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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Relationship of Chemical Composition and Moisture Sensitivity in LiNi x Mn y Co 1-x-y O 2 for Lithium-Ion Batteries

Chemical composition–moisture sensitivity relationship of LiNi x Mn y Co 1-x-y O 2 (NMC) cathode materials was investigated by exploring crystal structures, surface properties, and electrochemical performance behaviors of various commercial NMC powders: LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC111), LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532), LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622), and LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811). The NMC powders were stored in different moisture conditions: moisture-free, humidified air, or immersed in water. Rietveld refinement analysis of X-ray diffraction (XRD) data and scanning electron microscopy (SEM) were used to characterize the crystal structure changes and the evolution of particle surfaces morphologies. The effect of moisture contamination on the electrochemical properties of NMC cathodes was studied by galvanostatic cycling and electrochemical impedance spectroscopy (EIS). The moisture contamination resulted in either structural disorder or unwanted surficial deposition products, which increased a charge-transfer impedance and consequent performance degradation of battery cells. The results showed that NMC’s moisture vulnerability increased with Ni content (x) despite protective coatings on commercial particles, which stressed the necessity of alternative surface passivation strategies of Ni-rich NMC for broad applications such as electric vehicles and electrified aircraft propulsion.

25 ENERGY STORAGE↗
DOE OSTI2025

Development and transferability of neural-network models for plasma-surface interactions

Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

Ab-initio molecular dynamics↗
DOE OSTI2020

Remote heteroepitaxy of GaN microrod heterostructures for deformable light-emitting diodes and wafer recycle

There have been rapidly increasing demands for flexible lighting apparatus, and micrometer-scale light-emitting diodes (LEDs) are regarded as one of the promising lighting sources for deformable device applications. Herein, we demonstrate a method of creating a deformable LED, based on remote heteroepitaxy of GaN microrod (MR) p - n junction arrays on c -Al 2 O 3 wafer across graphene. The use of graphene allows the transfer of MR LED arrays onto a copper plate, and spatially separate MR arrays offer ideal device geometry suitable for deformable LED in various shapes without serious device performance degradation. Moreover, remote heteroepitaxy also allows the wafer to be reused, allowing reproducible production of MR LEDs using a single substrate without noticeable device degradation. The remote heteroepitaxial relation is determined by high-resolution scanning transmission electron microscopy, and the density functional theory simulations clarify how the remote heteroepitaxy is made possible through graphene.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-voltage water-scarce hydrogel electrolytes enable mechanically safe stretchable Li-ion batteries

Soft Li-ion batteries, based on conventional organic electrolytes, face performance degradation challenges due to moisture penetration and safety concerns due to possible leakage of toxic fluorine compounds and flammable solvents under mechanical damage. We design a water-scarce hydrogel electrolyte with fluorine-free lithium salt to achieve wide electrochemical stability window (up to 3.11 volts) in ambient air without hermetic packaging while balancing high stretchability (1348%), ion conductivity (41 millisiemens per centimeter), and self-healing capabilities for mechanically and chemically safe stretchable Li-ion batteries. Molecular synergy between hydrophilicity and lithiophilicity of zwitterionic polymer backbone is revealed by molecular dynamics simulations. The battery exhibits capacity retention under harsh mechanical stresses—enduring stretching, twisting, folding, and multiple through-punctures by a needle—while self-healing from repeated through cuts by a razor blade. Stable ambient operation for 1 month over 500 charge-discharge cycles (average coulomb efficiency, 95%) is achieved. A prototype self-healing electronic system with embedded soft batteries demonstrates practical application as a durable embodied energy source.

Science & Technology - Other Topics↗
DOE OSTI2025

SlimIO: Lightweight I/O Path Design for Write Isolation in FDP-backed In-Memory Databases

In-Memory Databases (IMDBs) are widely used with HPC applications to manage transient data, often using snapshot-based persistence for backups. Redis, a representative IMDB, employs both snapshot and Write-Ahead Log (WAL) mechanisms, storing data on persistent devices via the traditional kernel I/O path. This method incurs syscall overhead, I/O contention between processes, and SSD garbage collection (GC) delays. To address these issues, we propose SlimIO, which adopts I/O passthru to minimize syscall overhead and inter-process I/O interference. Additionally, it leverages Flexible Data Placement (FDP) SSDs as backup storage to avoid performance degradation from SSD GC. Experimental results show that SlimIO reduces snapshot time by up to 25%, increases query throughput by up to 30% during non-snapshot periods, and lowers 99.9%-ile latency by up to 50%. Furthermore, it achieves a write amplification factor (WAF) of 1.00, indicating no redundant internal writes, thus extending SSD lifespan.

Lee, Sangyun [Sogang University]↗
DOE OSTI2020

Rate-Dependent Aging Resulting from Fast Charging of Li-Ion Cells

The mass electrification of personal and service vehicles is reliant on the ability of battery packs to undergo extreme fast recharging. A central challenge is that the repeated exposure to high currents can trigger and/or accelerate mechanisms of performance degradation, such as lithium deposition, mechanical damage of active material particles, and impedance rise. Here, we investigate whether constraining high-rate charging to limited state-of-charge (SOC) ranges can mitigate these aging processes. Our experiments map the boundaries of lithium plating conditions, and also indicate that permanent losses of electrode capacity appear to be a function of cycle number, but not charging rate. Interestingly, we show that impedance rise appears to depend on charging rate alone, and not on the amount of charge passed at each cycle. This observation suggests that limiting fast charging to a narrow SOC range would only delay cell aging, but not fully prevent it from happening. We identify oxide-particle cracking as the likely mechanism for this impedance rise, suggesting that particle and electrode design are essential to enable fast charging. The findings of this work can inform the development of fast-charging protocols that are less damaging to cell health.

25 ENERGY STORAGE↗
DOE OSTI2022

Quantifying the Impact of Charge Rate and Number of Cycles on Structural Degeneration of Li-Ion Battery Electrodes

A quantitative link between crack evolution in lithium-ion positive electrodes and the degrading performance on cells is not yet well established nor is any single technique capable of doing so widely available. Here, we demonstrate a widely accessible high-throughput approach to quantifying crack evolution within electrodes. The approach applies super-resolution scanning electron microscopy (SEM) imaging of cross-sectioned NMC532 electrodes, followed by segmentation and quantification of crack features. Crack properties such as crack intensity, crack width and length are quantified as a function of charge rate (1C, 6C, and 9C) and cycle number (25, 225, and 600 cycles). Hundreds of particles are characterized for statistical confidence in the quantitative crack measurements. The data on crack evolution is compared to electrochemical data from full cells and half cells with the NMC532 positive electrodes. We show that while crack evolution strongly correlates with capacity fade in the first 25 cycles, it does not correlate well for the following hundreds of cycles indicating that cracking may not be the dominant cause of capacity fade throughout the cycle-life of cells.

25 ENERGY STORAGE↗
DOE OSTI2023

Techno-Economic Analysis of Reversible and Paired Solid Oxide Cell Systems for Hydrogen Production

This is the Journal of the Electrochemical Society manuscript that details an NETL analysis on comparing the performance and cost of reversible solid oxide cell (SOC) units versus paired fuel cell (SOFC) and electrolysis cell (SOEC) units. The analysis compares tradeoffs such as capital costs versus rate of performance degradation, sensitivity to natural gas prices, sensitivity to imported power costs, and other relevant analyses. The systems include a high rate (98%) of carbon capture. The analysis shows that given a certain cost of electricity input, either approach could be viable in the market and points to the need to incorporate into a full system to understand impacts.

Noring, Alex↗
DOE OSTI2025

Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach

Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.

connectivity maintenance↗
DOE OSTI2025

A high-temperature Rutherford Backscattering Spectrometry apparatus for in situ material characterization

A new methodology for high-temperature Rutherford Backscattering Spectrometry (HT-RBS) has been developed to enable in situ material characterization at elevated temperatures. A 3.5 MeV proton beam penetrates a 10-µm-thick 316L stainless steel foil mounted on a graphite substrate, with backscattered signals detected using an HT-RBS system. Conventional semiconductor detectors, primarily based on silicon, suffer significant performance degradation at temperatures higher than ~ 60 °C due to increased leakage current and noise, leading to signal distortion and failure. Here, to preserve spectral quality, a 5 µm aluminum foil shields the detector from thermal radiation, allowing reliable operation up to 900 °C at the target. A rotatable shutter provides additional thermal isolation during data collection pauses. In situ measurements of areal density changes of 316L stainless steel were conducted to validate the technique, revealing consistency with the known thermal expansion coefficient. The method facilitates seamless switching between irradiation and analysis, enabling continuous studies. This approach supports in situ investigations of diffusion, void swelling, creep, and corrosion, offering a versatile tool for advanced materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Catalyst Deactivation Modes of PdO/γ-Al 2 O 3 Catalysts for Lean Methane Oxidation

PdO/γ-Al 2 O 3 catalysts are one of the most active catalytic components for the complete oxidation of methane. Under reaction conditions, especially in a wet feed, the catalysts suffer severe performance degradation. This study establishes a series of testing protocols to systematically investigate the causes of catalyst deactivation under methane oxidation reaction conditions. Four distinct catalyst deactivation modes are identified. Two of the deactivation modes are directly related to H 2 O, either from the feed gas or as a part of the reaction products, with one (Mode 2) being attributed to the formation of surface hydroxyl groups and the other (Mode 3) to the competitive adsorption of H 2 O on the catalysts. The impact of the two deactivation modes is acute and severe but reversible. In contrast, the other two deactivation modes are gradual and persistent but irreversible. Both modes are induced by CH 4 oxidation reaction, with the impact of a wet feed (Mode 4) being substantially more severe than that of a dry feed (Mode 1). The major cause of the irreversible catalyst deactivation is attributed to surface reconstruction of PdO nanoparticles, which behaves as a passivation layer lowering the number of coordinately unsaturated Pd sites for CH 4 activation. Although the passivation layer is relatively stable against thermal or hydrothermal treatment, it is not completely inert. Formation and partial regeneration of the passivation layer is a highly dynamic process and heavily depends on the reaction temperature: a lower reaction temperature (≤ 450 ℃) can lead to quicker catalyst deactivation; but a higher reaction temperature (between 500 – 550 ℃) can result in a greater extent of catalyst deactivation.

PdO/γ-Al2O3↗
DOE OSTI2023

Comparing the Effectiveness of Low Temperature Bake in EP and BCP Cavities

Electropolishing (EP) and buffered chemical polishing (BCP) are conventional surface preparation techniques for superconducting radiofrequency (SRF) cavities. Both EP and BCP treated SRF cavities display high field Q-slope (HFQS) which degrades performance at high gradients. While high gradient performance in EP cavities can be improved by introducing oxygen via a low temperature bake (LTB) of 120^{°}C by 48 hours, LTB does not consistently remove HFQS in BCP cavities. There is no consensus as to why LTB is not effective on BCP prepared cavities. We examine quench in EP, BCP, EP+LTB, and BCP+LTB treated 1.3 GHz single-cell Nb cavities by studying the heating behavior with field using a temperature mapping system. Cavity performance is correlated to characterizations of surface impurity profile obtained via time of flight secondary ion mass spectrometry studies. We observe a difference in near surface hydrogen concentration following BCP compared to EP that may suggest that the causes of quench in EP and BCP cavities are different.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Instrumented Photovoltaic Modules for Environmental Characterization and Simulation Model Validation

Photovoltaic modules are subjected to various mechanical stressors in their deployment environments, ranging from installation handling to wind and snow loads. Damage incurred during these mechanical events has the potential to initiate subsequent degradation mechanisms, reducing useful module lifespan. Thus, characterizing the mechanical state of photovoltaic modules is pertinent to the development of reliable packaging designs. In this work, photovoltaic modules with strain gauges directly incorporated into the module laminate were fabricated and subjected to mechanical loading to characterize internal strains within the module when under load. These experimental measurements were then compared against results obtained by high-fidelity finite-element simulations. The simulation results showed reasonable agreement in the strain values over time; however, there were large discrepancies in the magnitudes of these strains. Both the instrumentation technique and the finite-element simulations have areas where they can improve. These areas of improvement have been documented. Despite the observed discrepancies between the experimental and simulated results, the module instrumentation proved to be a useful gauge in monitoring and characterizing the mechanical state. With some process improvements, this method could potentially be applied to other environments that a photovoltaic module will encounter in its lifetime that are known to cause damage and degrade performance.

14 SOLAR ENERGY↗
DOE OSTI2023

An Active and Resilient Bilayer Oxygen Electrode for Intermediate Temperature Reversible Solid Oxide Cells

High temperature solid oxide cells (SOCs) have intrinsic advantage in efficiency over conventional internal combustion engines for power generation and low-temperature electrolysis cells for H2 production. This efficiency advantage could potentially lead to cost saving and emission reduction. However, commercialization of the current SOCs technology is hindered by its poor durability. One major component with the durability issue is oxygen electrode (OE). For example, the durability of OEs can be negatively affected by the gaseous Cr-species originated from the air oxidation of high-temperature alloy interconnect. For electrolysis operation, on the other hand, delamination of OE from electrolyte, particularly at high current densities, has been identified as a major cause for the performance degradation. Therefore, developing robust and active OEs is of critical importance to SOC technology. WE have previously demonstrated a new bilayer OE with strong activity and stability. Here in this presentation, we present recent results of electrochemical characterization on bilayer OE operating under both fuel cell and electrolysis modes. We will first show the optimization of bilayer OE in terms of thickness, morphology, and calcination temperature and their impacts on cell area specific resistance (ASR). With three-electrode symmetric cell configuration, we also show the results of charge transfer ASR of bilayer OE as a function of current density, temperature, and time in different atmospheres. The results are further compared with the baseline OE (LSCF+GDC) to demonstrate the advantage of bilayer OE.

Huang, Kevin↗
DOE OSTI2023

Centrifugal Pump Model for System Codes for Advanced NPP Designs

In this document, a homologous model and one-dimensional line model for centrifugal pump are described to provide the theoretical background of a pump model to be developed in system-level codes. Input and output parameters are proposed, along with a summary of the governing equations for each model. Furthermore, hydraulic performance degradation of centrifugal pump is also modeled in the homologous pump model. This document suggests that system codes provide a homologous and one-dimensional line model for centrifugal pumps, allowing the users to choose the model that best fits their purposes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Next Generation Solid Oxide Fuel Cell Module Development

The overall objective of this project was to develop a transformative Solid Oxide Fuel Cell (SOFC) building block configuration comprised of multi-stack arrays that can be utilized in large-scale power plants. This transformative design signified the benefits of lower performance degradation coupled with improved reliability, low cost, smaller packaging for easier transport and installation, and improved maintenance and field serviceability characteristics. The project goal was to design and fabricate a scalable hot module for housing an array of SOFC stacks and to demonstrate the characteristics of the module gas distribution, insulation and instrumentation, and DC power take-off. The approach was to validate the design of a stack prototype scalable to megawatt (MW) class systems using FuelCell Energy’s Compact SOFC Architecture (CSA) stacks. The scope of work was intended to design, build, and test a compact and low-cost multi-stack sub-module with flexibility to house CSA stacks and scalable to 350 kW which could ultimately be deployed in construction of MW-class systems.

20 FOSSIL-FUELED POWER PLANTS↗