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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Using Best Basis Inventory Data to Direct Strategies for Real Time Monitoring of Hanford High Level Waste – 26226

The potential to accelerate the processing of low- and high-level tank waste by applying real-time monitoring (RTM) of chemical and physical properties has prompted research into the suitability of multiple analytical methods for that purpose. The broad variety of waste stream properties and the large number of analytes of interest (as evidenced by Waste Acceptance Criteria (WAC) and Process Control Limit (PCL) lists) lead to an overwhelming set of possible analytical scenarios. This report describes the use of Best Basis Inventory (BBI) data to find the most relevant analytical targets for the specific case of monitoring the blending of High Level Waste from multiple tanks prior to introduction into a vitrification facility. Campaigns for blending this waste to minimize the risk of exceeding WACs and PCLs have been proposed. However, the predicted compositions of the blended materials do not incorporate any uncertainties that may be associated with the representativeness of the waste layer samples or the laboratory analyses that generated the BBI data. Also, they do not include any uncertainty associated with the precision of collecting highly specific fractions of the layers during a blending campaign or any inhomogeneities that may exist in those layers. Monte Carlo methods are used to apply uncertainties to the compositions of the individual layers specified in the campaign recipes. The resulting variations in the compositions of the blended materials allow estimation of the risks of exceeding WACs and PCLs for each campaign. A critical subset of WACs/PCLs – NOx, NaK, AlFeZr, and S – are especially at risk of being exceeded in multiple campaigns. These analytes should be the focus of instrument development. We also have extracted the expected solid/supernate distribution for these analytes, which establishes important performance criteria for individual analytical methods. The BBI data also permits an understanding of the different chemical forms in which the analytes appear. Thus, the need to establish instrumental sensitivity to these forms can be gainfully addressed. Although concentrating on one specific application – the blending of tank waste - this approach should be generalizable for the analysis of other possible RTM applications for waste processing.

Lascola, Robert [Savannah River National Laborator↗
DOE OSTI2016

Measuring the seeds of ion outflow: auroral sounding rocket observations of low-altitude ion heating and circulation

Here, we present an analysis of in situ measurements from the MICA (Magnetosphere-Ionosphere Coupling in the Alfvén Resonator) nightside auroral sounding rocket with comparisons to a multifluid ionospheric model. MICA made observations at altitudes below 325 km of the thermal ion kinetic particle distributions that are the origins of ion outflow. Late flight, in the vicinity of an auroral arc, we observe frictional processes controlling the ion temperature. Upflow of these cold ions is attributed to either the ambipolar field resulting from the heated electrons or possibly to ion-neutral collisions. We measure E → xB → convection away from the arc (poleward) and downflows of hundreds of m s -1 poleward of this arc, indicating small-scale low-altitude plasma circulation. In the early flight we observe DC electromagnetic Poynting flux and associated ELF wave activity influencing the thermal ion temperature in regions of Alfvénic aurora. We observe enhanced, anisotropic ion temperatures which we conjecture are caused by transverse heating by wave-particle interactions (WPI) even at these low altitudes. Throughout this region we observe several hundred m s -1 upflow of the bulk thermal ions colocated with WPI; however, the mirror force is negligible at these low energies; thus, the upflow is attributed to ambipolar fields (or possibly neutral upwelling drivers). Moreover, the low-altitude MICA observations serve to inform future ionospheric modeling and simulations of (a) the need to consider the effects of heating by WPI at altitudes lower than previously considered viable and (b) the occurrence of structured and localized upflows/downflows below where higher-altitude heating rocesses are expected.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Modeled Productivity in Permafrost Regions

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m -2 yr -1 ), most models produced higher NPP (309 ± 12 g C m -2 yr -1 ) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m -2 yr -1 ), which mainly resulted from differences in simulated maximum monthly GPP (GPP max ). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vc max_25 ), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO 2 concentration. In conclusion, these results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPP max as well as their sensitivity to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Sources of methane to an Arctic lake in Alaska: An isotopic investigation

Sources of dissolved methane (CH 4 ) at Toolik Lake, Alaska, include both diffusion from lake sediments and groundwater entering the lake from its perimeter. Here we use hydrogen and oxygen isotopes in water (H 2 O), carbon and hydrogen isotopes in CH 4 , and carbon isotopes in dissolved inorganic carbon (DIC) to calculate the relative importance of lake sediment and groundwater discharge as sources of dissolved CH 4 to Toolik Lake. We also resolve the relative importance of the source contribution spatially within the lake and determine the processes controlling CH 4 concentrations in groundwater surrounding the lake. Our findings, from a mixing model based on isotopes in CH 4 , suggest that groundwater is a more important source of CH 4 at the perimeter of the lake where the water-to-air flux is high. In addition, we find on the local scale that high groundwater methane concentrations may be better linked to areas around the lake where rain is the dominant source of water to the active layer, indicating that changes in precipitation and active layer thaw depth will impact methane concentrations in the active layer and, ultimately, the groundwater associated flux to Toolik Lake.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Relativistic electron dynamics produced by azimuthally localized poloidal mode ULF waves: Boomerang-shaped pitch angle evolutions

Here, we present an analysis of “boomerang-shaped” pitch angle evolutions of outer radiation belt relativistic electrons observed by the Van Allen Probes after the passage of an interplanetary shock on 7 June 2014. The flux at different pitch angles is modulated by Pc5 waves, with equatorially mirroring electrons reaching the satellite first. For 90° pitch angle electrons, the phase change of the flux modulations across energy exceeds 180° and increasingly tilts with time. Using estimates of the arrival time of particles of different pitch angles at the spacecraft location, a scenario is investigated in which shock-induced ULF waves interact with electrons through the drift resonance mechanism in a localized region westward of the spacecraft. Numerical calculations on particle energy gain with the modified ULF wavefield reproduce the observed boomerang stripes and modulations in the electron energy spectrogram. The study of boomerang stripes and their relationship to drift resonance taking place at a location different from the observation point adds new understanding of the processes controlling the dynamics of the outer radiation belt.

58 GEOSCIENCES↗
DOE OSTI2025

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System

Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg 32 (Zn,Al) 49 . It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗
DOE OSTI2017

Impact of media and antifoam selection on monoclonal antibody production and quality using a high throughput micro-bioreactor system

Monoclonal antibody production in commercial scale cell culture bioprocessing requires a thorough understanding of the engineering process and components used throughout manufacturing. It is important to identify high impact components early on during the lifecycle of a biotechnology-derived product. While cell culture media selection is of obvious importance to the health and productivity of mammalian bioreactor operations, other components such as antifoam selection can also play an important role in bioreactor cell culture. Silicone polymer-based antifoams were known to have negative impacts on cell health, production, and downstream filtration and purification operations. High throughput screening in micro-scale bioreactors provides an efficient strategy to identify initial operating parameters. Here, we utilized a micro-scale parallel bioreactor system to study an IgG1 producing CHO cell line, to screen Dynamis, ProCHO5, PowerCHO2, EX-Cell Advanced, and OptiCHO media, and 204, C, EX-Cell, SE-15, and Y-30 antifoams and their impacts on IgG1 production, cell growth, aggregation, and process control. This study found ProCHO5, EX-Cell Advanced, and PowerCHO2 media supported strong cellular growth profiles, with an IVCD of 25-35 3 106 cells-d/ mL, while maintaining specific antibody production (Qp> 2 pg/cell-d) for our model cell line and a monomer percentage above 94%. Antifoams C, EX-Cell, and SE-15 were capable of providing adequate control of foaming while antifoam 204 and Y-30 noticeably stunted cellular growth. This work highlights the utility of high throughput micro bioreactors and the importance of identifying both positive and negative impacts of media and antifoam selection on a model IgG1 producing CHO cell line.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Modeling electron beam irradiation of methane

We built a chemical kinetic model for the electron beam irradiation of methane and report simulation results with dose rates ranging from 2 to 200 kGy s -1 at 298 K and 1 atm pressure. The model contains 92 species and 778 gas-phase reactions. We report the time evolution of concentrations of stable products (including H 2 , C 2 H 6 , C 3 H 8 , C 2 H 4 , C 3 H 6 , C 4 H 10 , C 2 H 2 , and C 3 H 4 ) as well as some radicals and ions. Here we discuss how yields of products depend on dose rate and how they change during the course of irradiation. To understand the processes controlling product formation, we quantify the extent to which various reactions produce and destroy major products and the key radicals and ions. Normalized sensitivity coefficients are computed for the production of stable products to gain further insight into factors controlling product yields. We were able to reduce the mechanism to 63 species and 331 reactions while incurring less than 5% error in the final concentrations of stable products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Context-aided analysis of community evolution in networks

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Integration of Highly Luminescent Lead Halide Perovskite Nanocrystals on Transparent Lead Halide Nanowire Waveguides through Morphological Transformation and Spontaneous Growth in Water

The integration of highly luminescent CsPbBr 3 quantum dots on nanowire waveguides has enormous potential applications in nanophotonics, optical sensing and quantum communications. On the other hand, CsPb 2 Br 5 nanowires have also attracted a lot of attention due to their unique water stability and controversial luminescent property. Here we first report the growth of CsPbBr 3 nanocrystals on CsPb 2 Br 5 nanowires by simply immersing CsPbBr 3 powder into pure water, CsPbBr 3-γ X γ (X = Cl, I) nanocrystals on CsPb 2 Br 5-γ X γ nanowires are then synthesized for tunable light sources. Systematic structure and morphology studies, including in-situ monitoring, revealed that CsPbBr 3 powder was first converted to CsPb 2 Br 5 microplatelets in water, followed by a morphological transformation from CsPb 2 Br 5 microplatelets to nanowires, which is a kinetic dissolution-recrystallization process controlled by electrolytic dissociation and supersaturation of CsPb 2 Br 5 . CsPbB r3 nanocrystals are spontaneously formed on CsPb 2 Br 5 nanowires when nanowires are collected from the aqueous solution. Raman spectroscopy, combined photoluminescence and SEM imaging confirm that the bright emission originates from CsPbBr 3-γ X γ nanocrystals while CsPb 2 Br 5-γ X γ nanowires are transparent waveguides. In conclusion, the intimate integration of nanoscale light sources with a nanowire waveguide is demonstrated through the observation of the wave guiding of light from nanocrystals and Fabry-Perot interference modes of the nanowire cavity.

42 ENGINEERING↗
DOE OSTI2017

Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

Water inrush from coal-seam floors greatly threatens mining safety in North China and is a complex process controlled by multiple factors. This study presents a mathematical assessment system for coal-floor water-inrush risk based on the variable-weight model (VWM) and unascertained measure theory (UMT). In contrast to the traditional constant-weight model (CWM), which assigns a fixed weight to each factor, the VWM varies with the factor-state value. The UMT employs the confidence principle, which is more effective in ordered partition problems than the maximum membership principle adopted in the former mathematical theory. The method is applied to the Datang Tashan Coal Mine in North China. First, eight main controlling factors are selected to construct the comprehensive evaluation index system. Subsequently, an incentive-penalty variable-weight model is built to calculate the variable weights of each factor. Then, the VWM-UMT model is established using the quantitative risk-grade divide of each factor according to the UMT. On this basis, the risk of coal-floor water inrush in Tashan Mine No. 8 is divided into five grades. For comparison, the CWM is also adopted for the risk assessment, and a differences distribution map is obtained between the two methods. Finally, the verification of water-inrush points indicates that the VWM-UMT model is powerful and more feasible and reasonable. The model has great potential and practical significance in future engineering applications.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Are oxygen limitations under recognized regulators of organic carbon turnover in upland soils?

Understanding the processes controlling organic matter (OM) stocks in upland soils, and the ability to management them, is crucial for maintaining soil fertility and carbon (C) storage as well as projecting change with time. OM inputs are balanced by the mineralization (oxidation) rate, with the difference determining whether the system is aggrading, degrading or at equilibrium with reference to its C storage. In upland soils, it is well recognized that the rate and extent of OM mineralization is affected by climatic factors (particularly temperature and rainfall) in combination with OM chemistry, mineral–organic associations, and physical protection. Here we examine evidence for the existence of persistent anaerobic microsites in upland soils and their effect on microbially mediated OM mineralization rates. Further, we corroborate long-standing assumptions that residence times of OM tend to be greater in soil domains with limited oxygen supply (aggregates or peds). Moreover, the particularly long residence times of reduced organic compounds (e.g., aliphatics) are consistent with thermodynamic constraints on their oxidation under anaerobic conditions. Incorporating (i) pore length and connectivity governing oxygen diffusion rates (and thus oxygen supply) with (ii) ‘hot spots’ of microbial OM decomposition (and thus oxygen consumption), and (iii) kinetic and thermodynamic constraints on OM metabolism under anaerobic conditions will thus improve conceptual and numerical models of C cycling in upland soils. We conclude that constraints on microbial metabolism induced by oxygen limitations act as a largely unrecognized and greatly underestimated control on overall rates of C oxidation in upland soils.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds

Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Atmo-ecometabolomics: A novel atmospheric particle chemical characterization methodology for ecological research

Aerosol particles play important roles in processes controlling the composition of the atmosphere and function of ecosystems. A better understanding of the composition of aerosol particles is beginning to be recognized as critical for ecological research to further comprehend the link between aerosols and ecosystems. While chemical characterization of aerosols has been practiced in the atmospheric science community, detailed methodology tailored to the needs of ecological research does not exist yet. In this study, we describe an efficient methodology (atmo-ecometabolomics), in step-by-step details, from the sampling to the data analyses, to characterize the chemical composition of aerosol particles, namely atmo-metabolome. This method employs mass spectrometry platforms such as liquid and gas chromatography mass spectrometries (MS) and Fourier transform ion cyclotron resonance MS (FT-ICR-MS). For methodology evaluation, we analyzed aerosol particles collected during two different seasons (spring and summer) in a low-biologicalactivity ecosystem. Additionally, to further validate our methodology, we analyzed aerosol particles collected in a more biologically active ecosystem during the pollination peaks of three different representative tree species. Our statistical results showed that our sampling and extraction methods are suitable for characterizing the atmo-ecometabolomes in these two distinct ecosystems with any of the analytical platforms. Datasets obtained from each mass spectrometry instrument showed overall significant differences of the atmo-ecometabolomes between spring and summer as well as between the three pollination peak periods. Furthermore, we have identified several metabolites that can be attributed to pollen and other plant-related aerosol particles.We additionally provide a basic guide of the potential use ecometabolomic techniques on different mass spectrometry platforms to accurately analyze the atmoecometabolomes for ecological studies. Our method represents an advanced novel approach for future studies in the impact of aerosol particle chemical compositions on ecosystem structure and function and biogeochemistry.

Rivas-Ubach, Albert (ORCID:0000000312937127)↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗