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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mechanistic insights into CO 2 capture and electrochemical conversion in nonaqueous Na–CO 2 batteries

Developing efficient energy storage systems that capture and convert CO 2 is critical for mitigating carbon emissions. Here, we report a Na–CO 2 battery with ruthenium dioxide (RuO 2 ) cathode catalysts and propane-1,3-diamine (PDA) as an electrolyte additive to enhance CO 2 capture and conversion efficiency. The integration of CO 2 adsorption and electrochemical reduction facilitates activation of the inert CO 2 molecule and circumvents gas–solid–liquid ternary-phase reactions at the interface. We employed density functional theory (DFT) calculations to systematically unravel the reaction mechanisms and energetics governing CO 2 reduction, both with and without PDA. Our results reveal an energetically favorable pathway toward the formation of Na 2 CO 3 and C as final discharge products, rather than sodium oxalate (Na 2 C 2 O 4 ). The CO 2 –amine adduct facilitates charge transfer from PDA to CO 2 , which results in activation of CO 2 . The kinetics of CO 2 conversion and regeneration of PDA were found to be significantly enhanced on the RuO 2 surface compared to the bulk electrolyte. More importantly, pre-activation of CO 2 via the amine–CO 2 adduct lowers the total overpotential to 2.44 V, compared to 3.13 V without PDA. This study provides fundamental insights into CO 2 electroreduction in Na–CO 2 batteries and underscores the promise of electrolyte engineering for sustainable CO 2 utilization and high-performance energy storage.

25 ENERGY STORAGE↗
DOE OSTI2025

Cyclic loading–unloading impacts on geomechanical stability of multiple salt caverns for underground hydrogen storage

Underground caverns in a salt dome are promising geologic features to store hydrogen because of salt’s extremely low permeability and self-healing behavior. The salt cavern storage community, however, has not fully understood the geomechanical behaviors of salt rock driven by quick operation cycles of injection–production, which may significantly impact the cost-effective storage-recovery performance of multiple caverns. Our field-scale generic model captures the impact of cyclic loading–unloading on the salt creep behavior and deformation under different cycle frequencies, operating pressure, and spatial order of operating cavern(s). This systematic simulation study indicates that the initial operation cycle and arrangement of multiple caverns play a significant role in the creep-driven loss of cavern volumes and cavern deformation. Finally, our future study will develop a new salt constitutive model based on geomechanical tests of site-specific salt rock to probe the cyclic behaviors of salt precisely both beneath and above the dilatancy boundary, including reverse (inverse transient) creep, the Bauschinger effect, and damage-healing mechanism.

08 HYDROGEN↗
DOE OSTI2020

A New Family of Proton‐Conducting Electrolytes for Reversible Solid Oxide Cells: BaHf x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ

Abstract Reversible solid oxide cells based on ceramic proton conductors have potential to be the most efficient system for large‐scale energy storage. The performance and long‐term durability of these systems, however, are often limited by the ionic conductivity or stability of the proton‐conducting electrolyte. Here new family of solid oxide electrolytes, BaHf x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ (BHCYYb), which demonstrate a superior ionic conductivity to stability trade‐off than the state‐of‐the‐art proton conductors, BaZr x Ce 0.8− x Y 0.1 Yb 0.1 O 3− δ (BZCYYb), at similar Zr/Hf concentrations, as confirmed by thermogravimetric analysis, Raman, and X‐ray diffraction analysis of samples over 500 h of testing are reported. The increase in performance is revealed through thermodynamic arguments and first‐principle calculations. In addition, lab scale full cells are fabricated, demonstrating high peak power densities of 1.1, 1.4, and 1.6 W cm −2 at 600, 650, and 700 °C, respectively. Round‐trip efficiencies for steam electrolysis at 1 A cm −2 are 78%, 72%, and 62% at 700, 650, and 600 °C, respectively. Finally, CO 2 H 2 O electrolysis is carried out for over 700 h with no degradation.

Murphy, Ryan↗
DOE OSTI2021

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

Abstract Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density‐of‐states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high‐quality prediction using a small training set of examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high‐performance thermal storage materials and phonon‐mediated superconductors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

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

Low‐Temperature and High‐Rate Rechargeable Aluminum Batteries Enabled by Ternary Eutectic Electrolytes

Abstract Rechargeable aluminum batteries (RABs) have garnered extensive scientific attention as a promising alternative chemistry due to the inherent advantages associated with aluminum (Al) metal anodes, including their high theoretical capacities, cost‐effectiveness, environmental friendliness, and inherent non‐flammable properties. Nonetheless, the practical energy density of RABs is constrained by the electrolytes that support lower operational voltage windows. Herein, we report a ternary eutectic electrolyte composed of 1‐ethyl‐3‐methylimidazolium chloride ([C 2 C 1 im]Cl):1‐butyl‐3‐methylimidazolium chloride ([C 4 C 1 im]Cl):aluminum chloride (AlCl 3 ) for the application of RABs. The electrolyte exhibits a high operational potential window (~3 V vs. Al/Al 3+ on SS 316) and high ionic conductivity (~8.3 mS cm −1 ) while exhibiting only a low temperature glass transition at −65 °C suitable for all‐climate conditions. Al||graphene nanoplatelets cell delivers a high capacity of ~117 mAh/g, and ~43 mAh/g at a very high current densities of 1 A/g and 5 A/g, respectively. The cells render a reversible capacity of 20 mAh/g at −20 °C and 17 mAh/g at −40 °C, indicating their suitability for operation under extreme environmental conditions. We comprehensively evaluated the design and optimization of carbon paper‐based battery systems. The ternary eutectic electrolyte demonstrates exceptional electrochemical performance, thus signifying its substantial potential for utilization in high‐performance energy storage systems in all climates.

Vadthya, Raju↗
DOE OSTI2024

Off-design operation and performance of pumped thermal energy storage

In this article, we describe off-design models and control strategies for a Pumped Thermal Energy Storage (PTES) system that uses liquid thermal energy storage: specifically molten salt for hot storage and methanol for cold storage. Off-design conditions arise when load-following, or due to variations in storage tank temperatures or ambient temperatures. We propose a control strategy that uses inventory control to manage the mass flow rate in the thermodynamic cycles, which facilitates load following. We also propose a control strategy for the storage fluid mass flow rates, which are varied to ensure the molten salt is maintained at its design temperature. This maximizes efficiency and minimizes problems with salt freezing or degradation. The cold storage fluid mass flow rate is varied so that the cold tanks have the same state-of-charge as the hot tanks. This leads to variations in cold fluid temperature, but these variations are shown to be acceptably small (e.g. 7.5% increase), and this control method is shown to be simpler and more efficient than an alternative strategy where tanks become unbalanced. The ambient temperature and storage tank temperatures are moved ±50 °C from the design values and the impact on power, duration, and tank temperatures is quantified. Results demonstrate that the proposed control strategy is stable and self-correcting - that is, storage temperatures converge on stable values after two-to-three charge-discharge cycles. When inputs return to design values, the system returns to its design point after two charge-discharge cycles. We also demonstrate that inventory control enables delivery of the target power output even when off-design conditions exist that would normally reduce the power output.

25 ENERGY STORAGE↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
DOE OSTI2023

Oxygen-Vacancy Abundant Nanoporous Ni/NiMnO 3 /MnO 2 @NiMn Electrodes with Ultrahigh Capacitance and Energy Density for Supercapacitors

High-performance energy storage devices (HPEDs) play a critical role in the realization of clean energy and thus enable the overarching pursuit of nonpolluting, green technologies. Supercapacitors are one class of such lucrative HPEDs; however, a serious limiting factor of supercapacitor technology is its sub-par energy density. Here, this report presents hitherto unchartered pathway of physical deformation, chemical dealloying, and microstructure engineering to produce ultrahigh-capacitance, energy-dense NiMn alloy electrodes. The activated electrode delivered an ultrahigh specific-capacitance of 2700 F/cm 3 at 0.5 A/cm 3 . The symmetric device showcased an excellent energy density of 96.94 Wh/L and a remarkable cycle life of 95% retention after 10,000 cycles. Transmission electron microscopy and atom probe tomography studies revealed the evolution of a unique hierarchical microstructure comprising fine Ni/NiMnO 3 nanoligaments within MnO 2 -rich nanoflakes. Theoretical analysis using density functional theory showed semimetallic nature of the nanoscaled oxygen-vacancy-rich NiMnO 3 structure, highlighting enhanced carrier concentration and electronic conductivity of the active region. Furthermore, the geometrical model of NiMnO 3 crystals revealed relatively large voids, likely providing channels for the ion intercalation/de-intercalation. The current processing approach is highly adaptable and can be applied to a wide range of material systems for designing highly efficient electrodes for energy-storage devices.

25 ENERGY STORAGE↗
DOE OSTI2024

Stabilized Oxygen Vacancy Chemistry toward High-Performance Layered Oxide Cathodes for Sodium-Ion Batteries

Anionic redox has emerged as a transformative paradigm for high-energy layered transition-metal (TM) oxide cathodes, but it is usually accompanied by the formation of anionic redox-mediated oxygen vacancies (OVs) due to irreversible oxygen release. Additionally, external factor-induced OVs (defined as intrinsic OVs) also play a pivotal role in the physicochemical properties of layered TM oxides. However, an in-depth understanding of the interplay between intrinsic and anionic redox-mediated OVs and the corresponding regulation mechanism of the dynamic evolution of OVs is still missing. Herein, we disclose the strong interrelationship between these OVs and demonstrate that the presence of intrinsic OVs in the TMO2 layers could induce weak integrity of the TM-O frameworks and unlock additional diffusion paths to trigger the generation and migration of anionic redox-mediated OVs. Accordingly, an OV stabilization strategy is proposed by deliberately introducing high-valence Nb5+, which could serve as an important building block in anchoring the oxygen sublattice and preventing the formation of a percolating OV migration network, thereby suppressing the formation/diffusion of anionic redox-mediated OVs. Consequently, superb structural integrity and improved electrochemical performance with reversible anionic redox chemistry are achieved. This work advances our understanding of the role of OVs for developing high-performance energy storage systems utilizing anionic redox.

anionic redox↗
DOE OSTI2021

Efficient diffusion of superdense lithium via atomic channels for dendrite-free lithium–metal batteries

The non-uniform aggregation of fast-diffused Li on an anode surface would aggravate its tip-effect-induced nucleation/growth, leading to the notorious dendrite growth in Li metal batteries (LMBs). Tuning the Li diffusion on the anode surface has been regarded previously as a mainstream method to induce its uniform deposition, while the diffusion of Li in the anode bulk is usually ignored. In this study, conceptually different from the classic surface modification, we propose a molecular tunnelling strategy to construct atomic channels in graphite bulk, which enables the fast diffusion of superdense Li. Density functional theory calculations and ab initio molecular dynamics simulations prove that the bulk diffusion through atomic channels could become a new and dominating path. Its reversible and efficient diffusion has been further visualized by in situ transmission electron microscopy. As a result, when coupled with high-loading LiFePO 4 cathodes (20 mg cm –2 ), a high areal capacity and 100% capacity retention are achieved over 370 cycles. Through this work a new strategy is developed based on the bulk-diffusion of superdense Li for dendrite-free LMBs, which can be pervasive in other high-performance energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2022

Pairwise connected tensor network representation of path integrals

It has been recently shown how the tensorial nature of real-time path integrals (PIs) involving the Feynman-Vernon influence functional can be utilized with matrix product states, taking advantage of the finite length of the bath-induced memory. Tensor networks (TNs) promise to provide a unified language to express the structure of a PI. A generalized TN specifically incorporating the pairwise interaction structure of the influence functional and its invariance with respect to the average forward-backward position or the sojourn value in the form of the blip representation is derived and implemented. This pairwise connected TNPI (PC-TNPI) is illustrated through applications to typical spin-boson problems and explorations of the differences caused by the exact form of the spectral density. The storage and performance scalings are reported, showing the compactness of the representation and the efficiency of the contraction process. Finally, taking advantage of the compressed representation, the viability of using PC-TNPI for simulating multistate problems is demonstrated. The PC-TNPI structure can be shown to yield other TN algorithms currently in use. Consequently, it should be possible to use it as a starting point for deriving other optimized procedures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗
DOE OSTI2024

Understanding and Enhancing Silicon Nanoparticle Distribution during Electrode Processing

Silicon-dominant anodes are of great interest because of their potential to boost the cell-level energy of state-of-the-art Li-ion batteries. While silicon materials have been extensively studied, understanding interactions at the electrode level has recieved little attention, especially the coating process of Si particles, which plays an equally important role in unlocking the full potential of silicon anodes. Herein, the electrode processing of a Si-dominated anode (52.8 wt%, 3.5–4.5 mAh cm −2 ) is being investigated to understand the relationship of processing on the morphology and properties of Si anodes at the electrode level. It has been found that almost-undetectable Si agglomerates easily form during electrode processing, which grow into largeprotrusions after lithiation and trigger potential internal shorting and self-discharge problems. A facile slurry filtration step is proposed to homogenize the particle distribution within Si-dominant electrodes which improves the electrochemical performance and storage stability of Si-based Li ion batteries.

Wu, Bingbin↗
DOE OSTI2022

COOLING PERFORMANCE IN A DUAL ENERGY STORAGE RING COOLER*

The lon­gi­tu­di­nal and trans­verse emit­tance growth in hadron beams due to in­tra-beam scat­ter­ing (IBS) and other heat­ing sources de­te­ri­o­rate the lu­mi­nos­ity in a col­lider. Hence, a strong hadron beam cool­ing is re­quired to re­duce and pre­serve the emit­tance. The cool­ing of high en­ergy hadron beam is chal­leng­ing. We pro­pose a dual en­ergy stor­age ring-based elec­tron cooler that uses an elec­tron beam to ex­tract heat away from hadron beam in the cooler ring while the elec­tron beam is cooled by syn­chro­tron ra­di­a­tion damp­ing in the high en­ergy damp­ing ring. In this paper, we pre­sent a de­sign of a dual en­ergy stor­age ring-based elec­tron cooler. Fi­nally, the cool­ing per­for­mance is sim­u­lated using Jef­fer­son Lab Sim­u­la­tion Pack­age for Elec­tron Cool­ing (JSPEC) for pro­ton beams at the top en­ergy of 275 GeV for Elec­tron-Ion Col­lider.

Dhital, B.↗