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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Conformal high-entropy oxide coatings enable fast and durable surface oxygen reactions

Developing active and durable air electrodes for efficient oxygen reactions is challenging for protonic ceramic cells (PCCs), especially at temperatures below 550°C. Here, in this study, we report a rationally designed conformal coating with a high-entropy PrNi 0.2 Mn 0.2 Co 0.2 Fe 0.2 Cu 0.2 O 3−δ (PNMCFC) perovskite structure on the surface of a state-of-the-art PrBaCo 2 O 5+δ (PBC) air electrode. The formed hybrid air electrode (PNMCFC-PBC) shows faster surface oxygen kinetics and a more stable phase structure in high-humidity air than the bare PBC electrode. Further density functional theory calculations suggest that the conformal coating mitigates Ba segregation at the interface and improves oxygen-related reactions, enhancing overall stability and electrocatalytic performance. The cells with the developed hybrid electrodes show encouraging electrochemical performance at 550°C: a polarization resistance of 0.72 Ω cm 2 , a peak power density of 1.30 W cm −2 , an electrolysis current density of −1.36 A cm −2 at 1.3 V, and reasonable operating stabilities (∼200 h at 550°C).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Electrochemical Reactions Under Reverse Bias Create Additional Mobile Ions That Enable Hole Tunneling in Metal Halide Perovskite Diodes

Gradual reverse-bias breakdown in metal-halide perovskite diodes and solar cells is thought to originate from hole tunneling through steep bands in an ionic depletion region near the electron-transport layer after positively charged iodine vacancies accumulate near the hole-transport layer (HTL). However, typical reported mobile-ion concentrations near 1 x 10^17 cm-3 are too small to quantitatively explain significant tunneling-current densities and (Zener) breakdown observed near -5 V. Here, we show that inferred mobile-ion concentrations increase by more than 100x, to over 1 x 10^18cm-3 , within just 3 min of reverse bias at -6.0 V in p-i-n perovskite diodes. We attribute this increase to iodide oxidation and coupled iodine vacancy creation that must be balanced by reduction reactions near the HTL. Sub-optimal HTL coverage leads to direct contact between the transparent conducting electrode and perovskite, facilitates reduction events, enables the creation of even larger inferred mobile-ion concentrations (~1 x 10^19cm-3 ), and leads to faster degradation under reverse bias. This explains previous work that showed increased breakdown voltages and improved reverse-bias stability by implementing thick, uniform HTLs.

14 SOLAR ENERGY↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Exploring thermal runaway propagation in Li-ion batteries through high-speed X-ray imaging and thermal analysis: Impact of cell chemistry and electrical connections

Battery safety design is important to consider from the individual Li-ion cell to the level of the macro-system. On the macro-level, failure in one single cell can lead to propagation of the thermal runaway and rapidly set a whole battery pack on fire. Factors that can impact the propagation outcome, such as cell model/chemistry and electrical connection are here investigated using a combination of measurements. Several abusive tests were conducted, combining two different cell models (Molicel P42A and LG M50, both 21700s) in series and parallel connections (16 tests per configuration). Overall, a propagation outcome of 56% was measured from the 32 conducted tests, a minimum temperature of 150 °C was required to initiate propagation, and the fastest propagation occurred in 123 s. Temperature measurements were higher in series connected cells, initiating the discussion of cell chemistry and internal resistance on this effect. The difference in current-flow during thermal runaway in series and parallel connections, and how this can affect the temperature evolution is further discussed. Spatio-temporal mapping of X-ray radiography allowed us to derive the speed of thermal runaway evolution inside the battery and has shown that series connected cells, in particular P42A, occur faster. It was further observed that deviant sidewall behaviors such as temperature-induced breaches and pressure-induced ruptures occurred in P42As only respective nail-penetrated cells only.

25 ENERGY STORAGE↗
DOE OSTI2024

Identifying electrochemical processes by distribution of relaxation times in proton exchange membrane electrolyzers

Distribution of relaxation time (DRT) is used to interpret electrochemical impedance spectroscopy (EIS) for proton exchange membrane (PEM) water electrolyzers, with an attempt to separate overlapped relaxation processes in Nyquist plots. By varying operating conditions and catalyst loadings, four main relaxation peaks arising from EIS can be identified and successfully separated from low to high frequencies as (P1) mass transport, (P2) oxygen evolution reaction kinetics, (P3) reaction kinetics (with faster time constant than P2), and (P4) ionic transport. Here, the shape, height, and frequency of the DRT peaks change with different membrane electrode assembly (MEA) configurations. Electron microscopy reveals distinct features from the cross-sectioned MEAs which verify critical DRT results in that increasing the iridium (Ir)-anode loading from 0.2 mgIr/cm 2 to 1.5 mgIr/cm 2 reduces kinetic losses due to higher site-access; a thick and compacted anode, however, also triggers higher ohmic resistances from membrane/catalyst layer hydration and increases transport losses due to longer ionomer pathways. DRT provides higher resolution to EIS for deconvoluting processes with different relaxation times and the quantification of DRT peaks improves the accounting of total losses from each process.

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

Superior electrochemical performance and reduced heat generation in 3D printed vs. 2D tape-casted NMC622 electrodes

This study compares the charge storage mechanisms, thermodynamics behavior, ion transport, and heat generation in NMC622 electrodes fabricated using a novel 3D printing process and the conventional 2D tape casting process. First, potentiometric entropy measurements revealed that the charge storage mechanisms for both types of electrodes consisted of lithium deintercalation in a homogeneous solid solution of NMC622 followed by a transition from a hexagonal (H1) phase to another hexagonal (H2) phase through a monoclinic (M) phase. Both types of electrodes had similar thermodynamics behavior with overlapping entropic potential profiles. Furthermore, operando isothermal calorimetry at high C-rates indicated that the 3D printed electrodes featured larger specific capacity and better rate performance than the 2D tape-casted electrodes. The better performance of 3D printed electrodes was attributed to their larger electrode/electrolyte interfacial surface area and electrical conductivity as well as their faster lithium ion transport. As a result, the instantaneous heat generation rates were smaller in 3D printed electrodes than in 2D tape-casted electrodes, thus resulting in lower overall specific electrical energy and thermal energy dissipation per unit charge stored. Overall, additive manufacturing techniques offer great potential in producing electrodes with superior electrochemical performance and reduced heat generation for fast charging batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Emulation of radiation transport in 3D stochastic media using 1D planar Monte Carlo stochastic media radiation transport algorithms

A subset of stochastic media radiation transport problems involves those in which radiation is incident on a thin slab of stochastic material. Particle tracking in 3D for such problems is expensive, and 1D planar models lack accuracy because they only allow the material to change in one dimension. Therefore, we propose dimensional emulation, which through a slight modification allows existing 1D planar geometry stochastic media radiation transport models to reproduce results from the equivalent 3D models by allowing the material to change in all three dimensions, reproducing the fidelity of the 3D model for the low computational cost of the 1D planar model. In this work, we apply dimensional emulation to three Monte Carlo stochastic media radiation transport models: Chord Length Sampling (CLS), the Local Realization Preserving method (LRP), and a variant of Conditional Point Sampling (CoPS). For a common Markovian benchmark set, the 3D emulation variants of these algorithms are numerically verified to reproduce the results of the 3D variants within statistics while running 1.3 to 2 times faster in the implementation within Sandia National Laboratories open-source research code PlaybookMC. The 3D emulation variants are also shown to yield a 72%–92% reduction in error for the thin slab problems in comparison to the 1D benchmark. As a result, the 3D emulation variant of CLS and CoPS-1 are shown to reproduce 3D CLS results that were used to approximate results for a 3D spherical inclusion geometry benchmark set.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Freestream turbulence effects on unsteady wind turbine loads and wakes: An IDDES study

We investigate numerically the effects of freestream turbulence on the unsteady aerodynamics and wakes of the National Renewable Energy Laboratory Phase VI wind turbine rotor for increasing wind speed. Turbulence is modeled using the Improved Delayed Detached-Eddy Simulation (IDDES) method. As a first step, a detailed mesh resolution study is conducted with the decaying freestream turbulence model at turbulence intensity of 0.5%. Our blade-resolved IDDES simulations show that grid-independent average torque and thrust results can be achieved with relatively coarse meshes, whereas dramatically higher mesh resolution is required for grid-independent results for power spectral densities of thrust force, especially in the deep-stall regime. Comparing the loads with the Shear-Stress Transport model demonstrates the superiority of IDDES in predicting massively separated flows. The aerodynamic performance and wake predictions with the decaying freestream turbulence model are compared with the synthetic freestream turbulence model. Both models predict nearly the same loads, spectral energy content, and wake characteristics. The properties of both the near- and far-wake regions are then examined. Furthermore, we show that separated boundary layers accelerate turbulent mixing and entrainment of the external flow, which results in faster wake recovery. The effect of increasing turbulence intensity to 6% is investigated using the synthetic freestream turbulence model. In contrast with the fully attached boundary layer, higher freestream turbulence in deep stall does not significantly affect the loads and vortex-shedding characteristics. However, the turbulent mixing in the wake is enhanced, which further hastens the recovery of the self-similar velocity profile. In general, increasing the wind speed at high turbulence intensity shifts the recovery farther upstream and increases the wake width.

17 WIND ENERGY↗
DOE OSTI2025

Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes

Solid-state electrolytes, such as lithium lanthanum zirconium oxide (LLZO), show promise as technologies for next-generation high-energy-density batteries, but commercial development has been hindered by a lack of scalable processing methods. Current fabrication methods are costly or require long annealing steps to create dense films. We report an atmospheric pressure blown-arc nitrogen plasma jet process to rapidly form sub-micrometer-thick, dense amorphous LLZO (a-LLZO) films from sol-gel precursors. Films are processed in less than 2 min, an order of magnitude faster than what has previously been reported. We demonstrate 500-nm-thick a-LLZO films processed at 350°C with an ionic conductivity of 2 × 10 −6 S/cm at 30°C and 2 × 10 −3 S/cm at 100°C and a conductance of 19 S at 100°C, the highest conductance of any LLZO phase to date. Here, the films exhibit outstanding smooth surface morphology with low defectivity, advancing atmospheric plasma processing as a scalable processing method for solid-state electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2024

Cellulose acetate membranes exhibit exceptional monovalent to divalent cation selectivities

Salt transport properties of cellulose acetate membranes are reported for a series of chloride salts with mono- valent and divalent cations (LiCl, NaCl, MgCl 2 , CaCl 2 ). Measurements include salt permeability and sorption, with diffusivity values calculated from the permeability and sorption results. We report an exceptionally high LiCl/MgCl 2 selectivity of 750:1. Salts with similar valence (LiCl and NaCl; MgCl 2 and CaCl 2 ) have similar transport properties. The high monovalent/divalent selectivity arises from differences in both sorption and diffusion, with a LiCl/MgCl 2 solubility selectivity of about 11 and a diffusivity selectivity of about 70. Atomistic molecular dynamics simulations show that ions tend to reside in isolated clusters of water. Increasing ion charge strengthens ion–water interactions relative to ion–polymer interactions, explaining the reduced sorption of divalent ions. Diffusion of ions through the membrane occurs via hop-like motion between water clusters. Lithium diffuses faster than magnesium due to weaker ion–water coordination for lithium, which allows for greater mobility within water clusters and more frequent hopping. Altogether, our atomistic simulations suggest that the high LiCl/MgCl 2 selectivity is linked to cellulose acetate’s high water/salt selectivity and is a consequence of low water content and relatively uniform water distribution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Supported ionic liquid membranes (SILMs) with exceptional selectivity and permeability for dilute CO 2 separations

Supported ionic liquid membranes (SILMs), containing phosphonium ionic liquids with aprotic N-heterocyclic anions (AHA ILs) in an inert inorganic support, were tested under both dry and humidified (40 % RH) mixed-gas conditions down to 420 ppm CO 2 in N 2 at 35 °C. In the dry case, the best performing IL, triethyl(octyl)phosphonium 4-bromopyrazolide ([P 2228 ][4-BrPyra]) exhibited mixed-gas CO 2 permeabilities and CO 2 /N 2 permeability selectivities as high as 26,800 barrer and 7,000, respectively. In the presence of humidity, the CO 2 permeability and CO 2 /N 2 selectivity increased to 49,100 barrer and 13,200, respectively, and these are the highest reported combination in the literature. Humidity amplifies CO 2 permeabilities and CO 2 /N 2 permeability selectivities through increases in CO 2 capacity due to bicarbonate formation and through faster mobility of the mobile carrier from decreased viscosity. Furthermore, N 2 permeability stayed roughly invariant in the presence of humidity, likely from competing effects of viscosity reduction and lower N 2 solubility.

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

Recrystallization driven softening and heating rate dependencies of FeCrAl nuclear fuel cladding during accident transients

A refined understanding of FeCrAl cladding behavior during rapid transients is critical for its potential deployment in light-water reactors. Current assessments focus on transient burst testing metrics such as balloon geometry, burst temperature, and hoop stress, often used as proxies for simpler conventional tensile properties. However, directly correlating isothermal tensile and creep data with accident transient scenarios remains a challenge, although it is essential for high fidelity model development. Recent modeling based on tensile tests up to 800 °C, conducted with both immediate loading and a 10-minute soak, showed that immediate loading better predicts experimental burst temperatures, indicating a thermal softening effect. Building upon this observation, the current study connects transient performance, microstructural evolution, and high-temperature tensile properties by leveraging results from C26M claddings burst tests performed at heating rates of 1–50 °C/s and hoop stresses from 25 to 100 MPa. At 25 MPa, rupture temperatures varied by only 6 °C, but at 100 MPa, the difference reached 116 °C, with faster heating yielding higher burst temperatures. Microstructural analysis identified recrystallization as the primary cause of heating rate-dependent softening, eliminating prior cold-working. In-situ thermomechanical data linked ballooning onset to localized instabilities, similar to ultimate tensile strength behavior in conventional tensile tests. High heating rates correlated with immediate loading tensile data, while lower rates matched soaked data. Furthermore, by linking burst performance to microstructural evolution and tensile properties, this work provides a foundation for more accurate modeling of FeCrAl claddings and potentially other Fe-based materials under accident conditions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Post-build stress-relief optimization for laser powder bed fusion 316H stainless steel

Nuclear energy remains a critical component of a diversified and efficient energy portfolio, offering reliable, high-capacity, and low-carbon power. However, in the U.S., aging infrastructure and the slow qualification and deployment of advanced materials and manufacturing techniques hinder progress in next-generation reactor technologies. This study explores the application of laser powder bed fusion (LPBF) additive manufacturing for stainless steel 316H, with a focus on optimizing post-build heat treatments to enhance material properties for high-temperature nuclear applications. The research targets the optimization of stress-relief temperatures to alleviate postbuild residual stresses, ensuring improvements in the microstructural corelated properties. A series of microstructural and mechanical evaluations were performed on LPBF-printed SS-316H samples which were subjected to annealing at temperatures varying between 650 °C and 850 °C. X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses revealed that increasing the heattreatment temperature accelerated dislocation recovery. Vickers microhardness measurements showed an initial reduction in values, followed by stabilization over extended durations at all the temperatures. While higher temperatures facilitated faster recovery, they also promoted carbide precipitation along grain and solidification cell boundaries, narrowing the safe processing window. In contrast, heat treatment at 650°C preserved the cellular substructure and enabled controlled carbide precipitation over time. In conclusion, these findings highlight the importance of time–temperature optimization and suggest that 650°C for up to 2 h provides the most favorable balance between recovery and carbide control for a stress-relief treatment.

316 stainless steel↗
DOE OSTI2024

Heteroatom anchoring to enhance electrochemical reversibility for high-voltage P2-type oxide cathodes of sodium-ion batteries

P2-type cathode has received extensive attention due to its faster Na+ diffusion and a high theoretical capacity in sodium-ion batteries (SIBs). However, undesirable phase transformations have induced dramatic capacity decay of SIBs during the cycling process. In this study, heteroatom anchoring through Cu/Mg dual doping is introduced into P2-type Na 0.67 Ni 0.33 Mn 0.67 O 2 cathode to enhance high-voltage electrochemical reversibility and modulate interfacial Na + kinetics. Further, the as-prepared Na 0.67 Ni 0.23 Mg 0.05 Cu 0.05 Mn 0.67 O 2 exhibits an outstanding capacity retention (83.4% after 2000 cycles at 10C) and rate performance (73 mAh g -1 at 10C, accounting for 58.7% of that at 0.1 C) over the voltage range of 2.5–4.4 V. Intensive explorations further manifest that the modified mechanism of dual-ion doping strategy is attributed to the synergistic coupling effect of a substantial change in Na occupancy distribution and an increase in oxygen vacancy buffer. Thus, the optimized cathode expedites Na + diffusion and reduces detrimental phase transformation, which favors high-rate performance and long-term cycling stability. This study develops a route to rationally design high-voltage cathode materials for SIBs.

25 ENERGY STORAGE↗
DOE OSTI2025

The temporal onset of associations of cortical proteins with cognitive resilience vary during late life

Background: Cortical proteins associated with cognitive resilience have been identified but their temporal onset in older adults is unknown. We present a multistage approach to first identify cortical proteins associated with cognitive resilience and then examine their associated temporal onset. Methods: We used data from a subset of 1088 decedents from two cohort-studies who had selected reaction monitoring proteomics from the dorsolateral prefrontal cortex, and at least 3 cognitive assessments. Cognition was assessed using a composite derived from 19 tests. We first used linear mixed-effects models to identify cortical proteins associated with cognitive resilience. We then used functional mixed-effects models to examine non-linear associations between proteins and cognitive resilience to identify their temporal onset. Results: Mean age at death was 90 years (SD = 6.4); 69 % were female. On average, cognition started to decline at around 15 years before death, with accelerated decline in the last 7 years. We identified 40 proteins associated with cognitive resilience, of which 17 proteins also showed non-linear associations. Non-linear associations indicated that higher levels of 10 proteins were associated with slower cognitive decline between 23 and 4 years before death. In contrast, higher levels of 7 proteins were associated with faster decline only within the last 7 years before death. Conclusions: Cognitive resilience proteins are differentially related to late-life cognitive aging; the onset of proteins that maintain cognition may begin many years before the onset of proteins that hasten cognitive decline. The temporal onset of cognitive resilience proteins may be crucial for timing efficacious interventions.

Zammit, Andrea↗
DOE OSTI2024

Vascular endothelial growth factor receptor-1 (FLT1) interactions with amyloid-beta in Alzheimer’s disease: A putative biomarker of amyloid-induced vascular damage

We have identified FLT1 as a protein that changes during Alzheimer's disease (AD) whereby higher brain protein levels are associated with more amyloid, more tau, and faster longitudinal cognitive decline. Given FLT1's role in angiogenesis and immune activation, we hypothesized that FLT1 is upregulated in response to amyloid pathology, driving a vascular-immune cascade resulting in neurodegeneration and cognitive decline. We sought to determine (1) if in vivo FLT1 levels (CSF and plasma) associate with biomarkers of AD neuropathology or differ between diagnostic staging in an aged cohort enriched for early disease, and (2) whether FLT1 expression interacts with amyloid on downstream outcomes, such as phosphorylated tau levels and cognitive performance. Additionally, we sought to replicate FLT1 interactions in the brain. The results showed that higher levels of FLT1 in CSF and post-mortem brain tissue related to increased tau, particularly among amyloid positive individuals. These analyses help clarify the potential utility of FLT1 as a biomarker among individuals with evidence of brain amyloidosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A machine learning framework for accurate and robust analysis of radiation detector pulses

The microscopic properties of atomic nuclei are used to study various scientific questions. They are essential for understanding the fundamental forces of nature and the chemical evolution of the universe. Detecting decay radiation from radioactive nuclei makes it possible to probe these fundamental nuclear properties. Detector waveform traces may contain additional information about the radiation. Generally, advanced signal processing techniques are needed to extract this additional information, often involving fitting the waveform with model response functions using non-linear least-squares optimization with second-order gradient methods. While this is a powerful technique, it is also computationally expensive, leading to slow processing time, which scales with the volume of data. To address this problem, we have developed a machine learning (ML) approach that infers the characteristics of traces from a model detector response function. In particular, we are interested in classifying whether a single recorded trace consists of one or two pulse constituents and estimating the pulse parameters. Furthermore, our proposed ML method can precisely extract the pulses’ parameters, such as energy and timing information, and accurately classify the pulse multiplicity of a trace. Unlike non-learning-based approaches, our ML approach uses neural networks that are significantly faster at inference, as they do not require any optimization during this stage.

Curve fitting↗
DOE OSTI2026

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗