOZ (Debris ImagiNG on Z), was designed to improve our understanding of the post-shot debris environment. DINGOZ is an airtight enclosure that houses electronics and batteries to operate a high-speed (10-400 kfps) camera in the Z Machine center section. We report on the design considerations of this new diagnostic and present the first high-speed imaging data of the post-shot debris environment on Z.

The National Nuclear Security Agency (NNSA) initiated the Minority Serving Institution Partnership Plan (MSIPP) 1) to align investments in a university capacity and workforce development with the NNSA mission to develop the needed skills and talent for NNSA’s enduring technical workforce at the laboratories and production plants, and 2) to enhance research and education at under-represented colleges and universities. Out of this effort, MSIPP launched a new consortium in early FY17 focused on Tribal Colleges and Universities (TCUs) known as the Advanced Manufacturing Network Initiative (AMNI). This consortium has been extended for FY20 and FY21. The following report summarizes the status update during this quarter.

Pulsed-power generators using the magnetic loading technique are able to produce well-controlled continuous ramp compression of condensed matter for high-pressure equation-of-state studies. X-ray diffraction (XRD) data from dynamically compressed samples provide direct measurements of the elastic compression of the crystal lattice, onset of plastic flow, strength-strain rate dependence, structural phase transitions, and density of crystal defects such as dislocations. Here, we present a cost effective, compact X-ray source for XRD measurements on pulsed-power-driven ramp-loaded samples. This combination of magnetically-driven ramp compression of materials with single, short-pulse XRD diagnostic will be a powerful capability for the dynamic materials community. The success in fielding this new XRD diagnostic dramatically improves our predictive capability and understanding of rate-dependent behavior at or near phase transition. As Sandia plans the next-generation pulse-power driver platform, a key element needed to deliver new state-of-the-art experiments will be having the necessary diagnostic tools to probe new regimes and phenomena. These diagnostics need to be as versatile, compact, and portable as they are powerful. The development of a platform-independent XRD diagnostic gives Sandia researchers a new window to study the microstructure and phase dynamics of materials under load. This project has paved the way for phase transition research in a variety of materials with mission interest.

This report provides a demonstration of MELCOR and MELCOR Accident Consequence Code System (MACCS) capabilities to perform a dose assessment for a Molten Salt Reactor (MSR) off-gas system. A primary containment system salt spill is used as the off-normal scenario, along with a normal operation dose assessment for comparison. This report discusses the tools, methods, and information used in this assessment so that it may be utilized as a starting point for future advanced reactor consequence analyses. This report also highlights several gaps, to include the need for reactor inventory information specific to advanced reactors, and the need for specific atmospheric transport models that take into account the unique deposition behaviors of tritium and carbon-14, and makes recommendations for closing these gaps. This report satisfies the DOE NE Milestone M4RD-21SN0601062.

The direct reactor auxiliary cooling system is a very robust, passive safety system that is designed to remove up to 2.36 MW of heat from the reactor during accident conditions. This report details a variety of DRACS degradation conditions and their effect on the safety of the reactor. This preliminary investigation shows that only two of the three DRACS loops are necessary to quickly suppress the decay heat produced by a newly shut down reactor. Even with a single DRACS loop operational, the maximum salt temperature observed was far below the safety specification of the plant (1173 K). When investigating the degraded performance of each DRACS loop, the short-term maximum salt temperature observed was strongly dependent on the DHX performance but was unaffected by the TCHX performance. However, even a heavily degraded DHX heat transfer performance was sufficient to halt the rising salt temperature due to decay heat. Further investigation should be done to characterize the effects of TCHX performance degradation at longer time scales. High levels of TCHX degradation were shown to lead to a reactor salt temperature minimum after a few hours of operation followed by a steady increase in temperature. With reduced ability to exhaust heat to the environment, it is possible the DRACS would be unable to maintain cooling during a long loss of active cooling event.

Magnetic microscopy with high spatial resolution helps to solve a variety of technical problems in condensed-matter physics, electrical engineering, biomagnetism, and geomagnetism. In this work we used quantum diamond magnetic microscope (QDMM) setups, which use a dense uniform layer of magnetically-sensitive nitrogen-vacancy (NV) centers in diamond to image an external magnetic field using a fluorescence microscope. We used this technique for imaging few-micron ferromagnetic needles used as a physically unclonable function (PUF) and to passively interrogate electric current paths in a commercial 555 timer integrated circuit (IC). As part of the QDMM development, we also found a way to calculate ion implantation recipes to create diamond samples with dense uniform NV layers at the surface. This work opens the possibility for follow-up experiments with 2D magnetic materials, ion implantation, and electronics characterization and troubleshooting.

This LDRD supported efforts to significantly advance the scanning ultrafast electron microscope (SUEM) for spatial-temporal mapping of charge carrier dynamics in semiconductor materials and microelectronic devices. Sandia's SUEM capability in Livermore, CA, was built and demonstrated with previous LDRD funding; however, the stability and usability of the tool limited the throughput for analyzing samples. A new laser alignment strategy improved the stability of the SUEM, and the design and characterization of a new micro-channel plate (MCP)- based detector improved the signal-to-noise of the SUEM signal detection. These enhancements to the SUEM system improved throughput by over two orders of magnitude (before, a single time series of SUEM measurements would take several days to several weeks to acquire; now, the same measurements can be completed in~90 minutes in an automated fashion). The SUEM system can now be routinely used as an analytical instrument and will be a central part of several multi-year projects starting in FY22.

In order for analysts to be able to do their work, they sift through hundreds, thousands, or even millions of documents to make connections between entities of interest. This process is time consuming, tedious, and prone to potential error from missed connections or connections made that should not have been. There exist many tools in natural language processing, or NLP, to extract information from documents. However, when it comes to relationship extraction, there has been varied success. This project began with a goal to solve the relationship extraction problem which developed into a deeper understanding of the problem and the associated challenges for solving this problem on a general scale. In this report, we explain our research and approach to relationship extraction, identify other auxiliary problems in NLP that provide additional challenges to solving relationship extraction generally, explain our analysis of the current state of relationship extraction, and postulate future work to address these problems.

The aim of this project was to advance single-cell RNA-Seq methods toward the establishment of a platform that may be used to simultaneously interrogate the gene expression profiles of mammalian host cells and bacterial pathogens. Existing genetic sequencing methods that measure bulk groups of cells do not account for the heterogeneity of cell-microbe interactions that occur within a complex environment, have limited efficiency, and cannot simultaneously interrogate bacterial sequences. In order to overcome these challenges, separate biochemistry workflows were developed based on a No-So-Random hexamer priming strategy or libraries of targeted molecular probes. Computational tools were developed to facilitate these methods, and feasibility was demonstrated for single-cell RNA-Seq for both bacterial and mammalian transcriptomes. This work supports cross-agency national priorities on addressing the threat of biological pathogens and understanding the role of the microbiome in modulating immunity and susceptibility to infection.

This report describes the risk-informed technical elements that will contribute to a defense-in-depth assessment for cybersecurity. Risk-informed cybersecurity must leverage the technical elements of a risk-informed approach appropriately in order to evaluate cybersecurity risk insights. HAZCADS and HAZOP+ are suitable methodologies to model the connection between digital harm and process hazards. Risk assessment modeling needs to be expanded beyond HAZCADS and HAZOP+ to consider the sequence of events that lead to plant consequences. Leveraging current practices in PRA can lead to categorization of digital assets and prioritizing digital assets commensurate with the risk. Ultimately, the culmination of cyber hazard methodologies, event sequence modeling, and digital asset categorization will facilitate a defense-in-depth assessment of cybersecurity.

Graph partitioning has been an important tool to partition the work among several processors to minimize the communication cost and balance the workload. While accelerator-based supercomputers are emerging to be the standard, the use of graph partitioning becomes even more important as applications are rapidly moving to these architectures. However, there is no distributed-memory-parallel, multi-GPU graph partitioner available for applications. We developed a spectral graph partitioner, Sphynx, using the portable, accelerator-friendly stack of the Trilinos framework. In Sphynx, we allow using different preconditioners and exploit their unique advantages. We use Sphynx to systematically evaluate the various algorithmic choices in spectral partitioning with a focus on the GPU performance. We perform those evaluations on two distinct classes of graphs: regular (such as meshes, matrices from finite element methods) and irregular (such as social networks and web graphs), and show that different settings and preconditioners are needed for these graph classes. The experimental results on the Summit supercomputer show that Sphynx is the fastest alternative on irregular graphs in an application-friendly setting and obtains a partitioning quality close to ParMETIS on regular graphs. When compared to nvGRAPH on a single GPU, Sphynx is faster and obtains better balance and better quality partitions. Sphynx provides a good and robust partitioning method across a wide range of graphs for applications looking for a GPU-based partitioner.

Thermographic phosphors have been employed for temperature sensing in challenging environments, such as on surfaces or within solid samples exposed to dynamic heating, because of the high temporal and spatial resolution that can be achieved using this approach. Typically, UV light sources are employed to induce temperature-sensitive spectral responses from the phosphors. However, it would be beneficial to explore x-rays as an alternate excitation source to facilitate simultaneous x-ray imaging of material deformation and temperature of heated samples and to reduce UV absorption within solid samples being investigated. The phosphors BaMgAl10O17:Eu (BAM), Y2SiO5:Ce, YAG:Dy, La2O2S:Eu, ZnGa2O4:Mn, Mg3F2GeO4:Mn, Gd2O2S:Tb, and ZnO were excited in this study using incident synchrotron x-ray radiation. These materials were chosen to include conventional thermographic phosphors as well as x-ray scintillators (with crossover between these two categories). X-ray-induced thermographic behavior was explored through the measurement of visible spectral response with varying temperature. The incident x-rays were observed to excite the same electronic energy level transitions in these phosphors as UV excitation. Similar shifts in the spectral response of BAM, Y2SiO5:Ce, YAG:Dy, La2O2S:Eu, ZnGa2O4:Mn, Mg3F2GeO4:Mn, and Gd2O2S:Tb were observed when compared to their response to UV excitation found in literature. Some phosphors were observed to thermally quench in the temperature ranges tested here, while the response from others did not rise above background noise levels. This may be attributed to the increased probability of non-radiative energy release from these phosphors due to the high energy of the incident x-rays. These results indicate that x-rays can serve as a viable excitation source for phosphor thermometry.

Additively manufactured (AM) stainless steels (SSs) exhibit numerous microstructural differences compared to their wrought counterparts, such as Cr-enriched dislocation cell structures. The influence these unique features have on a SSs corrosion resistance are still under investigation with most current works limited to laboratory experiments. The work herein shows the first documented study of AM 304L and 316L exposed to a severe marine environment on the eastern coast of Florida with comparisons made to wrought counterparts. Coupons were exposed for 21 months and resulted in significant pitting corrosion to initiate after 1 month of exposure for all conditions. At all times, the AM coupons exhibited lower average and maximum pit depths than their wrought counterparts. After 21 months, pits on average were 4 μm deep for AM 316L specimen and 8 μm deep for wrought specimen. Pits on the wrought samples tended to be nearly hemispherical and polished with some pits showing crystallographic attack while pits on AM coupons exhibited preferential attack at melt pool boundaries and the cellular microstructure.

Ongoing progress in synthetic biology, metabolic engineering, and catalysis continues to produce a diverse array of advanced biofuels with complex molecular structure and functional groups. In order to integrate biofuels into existing combustion systems, and to optimize the design of next-generation combustion systems, understanding connections between molecular structure and ignition at low-temperature conditions (< 1000 K) remains a priority that is addressed in part using chemical kinetics modeling. The development of predictive models relies on detailed information, derived from experimental and theoretical studies, on molecular structure and chemical reactivity, both of which influence the balance of chain reactions that occur during combustion – propagation, termination, and branching. In broad context, three main categories of reactions affect ignition behavior: (i) initiation reactions that generate a distribution of organic radicals, R˙; (ii) competing unimolecular decomposition of R˙ and bimolecular reaction of R˙ with O2; (iii) decomposition mechanisms of peroxy radical adducts (ROO˙), including isomerization via ROO˙ ⇌ Q˙OOH. All three categories are influenced by functional groups in different ways, which causes a shift in the balance of chain reactions that unfold over complex temperature- and pressure-dependent mechanisms. The objective of the present review is three-fold: (1) to provide a historical account of research on low-temperature oxidation of biofuels, including initiation reactions, peroxy radical reactions, Q˙OOH-mediated reaction mechanisms, and chain-branching chemistry; (2) to summarize the influence of functional groups on chemical kinetics relevant to chain-branching reactions, which are responsible for the accelerated production of radicals that leads to ignition; (3) to identify areas of research that are needed – experimentally and computationally – to address fundamental questions that remain. Results from experimental, quantum chemical, and chemical kinetics modeling studies are reviewed for several classes of biofuels – alcohols, esters, ketones, acyclic ethers and cyclic ethers – and are compared against analogous results in alkane oxidation. The review is organized into separate sections for each biofuel class, which include studies on thermochemistry and bond dissociation energies, rate coefficients for initiation reactions via H-abstraction and related branching fractions, reaction mechanisms and product formation from reactive intermediates, ignition delay times, and chemical kinetics modeling. Each section is then summarized in order to identify areas for which additional functional group-specific work is required. The review concludes with an outline for research directions for improving the fundamental understanding of biofuel ignition chemistry and related chemical kinetics modeling.

Supplementing an existing high-quality seismic monitoring network with openly available station data could improve coverage and decrease magnitudes of completeness; however, this can present challenges when varying levels of data quality exist. Without discerning the quality of openly available data, using it poses significant data management, analysis, and interpretation issues. Incorporating additional stations without properly identifying and mitigating data quality problems can degrade overall monitoring capability. If openly available stations are to be used routinely, a robust, automated data quality assessment for a wide range of quality control (QC) issues is essential. To meet this need, we developed Pycheron, a Python-based library for QC of seismic waveform data. Pycheron was initially based on the Incorporated Research Institutions for Seismology's Modular Utility for STAtistical kNowledge Gathering but has been expanded to include more functionality. Pycheron can be implemented at the beginning of a data processing pipeline or can process stand-alone data sets. Its objectives are to (1) identify specific QC issues; (2) automatically assess data quality and instrumentation health; (3) serve as a basic service that all data processing builds on by alerting downstream processing algorithms to any quality degradation; and (4) improve our ability to process orders of magnitudes more data through performance optimizations. This article provides an overview of Pycheron, its features, basic workflow, and an example application using a synthetic QC data set.

We investigate the unitary mechanisms related to the surface migration of vacancies in dilute Ni-Cr alloys via first-principle calculations. We survey a complete set of surface and sub-surface migration paths for vacancies near the (100) free surface and calculate the corresponding migration barriers. Our results show that a vacancy migrating towards the free surface will face lower energy barriers to migrate via atomic exchange with a neighboring Cr atom rather than with a Ni atom. Once a vacancy reaches the free surface, it will be trapped there. Our results also reveal that, when a Cr atom sits in the atomic plane just below the free surface, any in-plane vacancy hopping jump that would result in the vacancy sitting on top a subsurface Cr atom is energetically unfavorable. Taken together, these fundamental unitary surface migration mechanisms offer insights into the complex interactions between surface segregation and vacancy migration phenomena in Ni-Cr-based alloys.