Publications

This is a single slide that highlights MESA packaging and integration capabilities.

Photonic Doppler velocimetry (PDV) is an established technique for measuring the velocities of fast-moving surfaces in high-energy-density experiments. In the standard approach to PDV analysis, the short-time Fourier transform (STFT) is used to generate a spectrogram from which the velocity history of the target is inferred. The user chooses the form, duration, and separation of the window function. Here, we present a Bayesian approach to infer the velocity directly from the PDV oscilloscope trace, without using the spectrogram for analysis. This is clearly a difficult inference problem due to the highly periodic nature of the data, but we find that with carefully chosen prior distributions for the model parameters, we can accurately recover the injected velocity from synthetic data. We validate this method using PDV data collected at the STAR two-stage light gas gun at Sandia National Laboratories, recovering shock-front velocities in quartz that are consistent with those inferred using the STFT-based approach and are interpolated across regions of low signal-to-noise data. Although this method does not rely on the same user choices as the STFT, we caution that it can be prone to misspecification if the chosen model is not sufficient to capture the velocity behavior. Analysis using posterior predictive checks can be used to establish whether a better model is required, although more complex models come with additional computational cost, often taking more than several hours to converge when sampling the Bayesian posterior. We, therefore, recommend it be viewed as a complementary method to that of the STFT-based approach.

A common method of determining elastic material properties is utilizing an ultrasonic pulse receiver. This is a non-destructive test (NDT) causing no plastic deformation of the material. It requires a pulse generator, oscilloscope, and two transducers to measure the sound velocity of a material. Both longitudinal and shear sound velocities may be obtained by utilizing the appropriate transducer. Acoustic nondestructive testing methods are often used in manufacturing certification processes and f

This report documents the work done as part of the “Enhancing Developer Productivity” level 2 milestone. The team surveyed developers about impediments and successes; improved our CI pipeline monitoring and reporting; developed tools for line coverage reporting and analysis; improved compiler warning adherence in SIERRA; prototyped static analysis, AI, and mutation testing tooling in SIERRA; and developed a 3-5 year SIERRA plan document to help these initiatives continue past this milestone.

This project advanced the development of an active hybrid mooring system integrating experimental testing with numerical simulation to capture complex mooring dynamics not feasible in existing wave basins, including deep-water and shared mooring interactions. Verification testing of the complete hybrid system yielded good agreement in mooring tension and platform translation responses, though discrepancies in platform pitch response indicate that further refinement may be needed in future work.

Many systems derive their electromagnetic radiation (EMR) environments from Mil-Std-464C or Mil-Std-464D, Electromagnetic Environmental Effects Requirements for Systems. This document is intended to provide additional clarity on the Mil-Std-464C and Mil-Std-464D EMR environments. This supplementary information may be used to guide tailoring of relevant environments or application to different systems with the additional consideration of any shielding that may reduce the external environments.

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

Sapphire (Al₂ ⁢O₃), known for its remarkable incompressibility at ambient conditions, plays a pivotal role in both static and dynamic compression research. Accurately characterizing its equation of state (EoS) is essential for these applications. Here, we present a complete Hugoniot of Al₂⁢ O₃ as locus of experimentally assessed, high-precision, pressure, density and temperature states up to 14 Mbar and 43 kK. The Hugoniot is established with single shock experiments using magnetically launched hyper velocity flyers on the Z Accelerator at Sandia National Laboratories. We explore principal Hugoniot states at very high shock 𝑇 and 𝑝 in the solid phase, tracking the solid-liquid boundary and culminating at 2.4-fold compression, where data provides a direct constraint on the liquid phase. Corresponding shock release data probe thermodynamic states complementary to the Hugoniot and place additional constraints on tabular EoS models. Our findings indicate a significant deviation from existing tabular EoS models for Al₂⁢ O₃ dictating a comprehensive overhaul. We develop two advanced EoSs for Al₂⁢ O₃ the SESAME 97412 model, featuring an extensive phase diagram that includes three solid phases and the liquid phase, and the updated LEOS 2200m2 model. EoS development is assisted with Quantum Molecular Dynamics simulations. Our experimental data allows for stringent testing of our EoSs. Both models accurately capture the Hugoniot of Al₂ ⁢O₃ up to the highest pressures and temperatures. Rigorous experimental determination of extreme pressures and temperatures, paired with sophisticated models, advances the frontier of EoS development beyond 1 terapascal.

Coulomb drag is a powerful tool to study interactions in coupled low-dimensional systems. Historically, Coulomb drag has been attributed to a frictional force arising from momentum transfer whose direction is dictated by the current flow. In the absence of electron-electron correlations, treating the Coulomb drag circuit as a rectifier of noise fluctuations yields similar conclusions about the reciprocal nature of Coulomb drag. In contrast, recent findings in one-dimensional systems have identified a nonreciprocal contribution to Coulomb drag that is independent of the current flow direction. In this work, we present Coulomb drag measurements between vertically coupled GaAs/AlGaAs quantum wires separated vertically by a hard barrier only 15 nm wide, where both reciprocal and nonreciprocal contributions to the drag signal are observed simultaneously, and whose relative magnitudes are temperature and gate tunable. Our study opens up the possibility of studying the physical mechanisms behind the onset of both Coulomb drag contributions simultaneously in a single device, ultimately leading to a better understanding of Luttinger liquids in multi-channel wires and paving the way for the creation of energy harvesting devices.

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Controlling the island decoration on metal nanoparticle supports is a major opportunity for improving the catalytic activity and an attractive synthetic challenge. The structure of the decorating metal determines how it interacts with the metal support and how it effectively catalyzes the reactants and the intermediates. In this work, we demonstrate that a slow-growth method maximizes the formation of Ru islands on faceted, branched Ni nanoparticles, thereby controlling the number of Ru–Ni atomic interactions and improving the catalytic activity. The Ru islands on branched Ni nanoparticles with the highest loading of Ru (9%) exhibited the highest activity for the electro-oxidation of biomass-derived 5-hydroxymethylfurfural (HMF). In conclusion, these results demonstrate the ability to synthetically control the second metal decoration to tune metal–support interactions, thereby enhancing the catalytic activity.

Coulomb drag is a powerful tool to study interactions in coupled low-dimensional systems. Historically, Coulomb drag has been attributed to a frictional force arising from momentum transfer whose direction is dictated by the current flow. In the absence of electron-electron correlations, treating the Coulomb drag circuit as a rectifier of noise fluctuations yields similar conclusions about the reciprocal nature of Coulomb drag. In contrast, recent findings in one-dimensional systems have identified a nonreciprocal contribution to Coulomb drag that is independent of the current flow direction. In this work, we present Coulomb drag measurements between vertically coupled GaAs/AlGaAs quantum wires separated vertically by a hard barrier only 15 nm wide, where both reciprocal and nonreciprocal contributions to the drag signal are observed simultaneously, and whose relative magnitudes are temperature and gate tunable. Our study opens up the possibility of studying the physical mechanisms behind the onset of both Coulomb drag contributions simultaneously in a single device, ultimately leading to a better understanding of Luttinger liquids in multi-channel wires and paving the way for the creation of energy harvesting devices.

Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook.

Diazonium compounds are synthetically useful in the production of dyes and textiles, however they are highly explosive under dry conditions. Explosion prevention becomes more difficult when new diazonium compounds are synthesized, because while some syntheses include a counterion to increase their stability, this is not always a reliable method to prevent an explosive incident. Due to the uncertainty surrounding the explosiveness of different diazonium compounds, it is important to understand how to safely clean up after an incident and how to determine when it is safe to return a laboratory to typical operational use, particularly when the incident involves a novel compound where a standard does not exist for instrument calibration. Here, an explosive event is discussed involving the synthesis of 4-bromo-benzenediazonium-2-carboxylate. Following the explosive incident and 3-step cleanup, which involved a precautionary neutralization step, samples were collected from the fume hood where the incident occurred. Because the incident involved an unstable, novel compound that is not commercially available and was deemed unsafe to resynthesize for instrument calibration, we assessed the risk of further explosion by analyzing for the stable decomposition products. Mass spectrometry analysis confirmed that the residue in the fume hood contained 5-bromosalicylic acid, a decomposition product of 4-bromo-benzenediazonium-2-carboxylate. Samples were taken from multiple points in the fume hood and analyzed to estimate the spatial distribution of the decomposition product. Based on this analysis, we inferred that the primary decomposition product was far more abundant than residual energetic, indicating the energetic had been consumed or neutralized to a trace quantity where the risk of further explosion was low. The steps presented here─specifically, initial neutralization and then analyzing the spatial distribution of expected decomposition products to assess risk when a novel explosive material is detonated in a confined space─were our approach to assess further risk following an explosion due to a novel diazonium compound without the need for any further handling or resynthesis of the energetic. Here, we present our approach and critically analyze these steps by discussing retrospective lessons learned and alternative analytical approaches.

The Stretched-Wire Alignment Technique (SWAT) is one method of magnet alignment for linear induction accelerators. The applications of SWAT have been implemented for aligning solenoid magnets on the Scorpius linear induction accelerator which will be sited at the Nevada National Security Site and the Flash X-Ray (FXR) linear induction accelerator at Lawrence Livermore National Laboratory’s Contained Firing Facility. This article describes both systematic (repeatable) and random sources of background and noise as well as practical ways to eliminate or reduce them to acceptable levels. Systematic sources include reflections from wire ends, rapid sag due to ohmic heating of the wire, magnetic materials, and shot rate. Random sources include air currents, vibration of nearby equipment, mechanical stability of test equipment, and the instruments used to measure the wire motion. Mitigations include curve fitting and adaptive noise signal cancellation, and mechanical damping. Finite Element Analysis (FEA) was used to identify and resolve a repeatable wire vibration frequency interfering with the signal resolution. Two stretched wire alignment technique set ups from Sandia National Labs and Lawrence Livermore National Lab have shown background noise sources and ways of mitigating them by either analysis methods or change of mechanical configuration. Conclusions that were drawn included the severe sensitivity of the deflection to even small external interferences of the SWAT wire such that it requires attention to detail in mechanical set up and analysis.

Ion implantation emerges as a promising technique to address the persistent challenge of lithium (Li) filament growth in solid-state electrolytes as it can induce compressive stresses inhibiting crack growth and deflect dendrites, de facto mitigating early electrolyte failure. In this study, we examine the potential paradox of ion implantation: while aiming to enhance electrolyte performance, the radiation damage associated with implantation might inadvertently compromise both the ionic conductivity and the intrinsic fracture toughness of the material, rendering the material unsuitable for battery applications. Specifically, we employed molecular dynamics simulations to examine the scope of the downsides of ion implantation, specifically: (i) reduced ionic conductivity (due to radiation-induced amorphization) and (ii) mechanical stability (due to radiation-induced embrittlement) in ion-implanted Li₇La₃Zr₂O₁₂ (LLZO) solid-state electrolytes. We explore how radiation damage impacts LLZO’s crystalline structure, Li-ion diffusion, and fracture properties at various temperatures and radiation damage levels. The study aims to provide insights into the competing effects of ion implantation and suggest potential engineering strategies for developing more robust solid-state electrolytes with improved conductivity and dendrite resistance.

Accurate calibration of irradiance measurement devices, or radiometers, is essential for ensuring the reliability of measurements in high heat applications such as concentrating solar power (CSP), aerospace, defense, and pulsed power systems. Despite the critical need, existing calibration standards and service providers are limited to irradiance levels below 100 kW/m² and specific radiation sources, which is insufficient for many applications. For instance, CSP technologies, particularly those under the Department of Energy’s Solar Energy Technologies Office (SETO) Gen 3 program, require accurate measurements of broadband irradiance at levels exceeding 2000 kW/m². In even more extreme scenarios, such as re-entry vehicles, heat levels can surpass 10000 kW/m². Current ISO standards, specifically ISO 14934–2 and ISO 14934–3, are constrained to lower irradiance levels and dependent on black body heat sources, limiting their applicability for high-intensity broadband irradiance measurements, particularly in concentrated solar applications. Here, to address this shortfall, the National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories (SNL) proposes a calibration method and facility capable of characterizing radiometers up to 2750 kW/m² using concentrated solar irradiance. Calibrating with concentrated sunlight is important for solar applications as it aligns the calibration process with the solar spectrum. This alignment is crucial for minimizing systematic errors and avoiding the need for additional corrections that may arise when radiometers designed for solar applications are calibrated using black-body or electrical sources. This paper presents the present day NSTTF characterization facility and procedure, detailing the proposed calibration method and uncertainty quantification. The presented method builds upon 1980′s NSTTF methodology and involves both theoretical and empirical methods to establish a robust relationship between gauge voltage output and irradiance intensity, quantifying both measurement and fitting errors. By addressing the limitations of existing standards and extending the characterization range, this work provides an advancement in the field of high-intensity irradiance measurement and instrumentation characterization.

We develop a Stockmayer fluid model for molecular dynamics simulations of ionic liquids that captures molecular polarization, ionic conductivity, viscosity, and glass transition temperature, using ethylammonium nitrate (EAN) as an example. The ions in EAN are treated as spheres interacting via the Lennard-Jones potential with an embedded point charge and a permanent dipole moment. We show that our simulation results for EAN are consistent with experimental data and then explore the effects of the molecular parameters on the viscosity of ionic liquids. Our results indicate that viscosity monotonically increases with ionic charge and dipole moment but non-monotonically changes with ionic diameter (or molar volume). This non-monotonic trend arises from the competition among the electrostatic interactions, molecular packing, and size asymmetry between the cation and anion. Our model also shows that long-lived ion pairs result in higher viscosities.

A photocontrolled isomerization reaction of oxabenzonorbornadiene (OBNBD) derivatives with a photo acid generator (PAG) has been developed. Under irradiation, the PAG releases an acid that catalyzes the heterolytic cleavage of the benzyl ether, triggering an aromatization process accompanied by substantial heat release that accelerates the transformation. Large exotherms can further push low reactivity substrates to achieve full conversion within minutes. This approach not only illuminates practical and environmentally benign advantages in organic synthesis but also opens new avenues for developing photoresponsive molecular architectures and advanced functional materials.

Data collected via seismic and infrasound array deployments are leveraged in the geosciences to detect and characterize a myriad of natural and anthropogenic sources. These deployments consist of numerous sensors placed in a predetermined configuration to amplify signal strength and improve the efficacy of array processing techniques used to measure signal directionality and waveform coherence. High‐fidelity feature extraction is often predicated on interstation distance as well as the frequency content and wavelength of an incident signal. Numerous array processing softwares analyze data in sequential frequency bands to obtain a more detailed characterization of a signal. However, current algorithms are limited in their ability to determine optimal array configuration for each band. We introduce an open‐source Python code, called Cardinal, to process seismic and infrasound array data in discretized time–frequency space with the option of applying an adaptive array design to determine optimal subarray configuration for each frequency band. To reduce computational time, the array processing step can be run in parallel using multithreading. Furthermore, the software has the capability to aggregate array processing results from different time–frequency pixels to produce separate sets of detections, or families, with added utility via the application of an adaptive semblance threshold, which aids in isolating signals‐of‐interest from coherent background noise. Upon appropriate configuration, Cardinal exhibits the potential to combine distinct seismic and infrasound phases into separate families.

Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

Pyomo is an open-source optimization modeling software that has undergone significant evolution since its inception in 2008. Pyomo has evolved to enhance flexibility, solver integration, and community engagement. Modern collaborative tools for open-source software have facilitated the development of new Pyomo functionality and improved our development process through automated testing and performance-tracking pipelines. However, Pyomo faces challenges typical of research software, including resource limitations and knowledge retention. The Pyomo team's commitment to better development practices and community engagement reflects a proactive approach to these issues. We describe Pyomo's development journey, highlighting both successes and failures, in the hopes that other open-source research software packages may benefit from our experiences.

Characterization of induced microseismicity at a carbon dioxide (CO₂) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO₂ injection. This approach enhances seismic monitoring capabilities at CO₂ injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Frontal ring-opening metathesis polymerization (FROMP) is a promising energy-efficient approach to fabricate polymeric materials. Recent advances have demonstrated FROMP for diverse applications, including additive manufacturing, composites, and foams. However, the characteristic properties of the front are currently controlled primarily by varying the resin composition or the environmental conditions. In this work we present an approach to control FROMP of dicyclopentadiene (DCPD) using photochemical methods. A photobase generator is used to inhibit FROMP of DCPD with UV light while a photosensitizer and co-initiator are used to accelerate FROMP with blue light, enabling orthogonal active photocontrol of front velocity. In addition, photoinhibition-enabled lithographic patterning of frontal polymerizations is demonstrated. Frontal polymerizations are spatially controlled, redirected, and even split into diverging fronts. This work establishes a foundation for advanced control of frontal polymerizations, enabling innovation in traditional and additive manufacturing, as well as emerging processes like morphogenic manufacturing.

Here, a systems governance approach emphasizes a holistic perspective that identifies and navigates the interdependencies and conflicts between security and operational needs. Governance is defined as a collection of metasystems that provide the necessary constraints and processes to support, steer, adapt, transform, and sustain a system (Keating et al. 2022). Utilizing the Cynefin framework, which distinguishes between simple, complicated, complex, and chaotic environments (Snowden and Boone 2007), the article highlights the challenges faced by nuclear power plants in predatory contexts and the importance of integrating security objectives into governance frameworks. By incorporating security as a fundamental aspect of governance, the article underscores its significance for persistence, adaptation, and transformation in the face of uncertainty. Additionally, it introduces key heuristics of systems security, such as the importance of context, knowledge‐based decision‐making, and organization‐specific sociological factors (Williams and Caskey 2024). Ultimately, this work provides valuable insights into enhancing resilient operations in complex environments by reinforcing the connection between effective governance and security in systems engineering.