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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Visualization and Decision Making Design Under Uncertainty

Uncertainty is an important aspect to data understanding. Without awareness of the variability, error, or reliability of a dataset, the ability to make decisions on that data is limited. However, practices around uncertainty visualization remain domain-specific, rooted in convention, and in many instances, absent entirely. Part of the reason for this may be a lack of established guidelines for navigating difficult choices of when uncertainty should be added, how to visualize uncertainty, and how to evaluate its effectiveness. Unsurprisingly, the inclusion of uncertainty into visualizations is a major challenge to visualization. As work concerned with uncertainty visualization grows, it has become clear that simple visual additions of uncertainty information to traditional visualization methods do not appropriately convey the meaning of the uncertainty, pose many perceptual challenges, and, in the worst case, can lead a viewer to a completely wrong understanding of the data. These challenges are the driving motivator for this special issue.

data models↗
DOE OSTI2025

Higher-order factorization machine for accurate surrogate modeling in material design

Efficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to efficiently navigate large, high-dimensional design spaces. Among surrogate models, 2 nd -order factorization machine (FM) models are widely employed as the surrogate model in active learning algorithms due to their balance between simplicity and effectiveness. However, their quadratic nature limits their capacity to capture complex, higher-order interactions among variables, often leading to suboptimal solutions. To overcome this limitation, we propose an active learning scheme integrating a 3 rd -order FM model, capable of modeling three-variable interactions and more intricate relationships in material systems. We comprehensively evaluate the surrogate modeling performance of the 3 rd -order FM case using various objective functions. Furthermore, we examine the optimization reliability and efficiency of the 3 rd -order FM-based active learning in a real-world material design task (e.g., nanophotonic structures for transparent radiative cooling). Our study shows that the 3 rd -order FM outperforms the 2 nd -order model in both surrogate accuracy and optimization performance, highlighting higher-order models’ promises for material design and optimization problems.

Factorization machine↗
DOE OSTI2012

Prediction of Total Dissolved Gas (TDG) at Hydropower Dams throughout the Columbia

The network of dams throughout the Columbia River Basin (CRB) are managed for irrigation, hydropower production, flood control, navigation, and fish passage that frequently result in both voluntary and involuntary spillway releases. The entrainment of air in spillway releases and the subsequent exchange of atmospheric gasses into solution during passage through the stilling basin cause elevated levels of total dissolved gas (TDG) saturation. Physical processes that affect TDG exchange at hydropower facilities have been characterized throughout the CRB in site-specific studies and at real-time water quality monitoring stations. These data have been used to develop predictive models of TDG exchange which are site specific and account for the fate of spillway and powerhouse flows in the tailrace channel and resultant transport and exchange in route to the downstream dam. Currently, there exists a need to summarize the findings from operational and structural TDG abatement programs conducted throughout the CRB and for the development of a generalized prediction model that pools data collected at multiple projects with similar structural attributes. A generalized TDG exchange model can be tuned to specific projects and coupled with water regulation models to allow for the formulation of optimal water regulation schedules subject to water quality constraints for TDG supersaturation. It is proposed to develop a methodology for predicting TDG levels downstream of hydropower facilities with similar structural properties as a function of a set of variables that affect TDG exchange; such as tailwater depth, spill discharge and pattern, project head, and entrainment of powerhouse releases.

13 HYDRO ENERGY↗
DOE OSTI2012

Prediction of Total Dissolved Gas Exchange at Hydropower Dams

Total dissolved gas (TDG) supersaturation in waters released at hydropower dams can cause gas bubble trauma in fisheries resulting in physical injuries and eyeball protrusion that can lead to mortality. Elevated TDG pressures in hydropower releases are generally caused by the entrainment of air in spillway releases and the subsequent exchange of atmospheric gasses into solution during passage through the stilling basin. The network of dams throughout the Columbia River Basin (CRB) are managed for irrigation, hydropower production, flood control, navigation, and fish passage that frequently result in both voluntary and involuntary spillway releases. These dam operations are constrained by state and federal water quality standards for TDG saturation which balance the benefits of spillway operations designed for Endangered Species Act (ESA)-listed fisheries versus the degradation to water quality as defined by TDG saturation. In the 1970s, the United States Environmental Protection Agency (USEPA), under the federal Clean Water Act (Section 303(d)), established a criterion not to exceed the TDG saturation level of 110% in order to protect freshwater and marine aquatic life. The states of Washington and Oregon have adopted special water quality standards for TDG saturation in the tailrace and forebays of hydropower facilities on the Columbia and Snake Rivers where spillway operations support fish passage objectives. The physical processes that affect TDG exchange at hydropower facilities have been studied throughout the CRB in site-specific studies and routine water quality monitoring programs. These data have been used to quantify the relationship between project operations, structural properties, and TDG exchange. These data have also been used to develop predictive models of TDG exchange to support real-time TDG management decisions. These empirically based predictive models have been developed for specific projects and account for both the fate of spillway and powerhouse flows in the tailrace channel and resultant exchange in route to the next downstream dam. Currently, there exists a need to summarize the general finding from operational and structural TDG abatement programs conducted throughout the CRB and for the development of a generalized prediction model that pools data collected at multiple projects with similar structural attributes. A generalized TDG exchange model can be tuned to specific projects and coupled with water regulation models to allow the formulation of optimal daily water regulation schedules subject to water quality constraints for TDG supersaturation. A generalized TDG exchange model can also be applied to other hydropower dams that affect TDG pressures in tailraces and can be used to develop alternative operational and structural measures to minimize TDG generation. It is proposed to develop a methodology for predicting TDG levels downstream of hydropower facilities with similar structural properties as a function of a set of variables that affect TDG exchange; such as tailwater depth, spill discharge and pattern, project head, and entrainment of powerhouse releases. TDG data from hydropower facilities located throughout the northwest region of the United States will be used to identify relationships between TDG exchange and relevant dependent variables. Data analysis and regression techniques will be used to develop predictive TDG exchange expressions for various structural categories.

13 HYDRO ENERGY↗
DOE OSTI2024

Navigating Economies of Scale and Multiples for Nuclear-Powered Data Centers and Other Applications with High Service Availability Needs

Nuclear energy is increasingly being considered for such targeted energy applications as data centers in light of their high capacity factors and low carbon emissions. This paper focuses on assessing the tradeoffs between economies of scale versus mass production to identify promising reactor sizes to meet data center demands. A framework is then built using the best cost estimates from the literature to identify ideal reactor power sizes for the needs of the given data center. Results should not be taken to be deterministic but highlight the variability of ideal reactor power output against the required demand. While certain advocates claim that with the gigawatts of clean, firm energy needed, large plants are ideal, others advocate for SMRs that can be deployed in large quantities and reap the benefits from learning effects. The findings of this study showcase that identifying the optimal size for a reactor is likely more nuanced and dependent on the application and its requirements. Overall, the study does show potential economic promise for coupling nuclear reactors to data centers and industrial heat applications under certain key conditions and assumptions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Strong intracellular signal inactivation produces sharper and more robust signaling from cell membrane to nucleus

For a chemical signal to propagate across a cell, it must navigate a tortuous environment involving a variety of organelle barriers. In this work we study mathematical models for a basic chemical signal, the arrival times at the nuclear membrane of proteins that are activated at the cell membrane and diffuse throughout the cytosol. Organelle surfaces within human B cells are reconstructed from soft X-ray tomographic images, and modeled as reflecting barriers to the molecules’ diffusion. We show that signal inactivation sharpens signals, reducing variability in the arrival time at the nuclear membrane. Inactivation can also compensate for an observed slowdown in signal propagation induced by the presence of organelle barriers, leading to arrival times at the nuclear membrane that are comparable to models in which the cytosol is treated as an open, empty region. In the limit of strong signal inactivation this is achieved by filtering out molecules that traverse non-geodesic paths.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A Prototype Dual Robot System to Perform Ultrasonic and Visual Inspection for the Bottom of Double-Shell Tanks - 18624

Up to this point, ultrasonic inspection-based condition data has only been available for double-shell tank (DST) sidewalls because direct inspection access to the primary tank bottom is limited to vent duct channels in an insulating refractory pad that the primary tank rests on. Probe access through the air slot would provide the opportunity to get information about the condition of the bottom of the primary tank, but it is a challenge to deploy a crawling robot that is capable of performing nondestructive ultrasonic and visual inspection through confined air slots. As such, a conceptual prototype of a deployable inspection robot system to enable ultrasonic and visual inspection for DST base through ventilation ducts was designed and fabricated. This prototype dual robot system consisting of the AnnulusBot and the MouseBot can be integrated with nondestructive evaluation (NDE) sensors to inspect and examine the physical properties of the underside of DSTs to enable status assessment. The AnnulusBot was designed to carry the MouseBot and to be deployed to the annulus space through a riser. To deploy the MouseBot to every ventilation channel in the annulus, the arm consisting of a boom and a forearm was used to place the MouseBot on the underside of the tank. The AnnulusBot used segmented wheels enable to traverse pipes and other potential obstacles. The cable management system utilized motorized wheels to feed the cable into the ventilation channel. This would reduce the amount of work that the MouseBot would have to do during inspection. This feeding mechanism would also be used to retrieve the MouseBot at the end of its inspection. Additionally, if the MouseBot fails, this mechanism would be used for retrieval. Once deployed, the MouseBot navigates to the center of the tank via the air ventilation channel beneath the inner shell (the primary tank) while inspecting the space. The MouseBot is then retrieved from the channel by a combination of its own drive power and the AnnulusBot retracting the attached umbilical cord. Compare with the AnnulusBot, the physical dimensional limitations of the MouseBot were a greater challenge. As the surface state of the concrete of the air channels could have variable texture, and may contain debris, it was decided not to travel along this surface. The ceiling of these channels is carbon steel and can be utilized throughout the channel. To use this travel surface, magnetic wheels were employed. A foreseeable problem with the use of magnets on this surface, is that they would preclude the use of eddy currents sensors. However, other technologies like ultrasonic technology sensors should be unaffected. Laboratory scale test was performed on a mock-up single wedge of the refractory pad to evaluate maneuverability of the MouseBot. The electronic components for the prototype would work in the ranges of 150-185 deg. F (65.6-85 deg. C). Both the AnnulusBot and MouseBot will be subject to constant radiation. All major printed circuit board (PCB) microcontroller components were designed to be situated outside of the annulus space and inside the main control panel to avoid radiation-induced failure. As future work, electronic components including the Arduinos that used Atmel commercial microcontrollers will be replaced with the high temperature rated Atmel microcontrollers so that the robots can be operated reliably at ambient temperatures up to 200 deg. F (93.3 deg. C). In addition, the robots will be service life tested to evaluate performance at elevated temperature and under gamma irradiation. (authors)

42 ENGINEERING↗
DOE OSTI2019

Machine Learning Applied to a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures [Machine Learning a Variable Charge Atomistic Model for Cu/Hf Binary Alloy Oxide Heterostructures]

Alloy oxide heterostructures are essential for a wide variety of energy applications, including anticorrosion coatings, bifunctional catalysis, energy storage, as well as emerging platforms for multilevel nonvolatile memory and neuromorphic devices. The key role played by oxide composition, density, and stoichiometry in governing electrochemical reactions, interface structure, and ionic transport in alloy oxides remains largely unknown. This is primarily due to the lack of variable charge interatomic potential models that can adequately describe the various atomic/ionic interactions in alloy oxides within a unified framework. Here, we introduce a charge transfer ionic potential (CTIP) model for Cu/Hf/O alloy system and demonstrate its ability to accurately capture the complex potential energy surface owing to dynamically varying interactions, including metallic (Cu/Hf), ionic (Cu/Hf/Ox), mixed environment (interfaces). We leverage supervised machine learning methods powered by genetic algorithms coupled with local simplex optimization to efficiently navigate through a high-dimensional parameter space and identify an optimum set of an independent set of CTIP parameters. We train our model against an extensive first-principles based data set that includes lattice constants, cohesive energies, equations of state, and elastic constants of various experimentally observed polymorphs of hafnia, copper oxide, hafnium, as well as copper. Our machine-learned CTIP model captures the structure, elastic properties, thermodynamics, and energetic ordering of various polymorphs. It also accurately predicts the surface properties of both oxides and their metals. To demonstrate the suitability of our CTIP model for investigating dynamic processes, we employed it to identify the atomistic scale mechanisms associated with the initial nanoscale oxidation and surface oxide growth on Cu-doped hafnia surfaces. Here, our machine-learned CTIP model can be used to probe the dynamic response of Cu/Hf/O alloy interfaces subjected to external stimuli (e.g., electric field, pressure, temperature, strain, etc.) and a variety of atomistic phenomena including the dynamics of switching in emerging neuromorphic platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design of a Continuous Pellet Fueling System for Wendelstein 7-X

A continuous pellet fueling system (CPFS) is currently being designed at the Oak Ridge National Laboratory (ORNL) for the long pulse operation of the Wendelstein 7-X (W7-X) stellarator. The purpose of the CPFS is to provide deep continuous fueling for feedback-controlled high-density operation and mitigation of predicted hollow density profiles. As described here, the system will provide the capability to inject cylindrical pellets of solid hydrogen or deuterium into the plasma core, with flexibility to vary the pellet size, velocity, and injection frequency. Pellets are nominally of 3 mm in diameter and have a length between 1 and 4 mm. The heart of the CPFS is a vertically oriented, twin-screw extruder, cooled by three Gifford-McMahon cryocoolers in parallel, designed to form a continuous filament of hydrogen or deuterium. The filament width, which determines the pellet length, can be adjusted by means of a variable nozzle driven by a linear actuator at the base of the extruder. Coupled to the nozzle is a solenoid-operated gas gun and cutter assembly. A pneumatic propellant valve pulses a burst of ~60 bar helium to accelerate the cut pellet into W7-X. Three gaps in the guide tubes provide pumping locations to remove the helium propellant before it reaches the plasma. The maximum velocity of the pellet is limited by its ability to survive navigating the guide tube trajectory intact. A microwave cavity located within the guide tube provides the capability to measure the pellet size and velocity. The mechanical and thermal designs of the W7-X extruder, adjustable nozzle, and gun and cutter assembly design are described. A guide tube design and experimental pellet survivability test results are presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Data and scripts associated with a manuscript analyzing ELM-FATES parameter sensitivity under pre-fire and postfire scenarios using machine learning

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Fire Severity-Dependent Shifts in Vegetation Parameter Sensitivity: A Pre- and Post-Fire Analysis Using ELM-FATES and Explainable AI” submitted to Journal of Advances in Modeling Earth Systems (Zahura et al. 2026). The study examines vegetation physiological parameters controlling pre-fire and post-fire vegetation dynamics. To support this analysis, 73 vegetation parameters in Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (Fisher et al., 2018) , which is coupled with E3SM (Energy Exascale Earth System Model) land model (ELM, ELM-FATES), were perturbed using a Sobol sequence to generate 1,024 ensemble members for two plant functional types: needleleaf evergreen extratropical trees (NEET) and C3 grass. Simulations were conducted for the pre-fire period (2016) and post-fire period (2018–2023). Burn severity was represented by modifying the Nesterov index in FATES to 75,000, 150,000, and 300,000 for low, moderate, and high severity, respectively. A no-fire scenario was also included. Simulations were performed for 16 grid cells in the American River Watershed across different burn severities and plant functional types. XGBoost (eXtreme Gradient Boosting) models were trained using the parameter ensembles and ELM-FATES-simulated outputs, including leaf area index (LAI), gross primary productivity (GPP), aboveground biomass, vegetation evaporation, transpiration, and soil evaporation. Models were trained separately for each year and burn severity, followed by SHAP (SHapley Additive exPlanations) analysis to identify changes in dominant parameters after fire disturbance. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package contains the ELM-FATES simulation data. The scripts and data related to the analysis will be added later. The inputs and outputs from ELM-FATES are inside the “FATES” folder. “FATES_domain_surface” contains the domain and surface netcdfs that were used to run ELM-FATES in the study area. “FATES_parameters” contains the 1024 ensembles that were generated using Sobol sequence. “FATES_outputs” folder contains ELM-FATES simulated variables. All files are .csv and .nc (NetCDF).

Aboveground biomass↗
DOE OSTI2025

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

The optimization of functional materials is important to enhance their properties, but their complex geometries pose great challenges to optimization. Data-driven algorithms efficiently navigate such complex design spaces by learning relationships between material structures and performance metrics to discover high-performance functional materials. Surrogate-based active learning, continually improving its surrogate model by iteratively including high-quality data points, has emerged as a cost-effective data-driven approach. Furthermore, it can be coupled with quantum computing to enhance optimization processes, especially when paired with a special form of surrogate model (i.e., quadratic unconstrained binary optimization), formulated by factorization machine (FM). However, current practices often overlook the variability in design space sizes when determining the initial data size for optimization. In this work, we investigate the optimal initial data sizes required for efficient convergence across various design space sizes. By employing averaged piecewise linear regression, we identify initiation points where convergence begins, highlighting the crucial role of employing adequate initial data in achieving efficient optimization. These results contribute to the efficient optimization of functional materials by ensuring faster convergence and reducing computational costs in FM-based active learning.

active learning↗
DOE OSTI2024

CatTestHub: A benchmarking database of experimental heterogeneous catalysis for evaluating advanced materials

The ability to quantitatively compare newly evolving catalytic materials and technologies is hindered by the widespread availability of catalytic data collected in a consistent manner. While certain catalytic chemistries have been widely studied across decades of scientific research, quantitative comparisons based on literature information is hindered by variability in reaction conditions, types of reported data, and reporting procedures. Here, we present CatTestHub, an open-access database dedicated to benchmarking experimental heterogeneous catalysis data. Combining systematically reported catalytic activity data for selected probe chemistries, with relevant material characterization and reactor configuration information, the database provides a collection of catalytic benchmarks for distinct classes of active site functionality. Through key choices in data access, availability, and traceability, CatTestHub seeks to balance the fundamental information needs of chemical catalysis and the FAIR data design principles. Details of the database architecture and the means through which to navigate it are presented, highlighting examples of catalytic insights readily drawn from the available benchmarking data. In its current iteration, CatTestHub spans over 250 unique experimental data points, collected over 24 solid catalysts, that facilitated the turnover of 3 distinct catalytic chemistries. Here, a roadmap is presented through which to expand the open-access platform that serves as a community wide benchmark, primarily through continuous addition of kinetic information on select catalytic systems by members of the heterogeneous catalysis community at large.

Benchmark↗
DOE OSTI2024

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis—nonadditive interactions between amino acid substitutions—and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences—a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

biocatalysis↗
DOE OSTI2022

Temporal Study 2021-2022: Sample-Based Surface Water Chemistry and Organic Matter Characterization across Watersheds in the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of temporal variability in sediment respiration rates in the Yakima River Basin. The dataset provides geochemistry and organic matter characterization data generated from samples collected at weekly or bi-weekly intervals at six sites across the Yakima River Basin in Washington, USA. Related sensor data will be published separately and can be used to link sediment respiration rates to biogeochemical processing rates. The data package was originally published in November 2022. It was updated in November 2023 (v2; modified files) and April 2025 (new and modified files). See the change history section in readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) dissolved inorganic carbon (DIC), dissolved organic carbon (DOC; reported as non-purgeable organic carbon; NPOC), total nitrogen (TN), total suspended solids (TSS), and ions; (5) averaged values from water chemistry data; (6) surface water sampling protocol; (7) sensor protocol; (8) readme; (9) methods codes; (10) international generic sample number (IGSN) mapping file; and (11) folder of high resolution characterization of organic via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). This folder contains two subfolders, one containing the .xml data files and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .R, .ref, or .xml. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected our data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The Amazon Dense GNSS Meteorological Network: A New Approach for Examining Water Vapor and Deep Convection Interactions in the Tropics

The Amazon Dense Global Navigational Satellite System (GNSS) Meteorological Network ((ADGMN) provides high spatiotemporal resolution, all-weather precipitable water vapor for studying the evolution of continental tropical and sea-breeze convective regimes of Amazonia. The ADGMN campaign consisted of two experiments: a 6-week campaign in and around Belem, which coincided with the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) and a 1-yr campaign in and around Manaus. The Belem network was composed of 15 GNSS/meteorological stations that provided high-frequency (5 min) PWV data as well as surface meteorological variables For the 6-week duration of the Belem experiment, days were categorized as convective (22 days) or nonconvective (19 days) based solely on a minimum cloud-top temperature of 240 K or below over the central portion of the network and a report of precipitation at at least one site during the afternoon or evening. Here, the Manaus network commenced in April 2011 with 12 GNSS meteorological stations. Local circulations in Manaus driven by anthropogenic deforestation have, in particular, received attention.

58 GEOSCIENCES↗
DOE OSTI2021

The Global Fingerprint of Modern Ice-Mass Loss on 3-D Crustal Motion

Crustal motion generated by rapid ice-mass loss from Earth's glaciers and ice sheets has previously been considered in Global Navigational Satellite System (GNSS) analyses and numerical models across regions of ice retreat. However, the fingerprint of ice-mass loss is not limited to glaciated areas, but is characterized by a global pattern of 3-D crustal deformation. We compute “far-field” vertical and horizontal deformation rates that occurred in response to early 21st century mass flux from the Greenland and Antarctic Ice Sheets, global glaciers and ice caps, and associated ocean loading. We demonstrate that mass changes in the Greenland Ice Sheet and high latitude glacier systems each generated average crustal motion of 0.1–0.4 mm/yr across much of the Northern Hemisphere, with significant year-to-year variability in magnitude and direction. Horizontal motions associated with ice-mass loss exceed vertical rates in many far-field areas, and both should be considered in future analysis of GNSS measurements.

58 GEOSCIENCES↗
DOE OSTI2026

Accelerating catalytic advancements through the precision of high-throughput experiments & calculations

The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Longitudinal Patterns in Nitrate Revealed Through Drone‐Based Measurements in an Agriculturally Influenced Midwestern U.S. River

Nitrate concentrations in streams and rivers in the Midwestern United States are often elevated, reflecting the predominance of agriculture in the surrounding landscape. Recent advances in technology, including surface water drones and more precise sensors, provide opportunities to investigate nitrate dynamics with high spatial and temporal resolution. We deployed an aquatic drone, the AquaBOT, in a sixth-order, agriculturally influenced river to examine longitudinal patterns in water quality. Our goal was to measure the spatial and temporal heterogeneity in nitrate and nitrate removal processes and determine the influence of tributary inputs on main stem chemistry. We navigated the drone along a 12-km reach of the Des Moines River (Iowa, USA) nine times between June 2021 and August 2022. Across the deployments, mean nitrate concentration was positively related to discharge and was nearly two orders of magnitude higher in spring than summer. We observed contrasting patterns in main stem nitrate, which decreased downstream during some runs (e.g., 3.1–2.7 mg N L −1 in June 2021), demonstrating net nitrate uptake along the reach, and remained constant on other dates. Similarly, tributaries to the Des Moines had a varied influence on riverine nitrate. Tributaries either increased or decreased main stem nitrate concentrations depending on the tributary and the date. Nitrate removal rates were spatially and temporally variable but showed some consistency at the subreach (2 km) scale, with two subreaches often showing elevated rates of nitrate removal across dates. In conclusion, our study reveals nuanced heterogeneity in nitrate dynamics of the Des Moines River despite the homogeneity of agricultural land cover in the watershed.

54 ENVIRONMENTAL SCIENCES↗