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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Analysis of measurements from an array of radioxenon samplers near to Hartlepool Nuclear Power Station

As part of a scientific research and development project, the radionuclide fingerprint of an operating advanced gas-cooled reactor 25 (AGR) has been studied across several facets (Goodwin et al., 2024). One part of this project was to deploy an array of 26 radioxenon samplers to the region for a period of around 1 year, to measure any radioxenon emissions from the reactors of the 27 Hartlepool nuclear power station at a range of tens of kilometres away. The array of 3 sensors was operational for around 12 28 months from March 2022 and detected many occurrences of isotopes of radioxenon. Here we provide a detailed analysis and 29 interpretation of the data and where possible, attribution of detections to a source or region. Whilst a key part of this work is to 30 measure any emissions of radioxenon from Hartlepool, this work presents one of the most comprehensive efforts to determine the 31 source of a great number of (mostly) 133Xe detections. A combination of different types of atmospheric dispersion modelling 32 techniques, including the use of stack monitoring data from nearby civil radioxenon-emitting nuclear facilities has enabled the 33 majority of detections to be attributed to one or more possible sources. Whilst emissions from Hartlepool have been detected on 34 the systems, the majority of detections are associated with a medical isotope production facility in Fleurus, Belgium (IRE).

Advanced gas-cooled reactor↗
DOE OSTI2025

Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM

Reservoirs play a vital role in regulating streamflow timing and variability for hydroelectricity, flood control, water supply, irrigation, and recreation. Despite their importance, many reservoirs lack comprehensive operational guidelines, making their management complex due to conflicting operational objectives. Hence traditional policy-based reservoir models often fail to capture real-world conditions accurately and they depend on perfect streamflow predictions, which are not always available. In contrast, data-driven models like Long Short-Term Memory (LSTM) networks offer a robust alternative. This study introduces an approach that integrates reservoir characteristics—such as main use, climate, and maximum capacity—into the LSTM model to enhance reservoir release predictions. Using data from nearly 200 reservoirs in the contiguous United States (CONUS), our conditioned LSTM model (LSTM_cond) was compared with both the vanila LSTM and a traditional policy-based approach. Furthermore, our results show that while both LSTM_cond and LSTM perfoms better than the policy-based approach, LSTM_cond consistently outperforms LSTM for hydroelectric, water supply, irrigation, and recreation reservoirs. The KGE median values for LSTM_cond for out-sample reservoirs are 0.764, 0.565, 0.821, and 0.779, respectively, for the aforementioned reservoir types, which are consistently higher that the corresponding KGE values of 0.737, 0.413, 0.775, and 0.713 of LSTM, demonstrating its advantages in improving generalizability.

CONUS↗
DOE OSTI2025

Effect of Mg and Ni impurities on tritium diffusion in lithium ceramics through cluster dynamics simulations

This study investigates the impact of Mg and Ni doping on tritium diffusion in LiAlO 2 and LiAl 5 O 8 ceramics, that are used in tritium-producing burnable absorber rods (TPBARs). Utilizing Centipede simulations across a broad temperature range (500 K to 1250 K), we explore the interplay between defect dynamics, cluster formation, and tritium mobility. In LiAlO 2 , Mg doping significantly enhances tritium diffusivity by increasing tritium interstitial concentrations and diffusion coefficients of key species, thereby doubling the overall tritium diffusivity. Ni doping, while shifting the dominant defect to Li vacancies, maintains high tritium mobility due to the low binding energy of Li vacancy-tritium complexes, which ensures effective tritium migration. In LiAl 5 O 8 , Mg and Ni doping results in a slight reduction in the diffusion coefficients of key species, yet the dramatic increase in tritium interstitial concentrations compensates, leading to a net small increase in tritium diffusivity. In conclusion, the findings highlight the critical role of defects in tritium transport and the effect of Mg and Ni defects on the performance of these ceramics in demanding nuclear environments.

Cluster dynamics↗
DOE OSTI2026

A robust alloy design (RAD) strategy for next-generation (IV) nuclear fission reactors

Next-generation nuclear reactors demand structural materials capable of withstanding extreme conditions, including high temperatures, intense neutron flux, and corrosive environments. Multi-Principal Element Alloys (MPEAs) have emerged as promising candidates due to their exceptional radiation tolerance, thermal stability, and compositional flexibility. This study introduces a versatile and customizable Robust Alloy Design (RAD) strategy for systematically designing MPEAs for GEN-IV reactor fuel cladding. The RAD framework integrates nuclear-relevant selection criteria, empirical parameter assessments, and high-throughput CALPHAD simulations to efficiently narrow compositional space and identify stable alloys. A unified RAD score developed for the first time, combines key performance metrics, including fuel-clad chemical interaction (FCCI), neutron absorption cross-section (NAC), valence electron configuration (VEC), and melting point factor (MPF), into a flexible ranking system adaptable to reactor-specific priorities. Among 724 candidates, V555(5Al–5Cr–5Fe–85V) emerged as the top alloy, validated experimentally with a homogeneous single-phase BCC microstructure and superior mechanical properties (nano-indentation: 3.389 ± 0.258 GPa; Vickers hardness: 240 ± 6.7 HV), significantly outperforming Zircaloy-4 and V-4Cr-4Ti. Importantly, the RAD strategy is not limited to nuclear applications; its customizable weighting system enables scalability to other extreme environments. This adaptability positions RAD strategy as a versatile tool for advanced materials design across multiple industries.

Alloy design↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Dense autoencoders, clustering techniques, and semi-supervised learning for HPGe $γ$-spectra

Classifying high-resolution gamma spectra by their isotopic content is an essential task in nuclear forensics and other applications. Traditional analysis methods are often time-intensive, but machine learning (ML) may help analysts quickly process many spectra. Such methods tend to rely on abundant, well-labeled data for training. Historical gamma data exists in various fields but is not uniformly useful for supervised ML due to inconsistent labeling. Here, to address some of these challenges, we present a method to classify and organize unlabeled data from high-purity germanium detectors using an autoencoding neural network (autoencoder). We trained dense autoencoders to compress gamma data into latent representations that enable efficient data characterization. By clustering the encoded spectra or lower-dimensional mappings of them, we identified and removed portions of over-abundant data categories, resulting in a more balanced dataset and improved autoencoder performance. This encoding and clustering pipeline also enabled the organization of spectra into self-consistent categories. Finally, we found that encoded representations showed potential as inputs for semi-supervised learning of nuclide identification (NID) labels, achieving an average F1 score of 0.85 ± 0.03 when mapping encodings to a set of 65 isotope labels.

Autoencoders↗
DOE OSTI2025

Functional group activation and coupling agent migration induced by plasma treatment in adhesive for enhanced toughness of metal-composite joints

Commercial adhesive paste was additionally activated using low-power oxygen plasma with different plasma exposure durations to investigate its interfacial bonding contribution in adhesively-bonded Aural-5/CFRP-PA6 double cantilever beam (DCB) joints with plasma-treated adherends. The plasma-treated adhesive showed enhanced functional peak intensities in Fourier transfer infrared spectroscopy (FTIR) as plasma exposure time was increased, and some degree of oxidation was also detected via X-ray photoelectron spectroscopy (XPS). A small molecule, dicyandiamde (coupling agent) in adhesive was migrated to adhesive layer, where double concentration of coupling agent was detected after 10 minutes of plasma treatment. As a result, characterization of the adhesive/CFRP-PA6 interface after DCB fracture showed additional chemical bonds formed through amine-initiated epoxy ring opening polymerization, as well as amide bonds and ester bonds. In conclusion, these additional bonds at the adhesive/CFRP-PA6 interfaces led to significantly increased failure extensions and fracture energies of the joints as the plasma exposure time on the adhesive increased, compared to the joints with plasma-treated adherends but non-plasma-treated adhesive as commonly seen in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Control co-design under uncertainty for offshore wind farms: Optimizing grid integration, energy storage, and market participation

Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. Here, this paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2% and provide flexibility in managing wind resource uncertainties.

Control Co-design↗
DOE OSTI2026

Evaluating the effect of meso/submesoscale current–wave interactions on wave energy resource characterization at northeast U.S. coast

Wave energy is a promising renewable resource, but accurate assessment is difficult in regions with strong currents due to wave–current interactions (WCI). Here, this study develops a two-way coupled WCI model within the Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) framework at 2 km resolution to improve wave energy characterization along the northeastern U.S. coast, including the Mid-Atlantic Bight and Gulf of Maine. The model integrates WaveWatchIII (WWIII) and the Regional Ocean Modeling System (ROMS) to enhance wave hindcasting by accounting for Doppler-shift, refraction, and nonlinear energy exchanges. Validation against buoy and satellite observations confirms model accuracy. Analysis shows that Doppler-shifting can alter wave power density by over 20%, while strong current gradients and shear distort wave crests via focusing/defocusing and stretching/squeezing, modifying wave direction and frequency. These processes together can induce wave power fluctuations of up to 40% on synoptic scales. Applying a 2.5 MW Ocean Energy Converter power matrix shows that WCI may change harvested energy by up to 100% in shallow-waters and 60% in deep-waters. These results underscore the importance of incorporating nonlinear WCI for reliable wave climate predictions and resource assessments in energetic coastal regions.

doppler-shift↗
DOE OSTI2025

Autonomous sensor suite for evaluating fish-turbine interactions and environmental impacts in marine renewable energy and hydropower

Marine renewable energy (MRE) harnesses ocean-based resources such as waves, tides, currents, and thermal or salinity gradients for sustainable power generation. It has the potential to complement existing renewable resources, support remote communities, and contribute to decarbonization efforts. However, understanding the hydrodynamic forces created by MRE devices and their impacts on marine life is critical for responsible deployment. Here, to address these concerns, advanced sensor devices, including the Marine Sensor Fish (MSF), Sensor Fish Mini (SF Mini), and Flexible Sensor Fish (FSF), were developed to measure interactions between aquatic organisms and MRE systems. This paper details the design, manufacturing, calibration, and field deployment of these sensor suites, highlighting their ability to capture key physical stressors such as shear forces, pressure changes, and collision impacts. The MSF successfully evaluated turbine interactions at a tidal turbine in the Salish Sea, capturing data on turbulence, collision impact, and pressure gradients. The SF Mini validated hydrodynamic conditions in scaled hydraulic models, supporting computational fluid dynamics simulations. The FSF, with its flexible silicone body, measured species-specific impacts in turbulent environments. This research demonstrates the potential of Sensor Fish technology to advance sustainable marine energy systems by reducing biological impacts and informing environmentally sustainable designs.

Ecological impacts↗
DOE OSTI2025

Biogeochemical controls on iron speciation and cycling across upland to shoreline gradients in freshwater and estuarine coastal soils (Lake Erie and Chesapeake Bay, United States)

Coastal environments are dynamic interfaces that mediate carbon and nutrient exchanges between terrestrial landscapes and open waters, and understanding the biogeochemical factors controlling these exchanges, particularly iron (Fe) redox transformations, is crucial for predicting coastal ecosystem functions. Here, we investigated the mechanisms controlling Fe speciation changes across upland-to-shoreline gradients in freshwater and estuarine soils using Fe K-edge X-ray absorption spectroscopy, solid and porewater composition analysis, and 16S rRNA sequencing analysis. We show that Fe transformations depend primarily on inundation patterns. In unsaturated uplands, Fe occurs as Fe(III) oxyhydroxides, mainly goethite (9–35 %), Fe(II,III)-phyllosilicates (39–89 %), and Fe(III)-organic species (0–61 %). Soils influenced by estuarine waters exhibit porewater sulfide concentrations reaching up to 221 μM, Fe- and S-cycling bacteria, and up to 81 % pyrite (FeS 2 ), indicating that sulfur-driven redox dynamics control Fe transformations. In lacustrine wetlands, Fe(III) reduction is indicated by porewater Fe(II) concentrations increasing to 1.0–2.1 mM, and ~10–15 % of Fe as Fe(II,III)-(hydr)oxides (green rust), vivianite (Fe 3 (PO 4 ) 2 ·8H 2 O), and/or adsorbed Fe(II) species. EXAFS data also indicate reduction of structural Fe(III) to Fe(II) in phyllosilicates. The presence of Fe- and S-cycling bacteria, as well as sulfide (0–10 μM), suggests that Fe-cycling is microbially driven and potentially coupled with cryptic S-cycling. Fe(II) oxidation was indicated above/near the water table by the presence of Fe(III) oxyhydroxides (ferrihydrite, lepidocrocite). Furthermore, negligible Fe(III) or sulfate reduction was observed at some water-saturated sites located at the upland-wetland transition, likely due to oxic (sub-)surface water inputs. Overall, our results highlight the importance of considering both Fe-speciation and hydro-biogeochemical dynamics when predicting Fe-cycling at coastal interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Challenges in integrating dissolved organic matter chemodiversity into kinetic models of soil respiration

The chemodiversity of dissolved organic matter (DOM) in soil has been proposed to influence the microbial metabolism and fate of belowground organic carbon (C). However, integrating DOM chemistry into soil C cycle models to improve predictions of C stocks and fluxes—beyond simply considering DOM pool size—remains a challenge. While recent research suggests that incorporating DOM chemodiversity into models can improve predictions of microbial respiration, there is still a lack of mechanistic understanding describing how DOM chemodiversity affects microbial metabolism and soil respiration. Here, we evaluated whether DOM chemodiversity was a determinant of soil respiration using paired measurements of high-resolution DOM chemistry, obtained from Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), and potential soil respiration rates from across the United States (U.S.), all data provided by the Molecular Observation Network. Our objectives were to (1) assess statistical relationships between DOM chemodiversity and microbial respiration, and (2) evaluate the ability of kinetic models to leverage DOM chemistry to explain empirical relationships found in statistical models. Statistical regressions revealed that DOM chemodiversity (alpha diversity) was nonlinearly related to potential soil respiration rates, both independently and through its interactions with DOM and total C concentrations. In soils with relatively high DOM but low total C concentrations, potential soil respiration rates were negatively correlated with DOM alpha diversity, whereas in soils with relatively low DOM and high total C concentrations showed the opposite trend. However, when metabolic transition theory kinetic models were modified to include chemodiversity, their performance was comparable to traditional Monod kinetics approaches, which simulate respiration rates as a function of DOM concentration. The inability to account for nonlinearities in DOM chemodiversity–respiration relationships highlight an opportunity to advance substrate uptake kinetics by establishing causal links between DOM chemodiversity, microbial metabolism trade-offs, and potential interactions under varied environmental conditions.

Bioenergetic model↗
DOE OSTI2025

Lipids represent a dynamic, yet stable pool of microbially-derived soil carbon

There is an emerging consensus that microorganisms are a primary source of persistent, slow-cycling soil organic matter (SOM), however which microbial residues contribute to SOM and what controls their accumulation remains unresolved. Lipids are a commonly overlooked biomolecular pool that could contribute significantly to stable SOM. While current estimates for phospholipid degradation in soils are rapid, lipids are structurally heterogeneous molecules that could turnover at different rates. Through a year-long soil incubation and compound-specific, stable isotope probing (SIP)-lipidomics, we were able to rigorously track the persistence of lipid compounds in silty and sandy switchgrass bioenergy crop soils. Here, we assessed the influence of lipid structure on soil lipid accrual and degradation as moderated by soil texture, since mineral association is presumed to be the primary mechanism for lipid persistence. After rapid incorporation of 13 C glucose into microbial biomass, we found 13 C label was retained broadly across chemically diverse lipid classes, even after one year. 13 C-labeled lipid profiles varied significantly with soil texture; however, we found no difference between sandy and silty soils in lipid retention, suggesting soil texture may only play a minor role in modulating lipid persistence. Only two lipid subclasses were found to be persistent (i.e., retention of 13 C label without significant degradation or production): phosphatidylinositol lipids and hydroxyceramide lipids, both of which are negatively charged, possibly facilitating stabilization by mineral complexation. However, several other subclasses displayed substantial ongoing production. In particular, the accumulation of triacylglycerol lipids across soil textures suggests that storage lipids may be an important component of SOC, highlighting a potential target for management strategies to promote C retention by lipid accrual. Overall, the retention for over a year of 13 C label in microbial intact lipid biomarkers reveals the importance of efficient biomass production and turnover when considering microbial C contributions to SOM.

compound-specific↗
DOE OSTI2025

Effects of non-monochromaticity in laboratory XPS: Representative example of pyrimidine

The Mg and Al K α radiation used in standard laboratory sources for X-Ray Photoelectron Spectroscopy, XPS, is not naturally monochromatic because the spin orbit splitting of the Mg and Al 2p shells is not normally resolved. However, since the 2p spin-orbit splitting of the K α1 and K α2 X-Rays in these light atoms is small, it is normally ignored. In the present work, the consequences of the departure from monochromaticity is explicitly shown to be extremely small for the representative case of the C(1 s) ionizations in the XPS of the pyrimidine molecule. Furthermore, this conclusion is general and does not depend on the particular molecule studied since the K α1 and K α2 BE splittings reflect the spin-orbit splittings in the X-Ray source.

Binding Energies (BEs)↗
DOE OSTI2025

Electrocatalytic oxidation of hydrothermal liquefaction-derived aqueous phase for on-site wastewater treatment and H 2 production

Electrocatalytic oxidation (ECO) is a promising method for generating molecular hydrogen (H 2 ) while simultaneously treating the aqueous phase (AP) from hydrothermal liquefaction (HTL) of biomass-derived feedstocks such as algae, food waste, sludge, and wood. This study highlights the impact of HTL-AP composition on ECO performance, mainly activity, stability, and efficiency in a batch and flow electrolyzer. We demonstrated current efficiency (CE) for chemical oxygen demand (COD) removal ranges from 14% to 85% in the flow electrolyzer with electrode stability increasing from 20 to ≥2,800 h. Decreasing the applied potential enhances the CE. High ammonium content can accelerate deactivation, yet chloride ions appear to aid oxidation of organic compounds and enhanced anode stability. A preliminary energy and H 2 balance for an HTL sewage sludge plant shows that the HTL-AP contains sufficient COD to produce all the H 2 needs for bio-oil hydrotreating, with a 26% surplus available for other uses.

Electrolysis↗
DOE OSTI2026

Quantitative 14 N NMR with Monte Carlo Uncertainty Analysis of Nitrate/Nitrite in Alkaline Nuclear Waste

While monitoring of nitrate and nitrite concentrations is important for managing corrosion in nuclear waste systems, existing analytical methods are hindered by turbidity, spectral interference, and delays from sample handling. Here, we demonstrate quantitative 14 N nuclear magnetic resonance (qNMR) spectroscopy as a direct, matrix-tolerant approach for nitrate and nitrite detection at natural abundance. Monte Carlo resampling was integrated into the workflow to quantify random error, establish precision–time tradeoffs, and separate noise-limited uncertainty from systematic bias arising from shimming, transmitter offset, or excitation pulse conditions. Quantification of nitrate and nitrite were validated in controlled alkaline matrix challenges and in 18-component Hanford-type simulants. These results establish 14 N qNMR as a practical, uncertainty-bounded tool for monitoring redox-active nitrogen species in chemically complex environments and provide a generalizable framework for quantitative analysis of quadrupolar nuclei.

Graham, Trent R. [Pacific Northwest National Labor↗
DOE OSTI2025

Effect of Impurities on the Redox Properties of Goethite

Iron oxide minerals regulate the flux of electrons in the environment and are important hosts for trace and minor, yet critical, elements. Here, we present the first evidence of a direct link between the local coordination environments of Ni and Zn and the redox properties of their host phase goethite (α-FeOOH), the most abundant Fe(III) (oxyhydr)oxide at Earth’s surface. Here, we used aqueous redox measurements to show that the redox potential E H 0 , and hence the mineral’s stability, follows the order: pure goethite ≥ Zn-goethite > Ni-goethite. Parallel X-ray absorption and scattering measurements demonstrate, using quantum-informed analysis, that the local coordination environment of the smaller impurity, Ni, causes more bulk strain energy than Zn, which nearly accounts for the difference in E H 0 between Ni- and Zn-goethite. Our theory-informed, experimental study reveals how two common impurities affect the stability of goethite with implications for the biogeochemical reactivity of Fe(III) (oxyhydr)oxide in mediating elemental and electron fluxes in the environment.

42 ENGINEERING↗
DOE OSTI2026

Surface Crust Formation Controls Evaporation Kinetics of Secondary Organic Aerosols

Gas-particle partitioning is critical for the evolution of secondary organic aerosols (SOA) in the atmosphere. SOA particles evaporate more slowly than expected at nearly size-independent rates, but the underlying mechanism remains controversial. Here, in this study, we apply kinetic multilayer modeling to simulate evaporation of α-pinene SOA, demonstrating that surface crust formation, emerging from accumulation of low-volatility compounds at the particle surface, leads to slow evaporation and reduced size dependence of the evaporation rate. While evaporation induced by decomposition of oligomers would naturally lead to size-independent evaporation rates, we observe and simulate nearly size-independent slow evaporation of polyethylene glycol mixture particles containing polymeric species that do not decompose, confirming the relevance of composition-dependent diffusivity for size-independent, slow evaporation. Slow evaporation of limonene SOA was also observed in environmental chamber experiments, and model simulations demonstrate strong surface crust formation with bulk diffusivity being depressed by up to 5 orders of magnitude compared to the inner bulk. We present experimental evidence using a surface-based mass spectrometry technique that shows that the particle surface becomes enriched in high molecular weight compounds upon evaporation of monomers. Our findings imply that viscous surface crusts may also limit the growth and chemical transformation of SOA particles, influencing their impacts on air quality and climate.

heterogeneous chemistry↗