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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Exemption of lattice collapse in Ni–MnO 2 birnessite regulated by the structural water mobility

Lattice collapse and associated mechanical fracture frequently occur in Li-intercalated metal oxide cathodes at the deep charge state upon Li-ion removal, governed by chemical compositions and the resulting electron density of the oxygen atoms. However, similar lattice collapse for metal oxide electrodes in aqueous storage and its mitigation have not been well studied. In this work, we reported the lattice collapse of MnO 2 layered birnessite during the aqueous de-sodiation process at high voltage due to the structural water motion, as evidenced by in situ XRD. Unlike non-aqueous Li-intercalated electrodes, Ni-dopants mitigated the lattice collapse of birnessite at deep charge states. Moreover, density functional theory (DFT) calculations showed that Ni doping induces charge depletion of lattice oxygen due to its higher electronegativity than Mn. This charge reduction, in turn, yields a significant decrease in electrostatic repulsion between the oxygens belonging to lattice and structural water. Classical molecular dynamics simulations based on atomic charges obtained from DFT elucidate that Ni-doping facilitates immobilization of structural water in (Ni)MnO 2 and prevents lattice collapse upon Na-ion removal. (Ni)MnO 2 exempted from lattice collapse shows an improved storage capacity relative to MnO 2 while maintaining similar cycling stability.

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

Designed additive suppresses interpenetration in IRMOF-10

MOF interpenetration leads to reduced surface areas and diminution of guest storage capacity. Here we report an additive strategy to suppress interpenetration within IRMOF-10 resulting in improved surface area and crystallinity upon activation.

Chemistry↗
DOE OSTI2025

Enhancing the low-temperature performance of Pt-based three-way catalysts using CeO 2 (core)@ZrO 2 (shell) supports

Developing robust Pt/CeO 2 -based three-way catalysts (TWCs) with enhanced oxygen buffering capability and low-temperature activity is highly desirable. In this study, a new TWC family, Pt/(1 − x)CeO 2 (core)@xZrO 2 (shell) (where x = 0–0.5), was prepared and evaluated at degreened (DG) and hydrothermally aged (HTA) states. Incorporation of 0.1 molar concentration of ZrO 2 resulted in a decreased temperature that 50% (T 50 ) (CO: 167 °C, THCs: 218 °C, NO: 228 °C) and 90% (T 90 ) (CO: 207 °C, THCs: 237 °C, NO: 244 °C) conversions achieved over HTA 1.8 wt% Pt/0.9CeO 2 @0.1ZrO 2 compared to the HTA 1.8 wt% Pt/CeO 2 sphere (CO: T50,90 = 179, 222 °C, THCs: 234, 252 °C, NO x : 240, 260 °C). An enhanced oxygen storage capacity and oxygen release rate were observed over Pt/0.9CeO 2 @0.1ZrO 2 compared to the Pt/CeO 2 sphere. Increasing the ZrO 2 molar concentration to values greater than 0.2 resulted in increased T 50 s (224, 265 274 °C) and T 90 s (251, 289, 292 °C) for CO, THCs, and NO x , respectively, over 1.8 wt.% Pt/0.5CeO 2 @0.5ZrO 2 . Overall, this work highlights the potential of forming a ZrO 2 shell on CeO 2 spheres as a support for TWC applications.

Liu, Chih-Han [State Univ. of New York (SUNY), Buf↗
DOE OSTI2024

Hard carbon with an opened pore structure for enhanced sodium storage performance

The pore structure of hard carbon has a significant impact on its Na + storage capacity. Herein, a waste wood-derived hard carbon with opened pores (OP-HC) was fabricated with polyvinyl pyrrolidone (PVP) as an additive. Ex situ SAXS and HR-TEM testing results indicate that OP-HC with opened pores and enlarged d 002 interlayer spacing facilitates the reversible (de)sodiation of Na + ions. In situ TEM and XRD testing results demonstrate that OP-HC shows excellent structure stability during the (de)sodiation process. Thus, OP-HC delivers a high reversible charge capacity of 350.7 mA h g −1 at 0.05 C and an ultra-high initial Coulombic efficiency (ICE) of 94.9%. Moreover, OP-HC exhibits excellent cycling stability, and the assembled 18650 full cell with an OP-HC anode can achieve a high capacity retention of 94.5% after 400 cycles at 1.0 A. Furthermore, the excellent electrochemical performance and deep insights into OP-HC with opened pores and increased d 002 interlayer spacing offer a new strategy to design high-performance HC anodes for SIBs.

You, Shunzhang↗
DOE OSTI2026

Mass spectral molecular mapping shows benefits of thermal evaporation in prelithiated silicon-based electrodes

Silicon based composites have become increasingly popular as potential anodes for lithium-ion batteries due to their large storage capacity and potential ability to generate batteries with energy densities greater than 350 Wh kg −1 . These anodes often see reduced initial columbic efficiency (ICE) due to disruptive volume expansionup to 300% and continuous solid electrolyte interphase (SEI) layer formation. Prelithiation, where an excess reservoir of Li is added to the electrode to compensate for irreversible SEI formation losses during their sample preparation, has proven to solve the issue of immediate capacity loss. Thermal evaporation is a prelithiation technique with limited studies on its effectiveness. In this study, time-of-flight secondary ion mass spectrometry (ToF-SIMS) is used to highlight the benefits of prelithiation via thermal evaporation. ToF-SIMS provides chemical mapping and spatial information in 2D and 3D visualizing the deposition of lithium, identifying Li x Si y alloy and Li x Si y O z silicate formation, and the distribution of lithium passivation into the electrodes. Passivation under different atmospheric conditions, such as inert Argon (Ar) and Ar/ carbon dioxide (CO 2 ), highlights the impact of the environment on the passivation effectiveness and formation of Li x Si y alloy and Li x Si y O z silicate. The ToF-SIMS molecular imaging and depth profiling results indicate that prelithiation via thermal evaporation effectively distributes lithium throughout the depth profile thickness of several hundred nanometers. It induces a greater degree of Li x Si y O z silicate formation over Li x Si y alloy. Our ToF-SIMS characterization results show the effectiveness of thermal evaporation in producing a more stable electrode and an electrode with an effective lithium reserve that can preserve its capacity.

Parker, Gabriel D. [Oak Ridge National Laboratory ↗
DOE OSTI2026

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗
DOE OSTI2025

Data Placement Optimization for ATLAS in a Multi-Tiered Storage System within a Data Center

Scientific experiments and computations, especially in High Energy Physics, are generating and accumulating data at an unprecedented rate. Effectively managing this vast volume of data while ensuring efficient data analysis poses a significant challenge for data centers, which must integrate various storage technologies. This paper proposes addressing this challenge by designing and developing a precise data popularity prediction model utilizing state-of-theart AI/ML techniques. This model is crafted from the analysis of ATLAS data and access patterns. It enables us to migrate infrequently accessed data to more economical storage media, such as tape drives, while storing frequently accessed data on faster yet costlier storage media like HDD or SSD. This strategic approach ensures data is placed optimally into the appropriate storage classes, thereby maximizing storage capacity while minimizing data access latency for end-users. Furthermore, the paper includes a performance evaluation of the prediction model using various key metrics such as F1 score, accuracy, precision and recall. Finally, we present a prototype use case, leveraging real-world file access data to assess the model’s impact on performance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model

The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2020

Measurement of Palladium Hydride and Palladium Deuteride Isotherms Between 130 K and 393 K

The data collected in the present work extend the measured phase diagram for palladium hydride and palladium deuteride to a region that has been sparsely reported in open literature. Absorption isotherms were measured using a 2.5-g bed of palladium powder at temperatures between 130 K and 393 K and pressures less than 1.3 x 10 5 Pa. Such low-pressure and low-temperature measurements are useful for characterizing palladium beds used for tritium pumping and storage. For tritium storage, pressures are kept below a few millibar for safety reasons. Low temperatures increase the tritium storage capacity of palladium. The measured absorption isotherms show the well-documented, two-phase behavior for this system: two solubility regions and a mixed, hydride-forming region. The isotherms show that an increased quantity of hydride is formed at lower temperatures, as marked by an increase in the hydride-forming region. This region exceeds hydrogen to metal ratios of 0.75 for T ≤ 273 K. Equilibrium pressures in the mixed region decrease with decreasing temperatures until a critical temperature is reached for each isotope. Below these critical temperatures, the rate of pressure decrease with decreasing temperature is significantly reduced. This change in trend suggests hydrogen isotopes are adsorbed onto the palladium surface, rather than forming a hydride. Using the equilibrium pressures recorded at temperatures between 236 K and 393 K for protium and between 211 K and 354 K for deuterium, the van’t Hoff constants were calculated to be A = –36 ± 1 kJ/mol and B = 88 ± 3 J/K for protium and A = -32 ± 2 kJ/mol and B = 88 ± 9 J/K for deuterium. As a result, these constants agree favorably with literature in the range where the temperatures of the measured isotherms overlap.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

MnSn 2 and MnSn 2 –TiO 2 nanostructured anode materials for lithium-ion batteries

The high theoretical lithium storage capacity of Sn makes it an enticing anode material for Li-ion batteries (LIBs); however, its large volumetric expansion during Li–Sn alloying must be addressed. Combining Sn with metals that are electrochemically inactive to lithium leads to intermetallics that can alleviate volumetric expansion issues and still enable high capacity. Here, we present the cycling behavior of a nanostructured MnSn 2 intermetallic used in LIBs. Nanostructured MnSn 2 is synthesized by reducing Sn and Mn salts using a hot injection method. The resulting MnSn 2 is characterized by x-ray diffraction and transmission electron microscopy and then is investigated as an anode for LIBs. The MnSn 2 electrode delivers a stable capacity of 514 mAh g -1 after 100 cycles at a C/10 current rate with a Coulombic efficiency >99%. Unlike other Sn-intermetallic anodes, an activation overpotential peak near 0.9 V versus Li is present from the second lithiation and in subsequent cycles. We hypothesize that this effect is likely due to electrolyte reactions with segregated Mn from MnSn 2 . To prevent these undesirable Mn reactions with the electrolyte, a 5 nm TiO 2 protection layer is applied onto the MnSn 2 electrode surface via atomic layer deposition. The TiO 2 -coated MnSn 2 electrodes do not exhibit the activation overpotential peak. The protection layer also increases the capacity to 612 mAh g -1 after 100 cycles at a C/10 current rate with a Coulombic efficiency >99%. This higher capacity is achieved by suppressing the parasitic reaction of Mn with the electrolyte, as is supported by x-ray photoelectron spectroscopy analysis.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Pre-conditioned BFGS-based uncertainty quantification in elastic full-waveform inversion

SUMMARY Full-waveform inversion has become an essential technique for mapping geophysical subsurface structures. However, proper uncertainty quantification is often lacking in current applications. In theory, uncertainty quantification is related to the inverse Hessian (or the posterior covariance matrix). Even for common geophysical inverse problems its calculation is beyond the computational and storage capacities of the largest high-performance computing systems. In this study, we amend the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to perform uncertainty quantification for large-scale applications. For seismic inverse problems, the limited-memory BFGS (L-BFGS) method prevails as the most efficient quasi-Newton method. We aim to augment it further to obtain an approximate inverse Hessian for uncertainty quantification in FWI. To facilitate retrieval of the inverse Hessian, we combine BFGS (essentially a full-history L-BFGS) with randomized singular value decomposition to determine a low-rank approximation of the inverse Hessian. Setting the rank number equal to the number of iterations makes this solution efficient and memory-affordable even for large-scale problems. Furthermore, based on the Gauss–Newton method, we formulate different initial, diagonal Hessian matrices as pre-conditioners for the inverse scheme and compare their performances in elastic FWI applications. We highlight our approach with the elastic Marmousi benchmark model, demonstrating the applicability of pre-conditioned BFGS for large-scale FWI and uncertainty quantification.

58 GEOSCIENCES↗
DOE OSTI2024

Spin-flop coupling at L⁢a 0.5 ⁢S⁢r 0.5 ⁢Fe⁢O 3 /L⁢a 0.7⁢ S⁢r 0.3 ⁢Mn⁢O 3 interfaces

Antiferromagnetic (AFM) spintronics offer several benefits compared to their ferromagnetic (FM) counterparts, such as high storage capacity and faster processing speed, however, difficulties in manipulating and detecting the AFM moments impede their implementation. Spin-flop coupling, the interfacial perpendicular coupling between FM and AFM moments, can be utilized to control the orientation of AFM moments with the application of moderate magnetic fields on the scale of tenths of a Tesla. Here, in this work, epitaxial bilayers of AFM La 0.5 Sr 0.5 FeO 3 (LSFO)/FM La 0.7 Sr 0.3 MnO 3 (LSMO) with fixed LSMO thickness (~85 u.c.) and LSFO thicknesses varying from 10 to 50 u.c. were investigated to determine the effect of Sr doping and La 1-x Sr x FeO 3 magnetocrystalline anisotropy on the strength of spin-flop coupling. X-ray magnetic linear dichroism demonstrated that the spin-flop coupling strength decreased with increasing LSFO layer thickness, persisting at a thickness of 50 u.c. (~20 nm). Furthermore, photoemission electron microscopy revealed a domain-by-domain correlation between the FM and AFM domains consistent with the perpendicular orientation dictated by spin-flop coupling. These results demonstrate that LSFO/LSMO bilayers have the potential to serve as a model materials system for AFM spin transport measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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 OSTI2021

Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation

Today's scientific simulations are producing vast volumes of data that cannot be stored and transferred efficiently because of limited storage capacity, parallel I/O bandwidth, and network bandwidth. The situation is getting worse over time because of the ever-increasing gap between relatively slow data transfer speed and fast-growing computation power in modern supercomputers. Error-bounded lossy compression is becoming one of the most critical techniques for resolving the big scientific data issue, in that it can significantly reduce the scientific data volume while guaranteeing that the reconstructed data is valid for users because of its compression-error-bounding feature. In this paper, we present a novel error-bounded lossy compressor based on a state-of-the-art prediction-based compression framework. Our solution exhibits substantially better compression quality than all of the existing error-bounded lossy compressors, with comparable compression speed. Specifically, our contribution is threefold. (1) We provide an in-depth analysis of why the best-existing prediction-based lossy compressor can only minimally improve the compression quality. (2) We propose a dynamic spline interpolation approach with a series of optimization strategies that can significantly improve the data prediction accuracy, substantially improving the compression quality in turn. (3) We perform a thorough evaluation using six real-world scientific simulation datasets across different science domains to evaluate our solution vs. all other related works. Experiments show that the compression ratio of our solution is higher than that of the second-best lossy compressor by 20%similar to 460% with the same error bound in most of the cases.

Zhao, Kai↗
DOE OSTI2022

Building Energy Systems as Behind-the-Meter Resources for Grid Services: Intelligent load control and transactive control and coordination

To mitigate the impacts of climate change, significant reductions in emissions from all sectors of the economy are needed. The electricity generation sector has embarked on an ambitious plan to include renewable generation as part of its decarbonization efforts, and many cities and states are mandating all-electric buildings. While renewable resources will reduce emissions, they are not dispatchable, they vary temporally, and their generation is uncertain. Under these conditions, traditional approaches to managing grid reliability, where supply follows demand, will not be efficient and may not be cost-effective. Further, there is a more efficient alternative for balancing the supply–demand imbalance and for absorbing variability and uncertainty of renewable energy using distributed energy resources (DERs) as opposed to reserve generation. Because buildings consume more than 75% of total U.S. annual electricity consumption, behind-the-meter (BTM) DERs have a load flexibility of 77 GW of power and 90 GWh of virtual energy storage capacity nationwide (Kalsi, 2017). Therefore, some portion of the supply–demand imbalance can be met by these DERs at a lower cost compared to business-as-usual solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Utility-Scale Shared Energy Storage Deployment: Challenges, Research Gaps, and Opportunities

Although community energy storage (CES) and behind-the-meter (BTM) energy storage systems have been widely used to offer homeowners and communities a variety of localized benefits, their scalability and grid support functionalities are limited. On the other hand, utility-scale shared energy storage (USES) systems may offer a number of benefits for grid integration, scalability, and economic viability. When compared to BTM and CES alternatives, these large-scale systems provide more storage capacity, more efficient operations, and more economically viable options. The deployment of USES presents opportunities for optimizing grid performance, integrating renewable energy resources, and improving energy security at the community level. However, significant research gaps exist in optimizing the integration and operation of these systems, especially to allocate energy for consumer use, grid services, and enhancing energy resilience. This paper reviews the literature in this regard, focusing on the opportunities, research gaps, and challenges associated with USES deployment. Firstly, the paper provides an overview of USES systems and emphasizes their benefits. Secondly, the key challenges are identified, and research gaps associated with the operation and integration of these systems are highlighted. Lastly, some potential solutions and opportunities that can be adopted to facilitate the rapid deployment and management of USES are presented. Technological, economic, regulatory, and environmental aspects are also discussed in this paper, providing an overview of the current state and future prospects of this technology.

Gautam, Mukesh [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
DOE OSTI2022

Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Furthermore, our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

97 MATHEMATICS AND COMPUTING↗