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DOE OSTI2024

High-Fidelity Accelerated Design of High-performance Electrochemical Systems

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. To accelerate this process, our team has developed the AutoMat platform. AutoMat can accelerate development of new electrochemical materials along two avenues: first, automated input generation and management of simulations at multiple lengthscales as well as “handoff” of outputs from one lengthscale as inputs to the next; and second, replacement of the most computationally intensive simulation processes with machine-learned surrogate models. The crux of our team’s effort was not “reinventing the wheel” by developing entirely new techniques, but rather building a “superhighway” that allows existing state-of-the-art techniques to run faster and more smoothly than before. AutoMat can utilize tools spanning from first-principles quantum chemistry computations to automated robotic experimentation, and is driven by design space search techniques to reduce the number of iterations through the full simulation loop by rapidly targeting promising regions of design spaces such as single-atom alloy catalysts or blends of liquid electrolytes.

25 ENERGY STORAGE↗

DOE OSTI2026

Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization

Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.

Mathematics and Computing↗

DOE OSTI2026

Report on the Update and Codification of Expert Knowledge into ASTM Standard Practice E521

The overall purpose of the Grand Challenge Integrated Research Project (IRP), Accelerating the Qualification of Materials to Enable Rapid Deployment of Advanced Reactors, is to fulfill several objectives. The first objective is to complete and demonstrate/establish the process for predicting the microstructure and properties of structural materials in reactor and at high doses using ion irradiation as an accurate predictive tool for assessing behavior under reactor irradiation. The second objective is for this process to be adopted as part of an ASTM standard, and to work with the US Nuclear Regulatory Commission to utilize this standard in licensing decisions on advanced reactor designs for which such data are nonexistent or impractical to achieve. The need for standardized procedures is apparent based on the outcomes of a recent round-robin experiment from laboratories using ion beams for radiation damage studies and the roadmap for ion beam technologies to address challenges for the advancement of nuclear energy technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Report on the Update and Codification of Expert Knowledge into ASTM Standard Practice E521

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Yeast Transformation on Hamilton Vantage (YT Vantage) v1

Our software program is designed for the Hamilton Vantage liquid handling robot, automating the Build step in the Design-Build-Test-Learn (DBTL) cycle for Saccharomyces cerevisiae. This program minimizes human intervention, enabling rapid identification of pathway bottlenecks and genes that enhance verazine production. The program takes competent yeast and plasmid DNA as input and generates an output library of engineered strains compatible with automated colony picking, high-throughput culturing, and chemical extraction for downstream LC-MS analysis. A user-friendly interface, developed using the Hamilton Method Editor software, allows for on-demand parameter customization. By automating this process, our program streamlines the construction of Saccharomyces cerevisiae, reducing manual labor and increasing efficiency. While the manual process is well-documented, integration with robotic automation is less common, making our program a valuable tool for researchers. With this software, we achieved 2-5 fold increases in verazine production, demonstrating its potential to accelerate research in this field.

Louie, Randy [Lawrence Berkeley National Laborator↗

DOE OSTI2024

Optimizing DOE Opportunities to Research Land–Atmosphere Interactions in the U.S. Southeast (Workshop Report)

The southeastern United States (Southeast), with its complex and varied environments, is an area of tremendous economic, ecological, and societal importance to the country. The region is characterized by heterogeneous landscapes (i.e., geology and soil type) and a long history of human land use coupled with a warm temperature regime and high precipitation. As a result, soil erosion and deposition are pronounced, vegetation recovery is rapid, and human modification is extensive across the region. To better understand land–atmosphere interactions in this important and complex region, research communities supported by the U.S. Department of Energy’s Biological and Environmental Research (BER) program identified the Southeast as a priority region of interest. In fall 2024, the third Atmospheric Radiation Measurement Mobile Facility (AMF3), one of three mobile monitoring facilities designed to collect atmospheric and climate data from undersampled regions around the world, will begin operations in northwestern Alabama’s Bankhead National Forest (BNF). The AMF3-BNF 5-year deployment, from 2024–2029, will monitor the effects of feedbacks among aerosols, clouds, and precipitation on plant physiology and canopy-scale fluxes. It will also focus on scale aggregation to resolve the role of local forcing on larger-scale processes. To enable broader AMF3 involvement by the science community, the BER Environmental System Science (ESS) program organized the Southeast Land– Atmosphere Research Opportunities (SELARO) workshop in August 2023. The purpose was to identify gaps in scientific understanding of terrestrial processes in the Southeast (defined as states bounded by the Gulf of Mexico to the south, the Atlantic Ocean to east, the Mississippi River to the west, and extending through Tennessee and North Carolina to the north) and explore opportunities to use the AMF3-BNF deployment to coordinate and leverage research efforts across the region. Many parts of the Southeast have experienced repeated anthropogenic forcings. Farming, hunting, burning, and settlement of the region by Indigenous Peoples first shaped the distribution of plant communities, which in turn influenced European colonization patterns. Timber harvesting was common during the expansion of European settlements, and production forestry continues today. Agricultural production was extensive and then waned through the 20th century, creating a period of afforestation following agricultural abandonment. Today, many formerly agricultural landscapes are undergoing rapid urbanization and suburbanization. Overlying these patterns of anthropogenic land use are frequent disturbances from hurricanes, tornadoes, wildfires, drought, flooding, ice storms, and the occasional blizzard. An additional characteristic of the Southeast is its overall landscape complexity. Unlike the western United States, where broad expanses may share similar characteristics, Southeast topography, drainage patterns, vegetation, and development patterns vary widely across relatively small spatial scales (<1 km). This is due to the region’s underlying geology and soil development, species biodiversity patterns, and land ownership and use coupled with strong forces of erosion, weathering, and rapid plant growth in the warm, wet climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Description of FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment

The urgency to deliver fusion power is growing now more than ever, with increasing pressure for both public programs and private companies to meet milestones timelines and overcome significant remaining technical challenges to ensure growth of a nascent fusion industry in time to meet rapidly growing clean energy demands. With incredible advancements in computation and years of investment in fusion model development and validation, integrated modeling is poised to fill a key role in accelerating the timeline to a fusion pilot plant (FPP). Future fusion pilot plants will operate in regimes far beyond current experience, and device design will rely on physics-based prediction and extrapolation. Many concepts will also rely on simulation to assess safety (shielding, tritium management, materials activation and lifetimes), economics and scalability before the decision to build. Importantly, integrated simulation can be used to reveal and solve the complexities of system integration that may otherwise not be apparent in physical components or models developed in isolation. New experimental test facilities that produce relevant conditions to validate and resolve key technical challenges for various subsystems (materials, blankets, fuel cycle, etc.) have been repeatedly called for by the fusion community but are not yet realized. Integrated modeling has an important role in identifying realistic load conditions (thermal, electromagnetic, plasma, neutron and photon loads, etc.) and defining the components and experiments for these test facilities in order to ensure meaningful validation that sufficiently reduces modeling uncertainties and technical risk for the full integrated reactor. The Fusion REactor Design and Assessment (FREDA) SciDAC project is building a component-based integrated modeling framework & data structure to enable self-consistent, multi-fidelity, iterative optimization workflows for the fusion reactor design process. FREDA aims to shorten the time to viable designs by providing a set of flexible workflows to support the various stages of the design process using an integrated model hierarchy, ranging from the simple analytic descriptions to the highest fidelity, theory-based plasma and engineering modeling developed by the fusion and fission communities. These tools are expected to be needed for timely support of FPP design in the milestone program and in the FIRE collaboratives. The plasma simulation backbone of FREDA is IPS-FASTRAN with newly developed coupled Core-Edge Pedestal-SOL (CESOL) workflows, which is being extended to the far-SOL region up to the plasma facing components. FREDA incorporates the FERMI engineering modeling suite and will enable self-consistent evaluation of the thermal shields, limiters, blanket, magnets, and other surrounding structures with predictions of temperatures, erosion, dpa, activation, tritium generation and transport, creep, corrosion, material degradation, etc. Parametric generation of 3D CAD enables rapid iteration of component geometry in response to plasma and loading specifications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Exploding Bridgewire (EBW) Detonators: An Example of Synergistic Multiphysics

Exploding bridgewire (EBW) detonators are highly temporally reproducible explosive devices that require the rapid discharge of a high‐voltage capacitance to operate and so are immune to most of the accidental hazards associated with traditional electric detonators. They have been demonstrated to be safe enough for use in high‐consequence explosive applications. Despite continued use for over 82 years, understanding the exact mechanism of operation has remained elusive. Various researchers have ascribed either deflagration‐to‐detonation (DDT) or shock‐to‐detonation (SDT) phenomena observed in other explosive events to explain the science behind the successful engineering; however, a rigorous justification has been absent. Previously, we have demonstrated a complex interaction in EBW detonators between large electrical currents, non‐equilibrium thermodynamic material states, plasma physics, powder compaction phenomena, shock physics, photochemistry, and rapid conventional explosive chemical reaction processes. Specifically, we have made progress in understanding the complex multiphysics that operates in these detonators and demonstrating that it is a serendipitous synergy between UV light emitted from the arc formed as the bridge is electrically exploded and the accompanying short‐duration shock transmitted into the explosive powder bed that allows these devices to function at practical capacitor sizes and charge voltages. This insight not only places the topic on a firmer scientific footing but potentially enables new approaches to safe detonator design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Simulation of ultrafast transient absorption spectra of a perylene-based light harvesting antenna

Atomistic simulations of photo-induced responses in artificial light-harvesting molecular systems help to reveal the mechanisms of ultrafast intramolecular energy transfer between individual chromophores. These light-induced processes mimic the primary events occurring in natural photosynthesis. Modeling studies contribute to the design of more efficient molecular architectures enabling performance optimization for applications in light harvesting, energy conversion, and optoelectronics. Within this context, the direct comparison between simulated and experimental transient absorption pump–probe (TA-PP) spectra are especially valuable for validating theoretical approaches and deepening mechanistic understanding. Herein, we investigate the photoinduced dynamics of an antenna system composed of two naphthalene monoimides donor units covalently linked to a perylene derived acceptor. Following photoexcitation, the exciton rapidly self-traps on one of the donor units. Thereafter, efficient ultrafast energy transfer to the acceptor unit takes place via two possible pathways: either through transient exciton localization on the second donor unit or by direct transfer to the acceptor. The simulated TA-PP spectra clearly capture these distinct energy transfer pathways and enable a detailed comparison of their relative efficiencies. This highlights the system's potential for tunable exciton dynamics towards advancing light-harvesting and optoelectronic molecular materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Highly accelerated life testing (HALT): A review from a statistical perspective

Despite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated-stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). Here, this review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high-stress test-analyze-fix-test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗

DOE OSTI2025

Autonomous organic synthesis for redox flow batteries via flexible batch Bayesian optimization

Traditional trial-and-error methods for materials discovery are inefficient to meet the urgent demands posed by the rapid progression of climate change. This urgency has driven the increasing interest in integrating robotics and machine learning into materials research to accelerate experimental learning. However, idealized decision-making frameworks to achieve maximum sampling efficiency are not always compatible with high-throughput experimental workflows inside a laboratory. For multi-step chemical processes, differences in hardware capacities can complicate the digital framework by introducing constraints on the maximum number of samples in each step of the experiment, hence causing varying batch sizes in variable selection within the same batch. Therefore, designing flexible sampling algorithms is necessary to accommodate the multi-step synthesis with practical constraints unique to each high-throughput workflow. In this work, we designed and employed three strategies on a high-throughput robotic platform to optimize the sulfonation reaction of redox-active molecules used in flow batteries. Our strategies adapt to the multi-step experimental workflow, where their formulation and heating steps are separate, causing varying batch size requirements. By strategically sampling using clustering and mixed-variable batch Bayesian optimization, we were able to iteratively identify optimal conditions that maximize the yields. Our work presents a flexible approach that allows tailoring the machine learning decision-making to suit the practical constraints in individual high-throughput experimental platforms, followed by performing resource-efficient yield optimization using available open-source Python libraries.

Tamura, Clara [Univ. of Washington, Seattle, WA (U↗

DOE OSTI2025

Rapid, efficient reduction of iron oxide with ammonia

Reducing iron oxide to iron metal with fossil fuels produces circa 4% of annual anthropogenic CO 2 , ca. 2 Gt/a, as direct chemical emissions from the process. Using ammonia instead of coke—releasing only water and nitrogen as byproducts—could be a widely deployable method of decarbonizing industrial ironmaking, because ammonia carries hydrogen and can be transported easily worldwide. However, initial reports suggest that the reaction of ammonia with iron oxide is slow and inefficient, completed only in hours and using only a few percent of the ammonia supplied. Drawing on thermodynamic and kinetic analysis of the reaction, we explored high-temperature, high-flow reaction conditions designed to increase the reaction rate and the ammonia utilization. We report the reduction of iron oxide in 1 min while using 64% of the ammonia supplied. We show that these insights translate to the gram scale and the reduction of pelletized ore, a common form used industrially.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A critical review on additive manufacturing of refractory alloys from a data analytics perspective- beyond nickel-based superalloys

Refractory alloys (RAs) are promising materials due to their exceptional physicochemical properties, but most research remains at the laboratory scale. For broader adoption, advancements in manufacturing are essential. Because their high stability makes conventional methods like machining and casting difficult, additive manufacturing (AM) is emerging as an effective approach for fabricating refractory alloy components. However, AM's repeated non-equilibrium thermal cycles introduce undesired features (e.g. defects, anisotropic microstructures, and residual stresses), which are magnified due to RAs’ unique properties. This paper comprehensively reviews the state-of-the-art methods of AM for refractory alloys. It explores data analytics techniques to establish design rules based on multi-fidelity experimental and computational methods. Furthermore, it investigates integrated, collaborative efforts to harmonise standalone databases, information, knowledge, and predictive models at multi-physics, multi-stage, and multi-scale. Unlike the existing literature that focuses primarily on material systems or process fundamentals, this work provides an integrated perspective on AM of refractory alloys from a data analytics standpoint, highlighting the roles of integrated computational materials engineering (ICME), verification, validation, and uncertainty quantification (VV&UQ), and digital twin-driven qualification in overcoming data scarcity and accelerating rapid qualification.

Additive manufacturing↗

DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Analytical noise bias correction for precise weak lensing shear inference

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the ‘renoising’ noise bias correction approach introduced by METACALIBRATION. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin Legacy Survey of Space and Time (LSST) imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a per cent, meeting LSST Dark Energy Science Collaboration requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Structural Design of Bismuth Telluride Nanoplates through Process Variables

Binary pnictogen chalcogen compounds, primarily bismuth tellurides and selenides, are of great interest due to their applications in emerging quantum devices, as well as thermoelectric generators. The performance of bismuth telluride in these roles depends on its structure at the nanoscale, particularly the size, shape, and crystallinity of its nanocrystalline forms. However, current methods for controlling these features are often slow, inconsistent, or difficult to scale. Here, we demonstrate that through a solvothermal synthesis and hot injection process, precise control over the morphology of bismuth telluride nanoplates is possible with independent tuning of process variables, such as temperature and reaction time. We find that the nanoplate shape and internal porosity vary systematically with synthesis temperature and that the same morphological outcomes can be rapidly achieved at a fixed temperature by adjusting reaction duration. These results reveal that both the temperature and time can independently direct bismuth telluride morphological features, allowing for rapid, tunable synthesis strategies. Our approach offers a scalable framework, not only for bismuth telluride but also for related layered chalcogenides used in energy harvesting and quantum technologies.

Ackley, Jordan [Boise State Univ., ID (United Stat↗

DOE OSTI2026

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗