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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Laser ablation of high-loading Li-ion battery electrodes improves accessible capacity and cycle life for Behind-the-Meter Storage

Adoption of Behind-the-Meter Storage (BTMS) requires design of batteries that enable high safety, long cycle life, and low cost at the system level. Pairing Li 4 Ti 5 O 12 (LTO) with LiMn 2 O 4 (LMO) achieves targets related to safety and cycle life, but these materials' low energy densities contribute to higher cost at the system scale. Increasing electrode loading is a simple approach to improve energy density, but comes with a trade-off in electrode utilization due to long, tortuous Li + diffusion pathways. Here, laser ablation is used to microstructure (pattern) high-loading electrodes to enhance electrode performance through improved Li + diffusion pathways. Four cell types, comprising combinations of standard or patterned anode and cathode, were prepared to evaluate the effects of laser ablation at each electrode. A rate test shows that patterning electrodes enhances active material utilization at ≳1C rates. Patterning the cathode yields the most benefit, as cells with a patterned cathode demonstrate a ~20% higher accessible capacity than those without at 1.4C. Additionally, 1C capacity retention of cells with patterned cathode (91% through 3000 cycles) is significantly improved over cells with only the anode patterned (64%) and non-patterned electrodes (50%). Characterization of post-mortem cells before and after refreshing their electrolyte suggests that 1C capacity retention is improved by mitigation of electrode "dry-out". We hypothesize that the microstructure acts as a reservoir of additional electrolyte, or a path for gas to escape, so that active material remains wetted throughout long-term cycling, and/or the microstructure may reduce localized, gas-forming overpotentials in the high-loading electrode.

25 ENERGY STORAGE↗
DOE OSTI2026

Mapping the Microhardness of Al Matrix in U-7Mo/Al Dispersion Fuels at Medium and High Burn-up by in situ Nanoindentation

The mechanical degradation of the Al matrix in U-7Mo dispersion fuels was detected after irradiation in previous studies, but more details of the degradation mechanism are not clear yet to clarify the root cause. In this study, microhardness mapping in fresh and irradiated U-7Mo dispersion fuels coated with ZrN, with burn-up of 3.35 × 10 21 and 6.28 × 10 21 f/cm 3 , are measured to reveal the hardening of the Al matrix as a function of distance to fuel particles. Micro cubes were pulled by plasma focused ion beam scanning electron microscopy and tested by in situ nanoindenter with Berkovitch tip. Size effects are measured in medium and high burn-up samples. The higher burn-up specimen exhibits a more pronounced size effect when indent load is lower than 20 mN. Size effect correction models are also calculated by plots fitting, which predicts the true microhardness without size effect. Results shows that the hardness of the Al matrix near the ZrN coating has the highest value, then decreases moving away from the coating and become stable when the distance reaches ∼10 µm. The hardness starts increasing again getting closer to the next fuel particle, which makes the hardness distribution between two fuel particles to be “U” shape.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Uranium measurements in the field using high-resolution cadmium zinc telluride detectors

A new generation of cadmium zinc telluride (CZT) detectors has become available and is being evaluated by the International Atomic Energy Agency (IAEA) for safeguards verifications in the field. The new CZT detector, model M400, is a room temperature spectrometer manufactured by H3D, Inc. The M400 demonstrates superior energy resolution, effective isotope identification capabilities, and convenient usability features when tested in a controlled laboratory environment. These characteristics define the M400 as a potential platform for IAEA field detection applications, which could become suitable for nuclear material characterization (e.g., enrichment verification) and nuclear safeguards missions. The capabilities of gamma spectrometry codes including Fixed energy, Response function Analysis with Multiple efficiencies (FRAM) from Los Alamos National Laboratory, CZT for Uranium (CZTU) from Lawrence Livermore National Laboratory, and Gamma Detector Response and Analysis Software (GADRAS) from Sandia National Laboratories were adapted for M400 spectra, and the performance of the codes has been validated. This was reported in a prior work. To further validate the performance of the high-energy resolution CZT detector and the isotopic analysis codes, a field measurement campaign consisting of uranium hexafluoride (UF 6 ) cylinder measurements was conducted at a fuel fabrication facility. A total of 34 Type 30B cylinders containing UF6 were measured using three different M400 CZT detectors. Each detector was outfitted with a custom rectangular collimator and shield made out T-Flex®, a tungsten-impregnated polymer. Measurements were performed at three different locations of the cylinder, ensuring that the measurement geometry satisfied the infinite thickness criterion. The spectra from the M400 CZT were analyzed using the code General Enrichment Meter (GEM). For analyzing the gamma-ray spectra from UF 6 cylinder, the GEM code is the appropriate tool since it relies only on the gamma-ray emissions from 235 U and not from other isotopes. Results from the spectral analysis were compared with the known abundance of 235 U in the cylinders, as well as with the International Target Values 2020 (ITV2020). The suitability of the different underlying techniques used by the various codes for UF 6 analysis is discussed. The challenges of measuring UF 6 contained in cylinders and mitigation strategies are highlighted.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Trade-offs at the interface

The durability of perovskite solar cells is closely linked to the mechanical adhesion toughness of their interfaces, though some toughening strategies could trigger detrimental chemical reactions. Here, research now shows that this trade-off can be effectively managed through interfacial engineering, informed by innovative mechanical testing.

14 SOLAR ENERGY↗
DOE OSTI2025

Unlocking soybean meal pectin recalcitrance using a multi-enzyme cocktail approach

Pectin is a complex plant heteropolysaccharide whose structure and function differ depending on its source. In animal feed, breaking down pectin is essential, as its presence increases feed viscosity and reduces nutrient absorption. Soybean meal, a protein-rich poultry feed ingredient, contains significant amounts of pectin, the structure of which remains unclear. Consequently, the enzyme activities required to degrade soybean meal pectin and how they interact are still open questions. In this study, we produced 15 recombinant fungal carbohydrate-active enzymes (CAZymes) identified from fungal secretomes acting on pectin. After observing that these enzymes were not active on soybean meal pectin when used alone, we developed a semi-miniaturized method to evaluate their effect as multi-activity cocktails. We designed and tested 12 enzyme pools, containing up to 15 different CAZymes, using several hydrolysis markers. Thanks to our multiactivity enzymatic approach combined with a Pearson correlation matrix, we identified 10 fungal CAZymes efficient on soybean meal pectin, 9 of which originate from Talaromyces versatilis. Based on enzyme specificity and linkage analysis, we propose a structural model for soybean meal pectin. Our findings underscore the importance of combining CAZymes to improve the degradation of agricultural co-products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Large-Angle Rocking Beam Electron Diffraction of Large Unit Cell Crystals Using Direct Electron Detector

We report a large-angle rocking beam electron diffraction (LARBED) technique for electron diffraction analysis. Diffraction patterns are recorded in a scanning transmission electron microscope (STEM) using a direct electron detector with large dynamical range and fast readout. We use a nanobeam for diffraction and perform the beam double rocking by synchronizing the detector with the STEM scan coils for the recording. Using this approach, large-angle convergent beam electron diffraction (LACBED) patterns of different reflections are obtained simultaneously. By using a nanobeam, instead of a focused beam, the LARBED technique can be applied to beam-sensitive crystals as well as crystals with large unit cells. Here, this paper describes the implementation of LARBED and evaluates the performance using silicon and gadolinium gallium garnet crystals as test samples. We demonstrate that our method provides an effective and robust way for recording LARBED patterns and paves the way for quantitative electron diffraction of large unit cell and beam-sensitive crystals.

4DSTEM↗
DOE OSTI2025

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

As electricity consumption in commercial and residential buildings continues to rise, reducing energy costs presents an increasing challenge. Heating, ventilating, and air-conditioning (HVAC) systems, which typically account for 40%-50% of a building's energy use, are prime targets for energy savings. Intelligent control of HVAC temperature through the exploitation of HVAC load flexibility brings significant potential to reduce energy consumption and electricity expenses. The nonlinear models of HVAC systems challenge traditional control methods, while the uncertainty introduced by HVAC load flexibility complicates distributed energy resource (DER) management using conventional optimal dispatch techniques. In response to these challenges, we propose a hierarchical multi-agent deep reinforcement learning (DRL) approach. The lower-level agents focus on balancing comfort and energy conservation, while the upper-level DRL agents optimize the use of DERs to reduce peak demand based on the control outcomes of the HVAC by the lower-level agents. Here, in the upper-level agents, we incorporate a multi-agent structure based on ensemble learning, which acts based on historical and current data without relying on precise load forecasting to address the delayed rewarding issue in DRL. This allows for the effective reduction of energy costs. The proposed method is tested using a real-world microgrid comprising 413 buildings in Southern California, and the results demonstrate that our approach can significantly reduce overall electricity bills while ensuring the comfort of consumers and residents.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

3D printable silicone compositions exhibiting high toughness and low durometer

3D printable silicones can be designed with varying crosslink density, network structures, and types of reinforcing additives. Within this design space, one may tailor the uncured material’s rheology and mechanical response to access a wide range of potential applications. However, printable, low modulus silicones, particularly those applicable to direct ink write, are underreported in published literature. To address the need for higher performance, low modulus silicones with demonstrated printability, a new set of ca. 20–50 Shore A hardness silicone elastomers exhibiting ca. 7 MPa ultimate tensile strength and ca. 400–1200% elongation at break is presented. Mechanical properties were analyzed, providing insight to the effects of formulation constituents on mechanical properties. Cyclic mechanical testing of the silicone formulations was also performed, and the energy loss and permanent set throughout cycling were determined. In conclusion, printed structures demonstrate the feasibility of these new silicones as durable frameworks for novel soft device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Denoising Seismograms in the Time Domain Using a Deep Learning Model

Deep learning has emerged as a transformative tool for enhancing the extraction of reliable information from seismograms, addressing the increasing demand for precise and efficient seismic data analysis. We introduce an innovative encoder–decoder deep learning model, named WaveDenoiser, designed for noise reduction in the time domain, thereby eliminating the need for spectrogram computations that have been used for existing deep learning tools and significantly improving processing speed. Utilizing the benchmark dataset that is Stanford Earthquake Dataset, we developed three models of varying sizes: base, medium, and large. Notably, the large (referred to as WaveDenoiser) model demonstrated superior performance, achieving a median signal‐to‐noise ratio improvement of 8.8 dB on in‐distribution unseen data (in the same geographic region) and 7.7 dB on out‐distribution unseen data (in a new geographic region), outpacing both the base and medium models. Further evaluation of the WaveDenoiser model revealed a reduction in median arrival‐time errors by 0.02 s for P waves and 0.01 s for S waves when processing waveforms prior to phase picking using PhaseNet on in‐distribution unseen data. When tested on out‐distribution unseen data, the model also effectively reduced the P‐wave median arrival‐time error by 0.02 and 0.01 s in median arrival‐time error for S waves. Importantly, the application of WaveDenoiser resulted in a significant reduction of phase picking outliers by 1.1% to 3.6% for both P and S waves. In addition, we achieved over five times acceleration in processing speed compared with the seisBench implementation of DeepDenoiser. Our findings underscore the potential of WaveDenoiser as a powerful tool for improving seismic data analysis and processing efficiency.

P-waves↗
DOE OSTI2025

Simulating High-Frequency Seismograms in Realistic Earth Models to Better Understand Source Discrimination Based on Differential Magnitudes ( M L− M c)

Discriminating low-yield underground nuclear explosions from small earthquakes is a key task in monitoring nuclear test ban treaties. P/S amplitude ratios have been an effective discriminant for moderate-sized events recorded at regional distances, but it is unclear if they are as effective in discriminating small seismic events recorded at local distances (<150 km). The difference between local magnitude (M L ) and coda duration magnitude (M c ) has been proposed as a new discriminant that may complement P/S amplitude ratios at local distances. Here, in this work, we calculate high-frequency (up to ∼4 Hz) synthetic seismograms at epicentral distances of 0–30 km in realistic models of the Salt Lake basin (Utah, United States) to better understand how variations in source type and depth affect M L −M c values. The Earth models incorporate simplified 1D and deterministic 3D structures, small-wavelength stochastic velocity perturbations, and surface topography. Coda waves are enhanced for the more complicated models compared to the base 1D model, but still underpredict observed durations by about a factor of two, which results in overprediction of amplitude to duration ratios (i.e., M L −M c values) for a near-surface explosion and a 7 km deep earthquake. For both source types, the predicted M L and M c values decrease as source depth increases, and M L −M c shows only minor variation with depth; however, M L −M c is on average ∼0.5 units smaller for explosions than earthquakes. This finding may imply that M L −M c has sensitivity to source type, in addition to being a depth discriminant, but more modeling is needed given the limitations of the current study. Future modeling should incorporate higher-frequency (≳5 Hz) simulations over a larger distance range (0–150 km), where M L and M c are commonly measured, while honoring low shear velocities (<300 m/s) near the surface and sampling a wider range of earthquake and explosion source mechanisms.

Hutchings, Sean J. [Univ. of Utah, Salt Lake City,↗
DOE OSTI2024

Enabling in-situ LIBS measurements of liquids and slurries

The aim of this work was to explore the use of, and further develop, a in-situ and near-real time LIBS (Laser Induced Breakdown Spectroscopy) instrument to measure liquids relevant to the Hanford site. Specifically, we investigated the feasibility of using LIBS to measure the cation composition of HLW (High Level Waste) streams. In addition, we focused on enabling continuous interrogation of those liquids using the LIBS system. For the first part, we investigated simple (sodium nitrate) solutions as well as various Hanford tank waste surrogate slurries. We showed that relevant cations (such as sodium, aluminum, magnesium, potassium) can be measured via LIBS under good conditions but the results – both quantitative and qualitative (aka reproducibility) – strongly depend on the kind and concentration of the material used. The dependence of signal intensity on concentration was also confirmed by other measurements conducted (sodium chloride and sodium nitrate in water). For the second part, we investigated the influence of varying depth and concentration of a liquid sample. We observed that for the concentrations and fill depths investigated, the liquid fill depth did not impact the emission intensity of the analyte, while concentration is confirmed to have significant effects. The results of the sample depth experiments influenced testing of waste surrogates. In addition, we focused on further developing and adapting data fitting tools in order to efficiently, accurately, and reproducibly identify the elements and materials present in a LIBS spectrum.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗
DOE OSTI2025

Virtual to Physical: Reinforcement Learning to Optimize SNS Particle Accelerator Controls

Complex accelerators must have control systems that can handle dynamic nonlinear environments. This makes traditional control methods unsuitable as they can struggle to adapt to these uncertainties. This provides an ideal environment for reinforcement learning algorithms as they are adaptable and generalizable. We present a reinforcement learning pipeline that can effectively handle the dynamics of a complex accelerator. We test and prove our pipelines capabilities on multiple environments including the Spallation Neutron Source (SNS) and the Beam Test Facility (BTF) at Oakridge National Lab (ORNL). Due to the limited time available to train an online algorithm like reinforcement learning on a real accelerator, we utilize a virtual twin accelerator (VIRAC) developed by ORNL to pretrain the policy and show its ability to converge in the virtual environment. We then test the adaptability of the pretrained RL model by applying it on the real accelerator and comparing the results. Utilizing our Scientific Optimization and Controls Toolkit (SOCT) and open-source standards such as Gymnasium we create and solve for a MEBT orbit correction problem in the SNS and an emittance maximization problem in the BTF. We show how Twin Delayed Deep Deterministic Policy Gradient (TD3) can solve this optimization environment in the virtual accelerator and transfer this policy onto the real accelerator for inference and model retraining. We show how reinforcement learning can be utilized as a control system for complex accelerators and provide a model pipeline for how an implementation performs and can be adapted to new accelerator control problems.

Kasparian, Armen [Thomas Jefferson National Accele↗
DOE OSTI2025

Accelerating effects of galvanic corrosion and dissimilar materials on the corrosion of 316H in NaCl-MgCl2 salt

Corrosion of materials presents a significant challenge for the long-term operation of molten salt reactors. This study aims to identify the most effective techniques for evaluating the corrosion performance of materials in molten salts, with a focus on the effects of galvanic corrosion and dissimilar materials. A reliable testing methodology for assessing material corrosion in molten chloride salts has been successfully developed. The corrosion of Alloy 316H in molten NaCl-MgCl2 salt was found to be significantly accelerated by galvanic corrosion. Additionally, the presence of dissimilar materials resulted in a slight increase in the corrosion rate of Alloy 316H in NaCl-MgCl2 salt. Microstructural characterization was utilized to understand the corrosion behavior of test samples under different conditions. Common trends observed across samples include chromium depletion and iron enrichment near corroded surfaces. Molybdenum enrichment along grain boundaries and corrosion surfaces was also frequently noted.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Measurements of Lund subjet multiplicities in 13 TeV proton-proton collisions with the ATLAS detector

This Letter presents a differential cross-section measurement of Lund subjet multiplicities, suitable for testing current and future parton shower Monte Carlo algorithms. This measurement is made in dijet events in 140 fb -1 of $\sqrt{s}$ =13 TeV proton–proton collision data collected with the ATLAS detector at CERN's Large Hadron Collider. The data are unfolded to account for acceptance and detector-related effects, and are then compared with several Monte Carlo models and to recent resummed analytical calculations. The experimental precision achieved in the measurement allows tests of higher-order effects in QCD predictions. Most predictions fail to accurately describe the measured data, particularly at large values of jet transverse momentum accessible at the Large Hadron Collider, indicating the measurement's utility as an input to future parton shower developments and other studies probing fundamental properties of QCD and the production of hadronic final states up to the TeV-scale.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Development of Additive Manufacturing for Ceramic Matrix Composite Vanes

This report discusses the development of additive manufacturing for polymer-derived ceramic materials to create novel cooled gas turbine airfoils made from ceramics. Gas turbine engines have extremely high temperatures in the hot section of the engine that exceed the melting point of nickel superalloy materials that the engine is made from. Advanced cooling technologies have been developed over decades for metallic parts. Ceramic materials have higher temperature capabilities than superalloys, but are difficult to shape in the complex designs used in modern turbines. Several thrusts were investigated during the project, including development of higher material strength resins, improvement of ultraviolet (UV) photopolymerization and post-processing techniques to increase survivability of ceramic parts, thermomechanical modeling of novel ceramic airfoil designs for higher cooling effectiveness, and experimental validation of the modeling and manufacturing in a turbine vane test facility at nondimensional conditions that are relevant to modern gas turbines. Some major findings of the work include a significant increase in ceramic survivability during pyrolysis (a high temperature process that converts the organic material to a ceramic) by adjusting the composition ratios of the resins, as well as a tolulene soak after printing to remove unreacted polymer material. Thermomechanical optimization of the internal cooling structure indicated that a high density pin fin array would enable an 80% increase in overall cooling effectiveness relative to a baseline geometry, which was later verified during experimental testing in a high speed linear cascade. Ceramic vanes were tested at Mach numbers of up to 0.9 which is relevant to modern gas turbines. High temperature capability of the ceramic vanes was not tested in this work.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nonseeded linewise temperature measurements by resonantly ionized photoemission thermometry in a Mach 4 Ludwieg tube

A one-dimensional (1D) thermometry using oxygen-tagging resonantly ionized photoelectron thermometry (O 2 RIPT) was employed to investigate thermal gradients within a Mach 4 Ludwieg tube. The Ludwieg tube is pulsed with a test duration of approximately 100 ms, providing a cold supersonic flow at Mach 4 ideal for studying aerothermal effects. This study focused on measuring freestream temperatures, capturing shock-induced heating behind a detached bow shock from a blunt cylinder, and resolving sharp temperature variations across a bow shock generated by a cylinder. The O 2 RIPT technique produced strong emission signals extending approximately 4 cm long, demonstrating its capability for precise temperature measurements in high-speed wind tunnel environments. The results confirm that O 2 RIPT is well-suited for applications in large-scale aerodynamic testing facilities, particularly in regions with strong compression effects, enabling the resolution of sharp thermal gradients. This method presents a promising solution for thermometry in dynamic flow conditions relevant to various experimental ground-test facilities.

McCord, Walker (ORCID:0000000179747550)↗