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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Enhancing the Electrochemical Performance of Aqueous Processed Li-Ion Cathodes with Silicon Oxide Coatings

Lithium-ion battery cathode materials suffer from bulk and interfacial degradation issues, which negatively affect their electrochemical performance. Oxide coatings can mitigate some of these problems and improve electrochemical performance. However, current coating strategies have low throughput, are expensive, and have limited applicability. In this article, we describe a low-cost and scalable strategy for applying oxide coatings on cathode materials. Here, we report synergistic effects of these oxide coatings on the performance of aqueously processed cathodes in cells. The SiO 2 coating strategy developed herein improved mechanical, chemical, and electrochemical performance of aqueously processed Ni-, Mn- and Co-based cathodes. This strategy can be used on a variety of cathodes to improve the performance of aqueously processed Li-ion cells.

25 ENERGY STORAGE↗
DOE OSTI2024

Mixed Enthalpy–Entropy Descriptor for the Rational Design of Synthesizable High-Entropy Materials Over Vast Chemical Spaces

The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional material design and discovery. However, the identification of stable and synthesizable HEMs and robust design rules remains a daunting challenge. Here, we propose a mixed enthalpy–entropy descriptor (MEED) that enables highly efficient, robust, high-throughput prediction of synthesizable HEMs across vast chemical spaces from first-principles. The MEED is based on two parameters: the relative formation enthalpy with respect to the most stable competing compound and the spread of the point-defect formation energy spectrum. The former measures the relative synthesizability of an HEM to its most stable competing phase, going beyond the conventional thermodynamic understanding. Further, the latter gauges the relative entropy forming ability of an HEM, entailing no sampling over numerous alloy configurations. By applying the MEED to two structurally distinct representative material systems (i.e., 3D rocksalt carbides and 2D layered sulfides), we not only successfully identify all experimentally reported HEMs within these systems but also reveal a cutoff criterion for assessing their relative synthesizability within each system. By the MEED, tens of new high-entropy carbides and 2D high-entropy sulfides are also predicted, which have the potential for a wide variety of applications such as coating in aerospace devices, energy conversion and storage, and flexible electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Statistical White-Line Analysis in High-Throughput TXM-XANES for Chemical State Quantification

The transmission X-ray microscopy (TXM) based X-ray absorption near-edge structure (XANES) technique provides three-dimensional mapping of element-specific chemical states at nanometer-scale spatial resolution and micrometer-scale fields of view. However, compared to conventional volume-averaged XANES (VA-XANES) measurements, the inherently small voxel size in TXM-XANES leads to a lower signal-to-noise ratio, making full-spectrum analysis computationally demanding and less robust. Here, we present the structural and compositional conditions for a statistical white-line analysis framework under which chemical state information can be directly extracted from the white-line peak position in voxel spectra without the need for voxel-wise background subtraction or normalization, under well-defined structural and compositional conditions. The method is validated on layered oxide cathode materials, where low-order polynomial fitting accurately reproduces white-line features, and the extracted energy distributions correlate strongly with VA-XANES results. This statistical approach enables high-throughput, dose-efficient, and noise-robust chemical state quantification in TXM-XANES, offering broad applicability to functional materials requiring nanoscale oxidation-state mapping.

TXM↗
DOE OSTI2025

Impact of Module Configuration on Lithium-Ion Battery Performance and Degradation: Part I. Energy Throughput, Voltage Spread, and Current Distribution

Batteries are commonly connected in series and parallel to create modules that fulfill the power and energy requirements of specific applications. However, conclusions about battery performance and degradation under different conditions, as well as predictive models, are often derived from single cell cycling results. In this study, we evaluate the performance of six different series-parallel configurations of commercial lithium nickel manganese cobalt cells over hundreds of cycles. Each cell within the modules was individually instrumented for voltage, current, and temperature monitoring. We quantified the impact of module configuration on overall energy throughput, the voltage spread among series-connected cells, and the current heterogeneity in parallel-connected cells. This module cycling study, one of the broadest reported to date, supports systematic evaluation of the performance trade-offs, pack penalty, and safety implications of different module configurations.

25 ENERGY STORAGE↗
DOE OSTI2021

Materials Challenges and Opportunities for Energy Generation, Conversion, Delivery, and Storage (Applied Energy Tri-Laboratory Consortium Workshop Report)

This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scaleup and manufacturability of symmetric-structured metal-supported solid oxide fuel cells

Metal-supported solid oxide fuel cells with symmetric architecture, having metal supports on both sides of the cell, are scaled up from button cell size to large 50 cm 2 active area cell size. The cells remain flat after sintering assisted by the symmetric structure. Equivalent performance is achieved for button cells and large cells, and thermal cycling and redox cycling tolerance are demonstrated for the large cells. The catalyst infiltration process is improved to enable high-throughput manufacturing. The cumbersome lab-scale molten nitrate infiltration process is replaced with a room-temperature process in which a shelf-stable aqueous solution of nitrate salts is applied to the cell by spraying, painting, or other scalable techniques. Here, a fast-ramp thermal conversion of the nitrate salts to the final oxide catalyst composition is implemented, allowing many infiltration cycles to be accomplished in a single work shift. Increasing the number of infiltration cycles from 5 to 10 led to an increase in peak power density from approximately 0.3 to 0.52 W cm -2 .

25 ENERGY STORAGE↗
DOE OSTI2025

High throughput screening of high entropy spinel electrolytes for multivalent batteries

High-entropy (HE) design emerged as a promising path for discovering multivalent superionic conductors. This work provides a computational exploration of the synthesizability of HE spinel-based electrolytes among the typical chemical space. Design principles have been established, while experimental synthesis has supported the stability rules predicted by computational data.

25 ENERGY STORAGE↗
DOE OSTI2022

Energy Exascale Earth System Model v2.0.1

First patch release of v2.0.0 Changes since v2.0.0 [Important change] Fix ocean threading bug seen in debug cases on Chrysalis with Intel 20.0.4. Was introduced around time of v2.0.0 tag. Does not change v2.0.0 answers on Chrysalis because those didn't use threading or debugging. [EAM] Add semi-lagrangian tracer transport for theta-l (F90 and C++), add new algorithm for finding tropopause, add DSCREAM to allow v2 and SCREAM settings in same code such as adjust_ps [EAM-MMF] 60L default, allow C++ back end of RRTMGP (EAM too). [EAMxx] add nu-top functionality, fix forcing functor, add ttype9 and dcmip2012 tests 2.1, 2.2, and 3 HOMME: remove obsolete remap algs, option to specify dynamics alg indep of tracer, new sponge layer, add imex tests [ELM] Add topography-based subgrid (topounits), add FATES-ELM Nitro., Phos. and CH4 coupling, add land-use ts for NARRM, add lulc for SSP3 RCP7, Fix nutrient fertilization exp test and carbon isotope flux, Fix xactive lnd dry deposition, add lake water storage option, fix plant hydraulics 2d params, fix carbon budget calc, fix soil nutrient conc. bug, fix mosart dam bug, add test for new ELM, MOSART features, fix bug in O3 dry dep stomatal resistances, fix plant hydraulics restart BFB error, update mkmapdata. [MOSART] fix bug for reading the latitude from an unstructured input file, fix oversat in bubble test. [MPAS-ocean] Add CFC11, CFC12 tracers, add 2D spherical transport tests, fix del4 tracer mixing, add MARBL ocean tracer mixing, modify harmonic analysis options, add GPU port of vmix routines, fix calc of ML-averaged BV freq. [MPAS-seaice] Change extents of initial polar disks for oRRS18to6v3 grid, fix ice BGC with MARBL, update spherical test cases, fix DON coupling, Remove Cf from sea ice constants. [MPAS-landice] add CRYO1850-4xCO2 compset [CIME] add GCP, ANL GCE, Spock, Perlmutter, deprecate config_compilers.xml, fix and clean-up cmake macros, fix slurm bindings, refactor CIME internal testing, cleanup SCORPIO perf data, allow position independent compset naming, [also] update v2 benchmarking suite, extend e3sm_prod with throughput and memory checks

E3SM Project, DOE↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗
DOE OSTI2025

Reducing the Cost and Energy of Lithium-ion Battery Manufacturing using High Throughput Atomic Layer Deposition Processes

Forge Nano has recently developed an innovative strategy based on atomic layer deposition(ALD) of oxide coatings on battery separators and electrode materials. ORNL team worked with Forge Nano under this CRADA to construct larger format cells, validate battery and separator performance, and perform advanced materials characterization. It was observed that oxide coatings improve the battery cell performance in terms of both rate capability and long-term cyclic stability. ORNL team performed the research under this CRADA at DOE’s Battery Manufacturing Facility (BMF) at ORNL.

25 ENERGY STORAGE↗
DOE OSTI2022

Trends in Formic Acid Electro-Oxidation on Transition Metals Alloyed with Platinum and Palladium

Direct formic acid fuel cells (DFAFCs) have emerged as a promising power source to meet increased demands for alternative energy sources in the transportation and portable energy storage sectors. Furthermore, these fuel cells utilize formic acid (FA), a nontoxic and carbon-neutral fuel when produced from biomass or via CO 2 reduction. Despite the promise of DFAFCs, the best monometallic catalysts, platinum and palladium, are poisoned by CO through the indirect oxidation pathway and require large overpotentials. By alloying Pt and Pd with other metals, we aim to improve both the activity and selectivity of these catalysts. Here, we present a systematic density functional theory (GGA-PW91) study of FA electro-oxidation (FAO) on the (111) facet of bimetallic Pt (Pt 3 M) or Pd (Pd 3 M) catalysts (M = Au, Ag, Cu, Pt, Pd, Ir, Rh, Ru, or Re) to evaluate the catalytic performance of these surfaces. For each surface, we calculate free energy diagrams and onset potentials of three key reaction mechanisms: direct oxidation of FA via carboxyl (COOH), direct oxidation of FA via formate (HCOO), and the indirect oxidation of FA that first forms CO en route to full oxidation to CO 2 . We then display the trends in the form of phase diagrams that compare the activity of the calculated surfaces against regions of high activity using the free energies of adsorbed CO and OH as descriptors, enabling high-throughput screening and design of improved catalysts, particularly those alloying Pt or Pd with Ir, Ru, or Re.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Reducing the Cost and Energy of Lithium-ion Battery Manufacturing using High Throughput Atomic Layer Deposition Processes

Forge Nano has recently developed an innovative strategy based on atomic layer deposition (ALD) of oxide coatings on battery separators and electrode materials. ORNL team worked with Forge Nano under this CRADA to construct larger format cells, validate battery and separator performance, and perform advanced materials characterization. It was observed that oxide coatings improve the battery cell performance in terms of both rate capability and long-term cyclic stability. ORNL team performed the research under this CRADA at DOE’s Battery Manufacturing Facility (BMF) at ORNL.

25 ENERGY STORAGE↗
DOE OSTI2025

An In Situ , Automated High-Explosives Aging Method Utilizing Two-Dimensional Gas Chromatography–Mass Spectrometry

Understanding chemical changes that occur in high explosives as they age is of great importance to the safe employment and storage of these compounds. Traditional methods of aging high explosives even under accelerated aging conditions are time intensive with durations on the order of months to years. The nature of traditional aging analyses reduces each sample to a snapshot data point often separated widely in time, requiring many assumptions as to how the degradation products develop. Further complicating matters, several analytical techniques are typically employed for each sample analysis in order to ascertain an entire picture of the decomposition pathways. To address these shortcomings with existing methods, a new method of accelerated aging of high explosives utilizing comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry (GC × GC-HRMS) was developed using 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) as a model compound for method development. This in situ automated method reduces the time scale of aging to a matter of hours using the inlet of the GC × GC as the aging vessel. GC × GC in combination with HRMS allowed for the collection of both evolved gases and other decomposition products produced during the entire aging process in real time with HRMS providing far greater certainty in identification of explosives aging products. Additionally, this method allowed for a higher throughput of samples with greatly simplified sample preparation. Chemometric analysis of the GC × GC-HRMS data set via the alteration analysis (ALA) enabled discovery of statistically significant chemical changes providing insight into the variation of decomposition pathways with varying aging temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Toward a persistent event-streaming system for high-performance computing applications

High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.

HPC↗
DOE OSTI2022

Selective Ion Enrichment and Charge Storage through Transport Hysteresis in Conical Nanopipettes

Greater selectivity and controls in the ion transport dynamics are essential in fields such as charge storage, separation, energy storage and conversion, neuromorphic computing and learning, electrochemistry, to name a few. Mechanistic insights into the intriguing hysteresis effects in the rectified electrokinetic transport through single conical nanopipettes are unveiled by combining time-resolved electroanalytical experiments with numeric simulation. Cations as counterions for surface charges are found to dominate not just the through-nanopore flux but also the hysteresis charges, that is, the net enriched or expelled charges during the transport process. Built on our earlier report on the through-nanopore ion flux dominated by counterions for surface charges, the “trapped ions” or hysteresis charges are analyzed herein. Cation selectivity is almost 100% in the hysteresis charges during the potential scans in low conductivity states driven by the combined applied and intrinsic surface electrical fields. Surprisingly, the cation selectivity in the total hysteresis charges remains high at 70–80% over a wide bulk concentration range in the high conductivity (HC) states, where higher ionic strength due to ion enrichment would decrease electrostatistic effects and thus ion selectivity. The retained high selectivity at HC is explained by the competition effects of electroosmotic flow against the co-ion migration. Furthermore, the respective cation and anion portions in the total hysteresis charges over a wide range of ionic strength and measurement conditions provide generalizable strategies for improvements in both transport throughput and selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

4D imaging of lithium-batteries using correlative neutron and X-ray tomography with a virtual unrolling technique

The temporally and spatially resolved tracking of lithium intercalation and electrode degradation processes are crucial for detecting and understanding performance losses during the operation of lithium-batteries. Here, high-throughput X-ray computed tomography has enabled the identification of mechanical degradation processes in a commercial Li/MnO 2 primary battery and the indirect tracking of lithium diffusion; furthermore, complementary neutron computed tomography has identified the direct lithium diffusion process and the electrode wetting by the electrolyte. Virtual electrode unrolling techniques provide a deeper view inside the electrode layers and are used to detect minor fluctuations which are difficult to observe using conventional three dimensional rendering tools. Moreover, the 'unrolling' provides a platform for correlating multi-modal image data which is expected to find wider application in battery science and engineering to study diverse effects e.g. electrode degradation or lithium diffusion blocking during battery cycling.

25 ENERGY STORAGE↗