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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Low-Rate Cycling Regeneration Sustains NMC811–Graphite Battery Performance through Extreme Ultrafast Charging

Urban air mobility (UAM) systems, such as electric vertical takeoff and landing (eVTOL) aircraft, require batteries capable of extreme ultrafast charging to support high-frequency operations. Here, in this work, we evaluate the electrochemical stability of NMC811–graphite full cells using a complex mixed-modal protocol designed to simulate the aggressive turnaround demands of UAM missions. We demonstrate that incorporating a periodic low-rate (0.3C) “regeneration” cycle enables cells to maintain 96% capacity retention over 900 cycles, despite repeated exposure to ultrafast pulses. To validate the efficacy of this regeneration mechanism, we performed simplified control experiments comparing continuous 10C charging against a 10C–0.3C interleaved protocol. Advanced characterization reveals that while mechanical particle fracture is pervasive at these rates, the regeneration cycles decelerate the electrochemical degradation by resolving spatially heterogeneous Ni oxidation states and relaxing lithium-ion concentration gradients. These findings provide a vital mechanistic framework for battery operation, proving that restorative charging can effectively delay and mitigate failure in demanding UAM applications.

batteries↗
DOE OSTI2021

Effects of Irradiation on Thermal Properties of Polyurethane Foam

Polyurethane foam is used as a thermal insulator, fire protective material, and impact absorber in the 9977 shipping package. The 9977 shipping package is used to transport special nuclear materials (SNM), and store SNM in the K-area complex at the Savannah River Site. The primary fire-retardant mechanism in the foam is that it produces intumescent char when exposed to high temperatures. Intumescence occurs in the direction of heat application and can allow the foam to “repair” cracks in the event of a mechanical impact, in addition to serving as a secondary thermal barrier with decreased heat conductivity. Because the foam is being used for long-term storage applications, it is vital that it retain its physical, thermal, and mechanical properties over the course of time and with the events of exposure of heat and irradiation. The goal of this project was to determine the effects of gamma irradiation on the thermal properties of polyurethane foam, both in terms of cumulative irradiation and rate of irradiation. At the dose rates and cumulative doses of irradiation measured, there was no correlation between higher cumulative irradiation and thermal stability, specific heat capacity, or chemical structure. Based on the results of the experiments, the fire protective properties in the 9977 shipping containers should be retained with the expected amount of radiation exposure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks. Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Nitrogen-doped graphene-wrapped Cu 2 S as a superior anode in sodium-ion batteries

Sodium-ion batteries (SIBs) have been attracting an ever-growing research interest, mainly ascribed to their cost-effectiveness. However, SIBs have been significantly hindered by lack of a suitable anode. Herein, an exceptional Cu 2 S-based composite anode is developed via a facile ball-milling method, in which Cu 2 S particles are wrapped by nitrogen-doped graphene sheets (Cu 2 S@NG). This Cu 2 S@NG composite anode enables extremely long cycling life, ultra-stable cyclability with high capacity, and excellent rate capability. The superior performance of the Cu 2 S@NG composite is owing to its intriguing core-shell structure and the exceptional properties of both the Cu 2 S and NG. In this study, it is found that the NG shell yields multiple merits in improving the performance of Cu 2 S: (i) mitigating the loss of active materials, (ii) constituting a stable interface, (iii) providing improved electrical conductivity and good ionic transfer, and (iv) enhancing mechanical integrity. Additionally, the vital effects of different voltage windows and a surface coating via atomic layer deposition on further enhancing performance are clarified. Significantly, the electrochemical mechanism of Cu 2 S during sodiation/desodiation is unveiled using advanced synchrotron-based in-situ X-ray diffraction and X-ray adsorption spectroscopy. This work represents a great advance in seeking high-performance anodes in SIBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Unrealized Critical Lanthanide Extraction from Sea Algae Mining (UNCLE SAM): Domestic production of critical minerals from seawater

The UNCLE-SAM project, under the Biotechnologies to Ensure a Robust Supply of Critical Materials for Clean Energy program, examined the biomining applications of seaweeds for sustainable, domestic production of critical mineral feedstocks. The ocean is a vast reserve of mineralogical wealth including rare earth elements (REEs) and platinum group metal (PGMs). These elements, categorized as “critical minerals”, are used in telecommunication devices, lasers, LED lighting, turbine generators, electric car motors, jet engine alloys, and many other applications. These critical elements are increasingly vital to a thriving, efficient and sustainable society. However, only a few countries in the global market currently produce and export REEs, leading to potential geopolitical supply disruptions. Marine macroalgae, often referred to as seaweeds, bioconcentrate critical minerals from seawater, including REEs and PGMs. Marine algae cultivation can generate a significant amount of biomass with minimal freshwater, fertilizer, and land requirements. In summary, the UNCLE-SAM project successfully evaluated the technological feasibility for marine macroalgal cultivation as a feedstock for critical minerals, explored the biological capacity of different seaweeds to provide economically relevant domestic mineral production, assessed processing techniques for thermal co-conversion of seaweeds into renewable fuel and mineral feedstocks, and executed techno-economic and lifecycle assessments for identifying the most critical gaps in our current understanding to move the technology into commercially relevant deployment. Further development of this technology could transform the bioproduct and REE mining industries and catalyze the development of a more sustainable future.

58 GEOSCIENCES↗
DOE OSTI2023

UNrealized Critical Lanthanide Extraction from Sea Algae Mining (UNCLE SAM)

The UNCLE-SAM project, under the Biotechnologies to Ensure a Robust Supply of Critical Materials for Clean Energy program, examined the biomining applications of seaweeds for sustainable, domestic production of critical mineral feedstocks. The ocean is a vast reserve of mineralogical wealth including rare earth elements (REEs) and platinum group metal (PGMs). These elements, categorized as “critical minerals”, are used in telecommunication devices, lasers, LED lighting, turbine generators, electric car motors, jet engine alloys, and many other applications. These critical elements are increasingly vital to a thriving, efficient and sustainable society. However, only a few countries in the global market currently produce and export REEs, leading to potential geopolitical supply disruptions. Marine macroalgae, often referred to as seaweeds, bioconcentrate critical minerals from seawater, including REEs and PGMs. Marine algae cultivation can generate a significant amount of biomass with minimal freshwater, fertilizer, and land requirements. In summary, the UNCLE-SAM project successfully evaluated the technological feasibility for marine macroalgal cultivation as a feedstock for critical minerals, explored the biological capacity of different seaweeds to provide economically relevant domestic mineral production, assessed processing techniques for thermal co-conversion of seaweeds into renewable fuel and mineral feedstocks, and executed techno-economic and lifecycle assessments for identifying the most critical gaps in our current understanding to move the technology into commercially relevant deployment. Further development of this technology could transform the bioproduct and REE mining industries and catalyze the development of a more sustainable future.

09 BIOMASS FUELS↗
DOE OSTI2020

Investigation of Exciton Delocalization and Exciton Coherence in Chromophores and Acoustic Nanostructures

Quantum materials have a rich potential for providing transformative energy technologies ranging from new energy efficient computation paradigms to lossless energy transport across the national grid. The field of quantum materials, however, is still in its infancy with many quantum materials systems requiring cyrogenic temperatures to be viable. Few viable, room-temperature quantum materials systems have been identified; therefore, identifying additional such systems is vitally important. This state of the science combined with the low number of potential systems that exhibit room-temperature quantum character make this field of research fertile ground for EPSCoR states (states eligible for funding under the Established Program to Stimulate Competitive Research program) such as Idaho to compete on a national level. As a result, this project proposed and accomplished two main goals in advancing two room-temperature quantum materials systems: (1) advance the science for demonstrating room temperature exciton delocalization in two excitonic systems and (2) build capacity and expertise in developing and understanding quantum materials at both Boise State University and Idaho National Laboratory. The work at Boise State focused mainly on the use of nucleic acid self-assembled chromophore molecule networks that offer a much easier and mature approach to construct aggregates of chromophores that can be tailored to specific two-dimensional and three-dimensional geometries and allow exciton manipulation towards achieving room-temperature quantum computation. A second complementary effort performed at Boise State and INL focused on confining excitons on quantum length scales using coherent surface acoustic phonons

36 MATERIALS SCIENCE↗
DOE OSTI2023

On-Chip Batteries as Distributed Energy Sources in Heterogeneous 2.5D/3D Integrated Circuits

Energy efficiency in digital systems faces challenges due to the constraints imposed by small-scale transistors. Moreover, the growing demand for portable consumer electronics necessitates the use of compact energy sources. To address these challenges, heterogeneous 3D IC technology has emerged as a promising solution for the former. Regarding the latter, we propose the concept of distributed batteries within a heterogeneous 3D IC. This approach involves utilizing multiple smaller batteries with different specifications among different modules of 3D ICs. This approach optimizes performance and overcomes limitations associated with both 3D ICs and conventional power delivery methods. Distributed batteries play a vital role in effectively managing the heat generated by energy sources and modules within a 3D IC. Furthermore, they contribute to achieving a uniform distribution of heat throughout the entire structure, which ultimately ensures the optimal performance of the batteries and modules. The simulation results indicate a 40 percent enhancement in achieving a more even distribution of generated heat. Additionally, the proposed distributed battery techniques improve power delivery, enhance reliability, and enable optimized voltage regulation while improving efficiency. In addition to the primary benefits, alternative configurations of the proposed approach can offer extra energy storage capacity and act as efficient electromagnetic shields, resulting in an impressive reduction of external electromagnetic noises by 60 dB.

47 OTHER INSTRUMENTATION↗
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 OSTI2024

Mitigating Calendar Aging in Si-NMC Batteries with Advanced Dual-Salt Glyme Electrolytes

In addressing the critical challenge of calendar aging in silicon (Si)-based lithium-ion batteries, this study introduces a groundbreaking strategy utilizing glyme-type dual-salt electrolytes (lithium bis(trifluoromethanesulfonyl)imide [LiTFSI] and lithium difluoro(oxalato)borate [LiDFOB]). These electrolytes are demonstrated to significantly mitigate parasitic reactions and capacity loss in Si-NMC (lithium nickel manganese cobalt oxide) full cells, especially when compared with traditional carbonate-based electrolytes. Further, our exhaustive mechanistic analysis reveals that such electrolytes not only preserve the integrity of the Si anode but also improve the cathode/electrolyte interphases (CEI) through the formation of a conformal coating on the high-voltage cathode surface. This dual-salt approach, enhanced by the addition of a phosphate additive, effectively decelerates calendar aging, marking a substantial advance in the quest for durable and reliable Si-based energy storage technologies. The findings underscore the vital role of electrolyte composition in extending the calendar life of Si batteries, offering an alternative avenue toward maximizing the performance and longevity of next-generation Li-Si batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bridging the Gap between Cosmological Simulations with Graph Neural Networks and Domain Adaptation

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross dataset tasks (up to 28% better relative error and up to almost an order of magnitude better χ 2 ). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

Deep learning models have been shown to outperform methods that rely on summary statistics, like the power spectrum, in extracting information from complex cosmological data sets. However, due to differences in the subgrid physics implementation and numerical approximations across different simulation suites, models trained on data from one cosmological simulation show a drop in performance when tested on another. Similarly, models trained on any of the simulations would also likely experience a drop in performance when applied to observational data. Training on data from two different suites of the CAMELS hydrodynamic cosmological simulations, we examine the generalization capabilities of Domain Adaptive Graph Neural Networks (DA-GNNs). By utilizing GNNs, we capitalize on their capacity to capture structured scale-free cosmological information from galaxy distributions. Moreover, by including unsupervised domain adaptation via Maximum Mean Discrepancy (MMD), we enable our models to extract domain-invariant features. We demonstrate that DA-GNN achieves higher accuracy and robustness on cross-dataset tasks (up to $28\%$ better relative error and up to almost an order of magnitude better $\chi^2$). Using data visualizations, we show the effects of domain adaptation on proper latent space data alignment. This shows that DA-GNNs are a promising method for extracting domain-independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Global variations in critical drought thresholds that impact vegetation

ABSTRACT Identifying the thresholds of drought that, if crossed, suppress vegetation functioning is vital for accurate quantification of how land ecosystems respond to climate variability and change. We present a globally applicable framework to identify drought thresholds for vegetation responses to different levels of known soil-moisture deficits using four remotely sensed vegetation proxies spanning 2001–2018. The thresholds identified represent critical inflection points for changing vegetation responses from highly resistant to highly vulnerable in response to drought stress, and as a warning signal for substantial vegetation impacts. Drought thresholds varied geographically, with much lower percentiles of soil-moisture anomalies in vegetated areas covered by more forests, corresponding to a comparably stronger capacity to mitigate soil water deficit stress in forested ecosystems. Generally, those lower thresholds are detected in more humid climates. State-of-the-art land models, however, overestimated thresholds of soil moisture (i.e. overestimating drought impacts), especially in more humid areas with higher forest covers and arid areas with few forest covers. Based on climate model projections, we predict that the risk of vegetation damage will increase by the end of the twenty-first century in some hotspots like East Asia, Europe, Amazon, southern Australia and eastern and southern Africa. Our data-based results will inform projections on future drought impacts on terrestrial ecosystems and provide an effective tool for drought management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Development of a SnO 2 -based 44 Ti/ 44 Sc generator for medical applications

Towards application of 44 Sc for diagnostic nuclear medicine, a 44 Ti/ 44 Sc generator based on an inorganic resin has been evaluated. Unlike other radionuclide generators used for medical applications, the long-term retention of the parent 44 Ti is vital due to its long half life. In this work, tin dioxide (SnO 2 ), a robust inorganic-based resin, has been synthesized and used as the stationary phase for a 44 Ti/ 44 Sc generator. The sorption behavior of 44 Ti/ 44 Sc was tested on SnO 2 with varying acids, concentrations, and times. Preliminary batch study results showed >88 % 44 Ti retention to the resin at lower acid concentrations (0.05 M HNO 3 and 0.05 M HCl). A pilot generator was evaluated for a year, demonstrating 85.3 ± 2.8 % 44 Sc elution yields and 0.71 ± 0.14 % 44 Ti breakthrough in 5 M HNO 3 . Based on capacity studies, a 7.4 MBq (200 µCi) upscaled generator system was constructed for further evaluation of the SnO 2 resin stability and the efficacy of the eluted 44 Sc for radiolabeling. 44 Sc could be regularly eluted from this generator in 5 M HNO 3 with an overall average radiochemical yield 84.7 ± 9.5 %. Post-elution processing of the 44 Sc with DGA-normal resin removed all 44 Ti present and allowed for high 44 Sc-DOTA labeling yields of 94.2 ± 0.5 %. Overall, SnO 2 has been shown to be a viable material for a 44 Ti/ 44 Sc generator.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

All-Solid-State Lithium–Sulfur Batteries Enhanced by Redox Mediators

We report redox mediators (RMs) play a vital role in some liquid electrolyte-based electrochemical energy storage systems. However, the concept of redox mediator in solid-state batteries remains unexplored. Here, we selected a group of RM candidates and investigated their behaviors and roles in all-solid-state lithium-sulfur batteries (ASSLSBs). The soluble-type quinone-based RM (AQT) shows the most favorable redox potential and the best redox reversibility that functions well for lithium sulfide (Li 2 S) oxidation in solid polymer electrolytes. Accordingly, Li 2 S cathodes with AQT RMs present a significantly reduced energy barrier (average oxidation potential of 2.4 V) during initial charging at 0.1 C at 60 °C and the following discharge capacity of 1133 mAh g s -1 . Using operando sulfur K-edge X-ray absorption spectroscopy, we directly tracked the sulfur speciation in ASSLSBs and proved that the solid-polysulfide-solid reaction of Li 2 S cathodes with RMs facilitated Li 2 S oxidation. In contrast, for bare Li 2 S cathodes, the solid-solid Li 2 S-sulfur direct conversion in the first charge cycle results in a high energy barrier for activation (charge to ~4 V) and low sulfur utilization. The Li 2 S@AQT cell demonstrates superior cycling stability (average Coulombic efficiency 98.9% for 150 cycles) and rate capability owing to the effective AQT-enhanced Li-S reaction kinetics. This work reveals the evolution of sulfur species in ASSLSBs and realizes the fast Li-S reaction kinetics by designing an effective sulfur speciation pathway

25 ENERGY STORAGE↗
DOE OSTI2020

Low-temperature and high-rate-charging lithium metal batteries enabled by an electrochemically active monolayer-regulated interface

Stable operation of rechargeable lithium (Li)-based batteries at low temperatures is vital for cold-climate applications but is plagued with dendritic Li plating and unstable solid-electrolyte interphase (SEI). Here we report high-performance Li-metal batteries under low-temperature and high-rate-charging conditions. This is realized by utilizing a self-assembled monolayer of electrochemically active molecules on current collectors that regulates the nanostructure and composition of SEI and deposition morphology of Li metal anodes. A multilayer SEI containing a LiF-rich inner phase and amorphous outer layer effectively seals the Li surface in contrast to the conventional impassive SEI at low temperatures. As a result, galvanic Li corrosion and self-discharge were suppressed; stable Li deposition was realized from -60º to 45ºC; and a Li|LiCoO 2 cell with a capacity of 2.0 mAh cm -2 displayed a 200-cycle life at -15ºC with a recharge time of 45 minutes.

25 ENERGY STORAGE↗
DOE OSTI2025

ASEAN Technical Exchange Workshop for System Operators, Regulators, and Policymakers

This presentation provides an in-depth exploration of power system planning, cross-border electricity trading, and battery energy storage systems (BESS), offering actionable insights for system operators, regulators, and policymakers. The first section delves into power system planning and analysis, focusing on capacity expansion models and resource adequacy studies, including their role in optimizing system efficiency, managing emissions, and addressing system reliability risks. Key considerations, such as integration of transmission into generation planning and the forecasting versus optimization of customer distributed energy resources (DER) technologies, are explored. The session highlights critical trade-offs in spatial granularity and model runtimes, as well as the feasibility of aligning distribution investments with capacity expansion efforts. The second section examines cross-border electricity trading, with an emphasis on resource adequacy concepts such as reliability targets, loss of load expectation (LOLE), and planning reserve margins (PRM). Case studies on reserve market design and coordination across US regions provide insights into improving reserve deliverability and managing interregional power balance and congestion. This section also addresses market-to-market congestion management, including advanced strategies for high-voltage direct current (HVDC) optimization and ancillary service delivery. Finally, the presentation covers the rapid evolution of Battery Energy Storage Systems (BESS), highlighting their operational growth, regulatory frameworks, and use cases in grid flexibility, energy storage, and reliability. The discussion focuses on the benefits of BESS for system stability, resilience, and integration of renewable energy, offering insights into its role as a vital component in the transition toward a more sustainable and flexible grid. Key performance parameters, such as throughput, round-trip efficiency, and state of charge, are also examined.

25 ENERGY STORAGE↗