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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Uncertainty Estimation and Anomaly Detection in Chiral Effective Field Theory Studies of Key Nuclear Electroweak Processes

Chiral effective field theory (χEFT) is a powerful tool for studying electroweak processes in nuclei. I discuss χEFT calculations of three key nuclear electroweak processes: primordial deuterium production, proton-proton fusion, and magnetic dipole excitations of 48 Ca. Further, this article showcases χEFT’s ability to quantify theory uncertainties at the appropriate level of rigor for addressing the different precision demands of these three processes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2024

Exploring the potential and impact of single-crystal active materials on dry-processed electrodes for high-performance lithium-ion batteries

Roll-to-roll powder-to-film dry processing (DP) and single-crystal (SC) active materials (AMs) with many advantages are two hot topics of lithium-ion batteries (LIBs). However, DP of SC AMs for LIBs is rarely reported. Consequently, the impact of SC AMs on dry-processed LIBs is not well understood. Herein, for the first time, via a set of experimental and theoretical studies of the conventional polycrystalline-AM- and SC-AM-based DPed electrodes (DPEs), this work not only reports a high-performance dry SC-AM cathode for LIB manufacturing, but also establishes some fundamental understanding of SC-based dry-processed electrodes, including their morphology, structure, mechanical strength, electronic conductivity and LIB electrochemical behavior. Further, the results suggest that DP of SC AMs is promising, which can dramatically improve the electrochemical kinetics at electrode level and particle level. Specifically, for the rate capability and long-term cyclability in full cells, SC DPEs exhibit a discharge specific capacity of 152.1 mAh g -1 at 1C and a capacity retention rate of 79.9 % at C/3 over 500 cycles, which are superior to those of PC DPEs (135.6 mAh g -1 and 68.3 %) at the same conditions and are further confirmed by the simulation data from the theoretical modelling study. Therefore, this comprehensive work marks a significant milestone for DP strategy and SC AMs, enlightening future research and development of LIB manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2025

Incorporating corrosion design constraints in desalination process optimization: A case study in mechanical vapor compression

Corrosion is an expensive and complex challenge for desalination, yet current design approaches do not explicitly account for corrosion mechanisms in process modeling and technoeconomic analysis. Here, to address this gap, we present a workflow for incorporating corrosion design constraints directly into desalination process optimization models. We develop surrogate models for general and localized corrosion metrics as functions of temperature, pH, salinity, dissolved oxygen, and material using data from OLI Systems’ Corrosion Analyzer. We then integrate these surrogates as corrosion design constraints in a cost-optimization MVC model that minimizes the levelized cost of water (LCOW). For a case study of mechanical vapor compression (MVC) treating seawater across a range of recoveries, we find dissolved oxygen (DO) is the dominant driver of localized corrosion, and thus of cost-optimal material choice and operating conditions. Reducing the DO from 8 mg/L to 0.5 mg/L reduces the LCOW by 15-35%, informing the breakeven costs for implementing DO removal or selecting highly corrosion-resistant alloys. This framework is broadly applicable across corrosion types, materials, and components and enables desalination process design that minimizes capital costs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Lithium-sulfur (Li-S) batteries involve complex solid-liquid-solid phase transformations during both discharging and charging processes, where cathode materials, formulation, and structure play a crucial role. Here, a design of experiments (DoE) methodology and an empirical model are developed to systematically explore the interactions and trade-offs among cathode factors and process variables, and to obtain generalizable effects estimates for the multivariate system. Compared to the conventional one-factor-at-a-time (OFAT) approach, this work demonstrates advantages in both efficiency and accuracy by allowing the data to guide future research and decisions. Further, an optimized cathode formulation and processing parameters are predicted and validated experimentally, achieving over 1000 mAh g -1 in discharge capacity and improved cycling under practical lean electrolyte (4 µL mg -1 S) and high S-loading cathodes (>4 mg cm -2 ) conditions. The optimized cathode was scaled up and assembled into Li-S pouch cells, achieving 316 Wh kg -1 in cell-level energy, proving that the comprehensive and rigorous framework for optimizing complex systems with DoE leads to improved performance in a practical pouch cell system.

25 ENERGY STORAGE↗
DOE OSTI2025

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10 %, surpassing those simulated using the ray-tracing method with an error margin of 30 %, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

Computational fluid dynamics↗
DOE OSTI2026

Fabrication of thin-walled tubes from alloy 602 CA using shear assisted processing and extrusion

Thin-walled tube is usually produced seamlessly via an expensive multi-step process or via welding of thin sheets but at reduced performance. To overcome the current process and performance inefficiencies, advanced manufacturing methods need to be investigated. In this investigation, shear assisted processing and extrusion (ShAPE) was used to fabricate thin-walled Inconel 602 tube in a single-step. Tubes measuring 0.83 m length with a 12 mm outer diameter and 1 mm wall thickness were fabricated with an average surface roughness, Ra of 1.6 µm and Rz of 15 µm. Tensile testing of tubes in the as-fabricated condition showed strength increases of 4-35% over current offerings while maintaining or improving elongation. Electron microscopy analysis revealed the recrystallized microstructure with refined inter- and intra-granular carbides. Preliminary results obtained in this investigation shows the feasibility of producing thin-wall high temperature tube in single-step via ShAPE. This achievement marks a significant step towards manufacturing larger diameter nickel alloy tubes for the U.S. Department of Energy’s Waste Treatment and Immobilization Plant.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Establishing a process-structure-property-performance framework for SLS additive manufacturing through integrated multiscale modeling

This study presents a comprehensive suite of high-fidelity computational models that integrate multiscale and multiphysics simulations to capture the full Selective Laser Sintering (SLS) additive manufacturing process—from initial melting and solidification to mechanical response under external loads. Process simulations are linked with mechanical analysis through Representative Volume Elements (RVEs), establishing a process-structure–property-performance framework. The interaction between laser light and polyamide 12 (PA12) powder is modeled, accounting for laser characteristics and the optical, thermal, and geometrical properties of the powder. The heat source is incorporated into a heat transfer model, coupled with crystallization kinetics and densification models to predict material density and crystallinity. The porosity distribution from the densification model and crystallinity interpolated from experimental data are used to construct the RVEs. A multi-mechanism constitutive model is then calibrated using mechanical tests to predict the stress–strain response. Simulation results show good agreement with experimental data in terms of porosity, crystallinity, and mechanical performance when sufficient laser power (62 W or higher) is used. This research supports the inverse design of 3D-printed structures by introducing a high-fidelity framework that combines multiscale and multiphysics modeling with experimental calibration for predictive and performance-driven additive manufacturing.

SLS↗
DOE OSTI2025

Evaluation of a high-throughput method for processing sponge-stick samples to detect viable, non-spore-forming biothreat agents

After a bioterrorism incident, surface sampling is often used to determine the extent of contamination and exposure, guiding decontamination efforts and decisions for re-occupancy of affected sites. The sponge-stick (SS) is a preferred and commonly used device for sample collection to detect both spore-forming and non-spore-forming biothreat agents from non-porous surfaces. Here, in this study, a recently developed high-throughput method (HTM) for processing SS samples to detect viable Bacillus anthracis spores was adapted for detection of non-spore-forming biothreat agents, Yersinia pestis and Francisella tularensis. The scalable HTM was used to process up to 20 SS samples simultaneously, compared to the current stomacher-based method which processes one SS at a time. Comparisons of the HTM and the stomacher-based method were statistically indistinguishable for most experiments (P > 0.05) with HTM recoveries of 37–60 % for Y. pestis inoculated at 102–103 cells/SS and held 48 h at 4 °C to mimic sample transport/storage. The HTM was integrated with Rapid Viability-Polymerase Chain Reaction (RV-PCR) analysis to detect viable Y. pestis in the presence of particulate contamination (Arizona Test Dust, ATD). This approach detected Y. pestis inoculated at 20 cells/SS and ATD did not impact detection (P > 0.05). F. tularensis showed significantly lower recoveries between no-hold time and 48-h hold time (4 °C, P < 0.05) using the HTM, which further testing showed could be due to toxicity of the neutralizing buffer used for SS pre-wetting. With modifications, this method could enhance throughput capacity while maintaining similar recovery efficiencies to current methods for other non-spore-forming bacterial pathogens.

Biological and medical sciences↗
DOE OSTI2025

Implications of a weakening N = 126 shell closure away from stability for r -process astrophysical conditions

The formation of the third r-process abundance peak near A ∼ 195 is highly sensitive to both nuclear structure far from stability and the astrophysical conditions that produce the heaviest elements. In particular, the N = 126 shell closure plays a crucial role in shaping this peak. Experimental data hints that the shell weakens as proton number departs from Z = 82, a trend largely missed by global mass models. To investigate its impact on r-process nucleosynthesis, we employ both standard global models with strong closures and modified Duflo-Zuker (DZ) models that reproduce the weakening, combined with three sets of β − -decay rates. Strong shell closures generate sharply peaked abundances, whereas weakened closures consistent with the experimental trend produce broader, flatter patterns. Accurately reproducing the solar third peak under weakened shell strength requires sufficiently neutron-rich conditions that significant fission occurs, and slower decay rates. These results demonstrate that a weakening N = 126 shell closure away from stability imposes significant constraints on the astrophysical environments of the r-process and underscores the need for precise mass measurements and improved characterization of β − -decay properties in this region.

N = 126 closed shells↗
DOE OSTI2025

Reducing waste of the hydride-dehydride process for U-6 wt% Nb spherical powders through lower impact and targeted milling

The breakdown of solid metal into powder during the hydride-dehydride process is commercially important for the formation of titanium and other metal powders. Typically, the milling of the brittle hydride powder occurs in a ball mill, where milling media impacts powder particles to break them down. The milling media can impact particles that are larger than desired, as desired, or smaller than desired, indiscriminately making all particles smaller. In this work, we investigate how to minimize waste powder production during milling using two different milling methods, planetary ball milling and milling in a sieve shaker (sieve-milling). Both processes yielded similar amounts of 20–75 μm diameter powder (the target size range); however, sieve-milling generated a significantly smaller amount of undersized waste powder. The powders were characterized by X-ray diffraction, SEM/STEM, and magnetic susceptibility. Several differences between ball milling and sieve-milling processes are discussed. We then conclude that the decreased yield of undersized powder in sieve-milling was due to a combination of lower impact energy in sieve-milling, unreacted metallic cores in the hydride flakes, and the ability to mill target particle sizes during sieve-milling. While these results are from the milling of brittle hydride powder, similar methods may be applicable to other brittle powders, including ceramics or salts.

Chemistry↗
DOE OSTI2026

JetGP: A derivative enhanced Gaussian process library

Derivative enhanced Gaussian Processes (DEGPs) can significantly improve surrogate model accuracy over standard Gaussian Process (GP) formulations by incorporating derivative information. However, standard implementations scale poorly with dimension, limiting their use in high dimensional engineering problems. JetGP is a Python framework that unifies existing derivative enhanced GP methodologies into a single library and extends them to support arbitrary order derivative information. The library implements four complementary formulations: standard derivative enhanced Gaussian Processes (DEGP), directional DEGP (DDEGP), generalized directional DEGP (GDDEGP), and weighted DEGP (WDEGP). By unifying these approaches in a consistent interface with robust numerical implementations, JetGP enables practitioners to balance predictive accuracy and computational efficiency for high dimensional optimization, uncertainty quantification, and sensitivity analysis in engineering design.

Derivative enhanced Gaussian process↗
DOE OSTI2025

Toward Quality Control in Perovskite Solar Cell Fabrication: Spot-Like Processing Defects Disrupt Charge Transport Layers and Promote Ag Metal Electrode Intrusion

Metal halide perovskite (MHP) photovoltaics provide high efficiencies with less stringent processing requirements than traditional photovoltaic materials. However, processing related defects must be suppressed as they can lead to decreases in initial device efficiency and potentially compromise long-term device operation. In this work we investigate morphological defects in MHP devices using luminescence imaging followed by in-depth structural and composition analysis using electron microscopy-based methods. We identify several different classes of spot-like processing-related defects and observe that a single device structure may contain multiple types of these defects. The presence of these defects in devices with different layer structures and absorber chemistries makes them relevant to the perovskite photovoltaic community as a whole. The defects are associated with voids in the perovskite layer, inclusions (glass, migrated Ag, dust), thickness variations, hole transport layer disruption with anomalous crystal growth, and electron transport layer disruptions that could allow Ag intrusion and lead to local shunts. As perovskite photovoltaic technology matures, mitigation of such defects is critical to improving not only initial performance but also the long-term stability required for industrial applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Phosphate-Based Approaches for Dechlorination and Treatment of Salt Waste from Electrochemical Processing of Used Nuclear Fuel: A Perspective on Recent Work

Phosphate-based reagents are being considered by the U.S. Department of Energy (DOE) Office of Nuclear Energy to process halide salt-based nuclear wastes for stabilization prior to disposal. As evidenced by the Experimental Breeder Reactor-II (EBR-II) project, electrochemical processing (pyroprocessing) can be employed to recover uranium and other actinides for reintegration into the nuclear fuel cycle from metallic fuels. The resultant salt-based wastes generated from electrochemical processing of EBR-II fuel contains fission products within a LiCl–KCl eutectic salt that necessitate appropriate disposal. This paper provides an overview of recent efforts to support halide-based salt waste treatment for disposition, as well as a basis for comparison with other related efforts in salt waste treatment through salt partitioning initiatives. The U.S. DOE has selected a phosphate waste form reference material for further investigation and longer-term studies.

electrorefiner↗
DOE OSTI2024

Electrical Resistivity Changes During Heating Experiments Unravel Heterogeneous Thermal‐Hydrological‐Mechanical Processes in Salt Formations

Abstract Rock salt is considered a suitable medium for the permanent disposal of heat‐generating radioactive waste due to its isolation properties. However, excavation damage and heating induce complex and heterogeneous thermal‐hydrological‐mechanical (THM) processes across different zones. Quantifying this heterogeneity is crucial for accurate long‐term performance assessment models, but traditional methods lack the necessary resolution. This study employs 4D electrical resistivity tomography (ERT) monitoring during controlled heating experiments in a salt formation to unravel the spatiotemporal dynamics of THM processes. Advanced time‐lapse inversion and clustering analysis quantify subsurface properties and map the heterogeneity of THM dynamics. The ERT results can estimate subsurface properties and delineate the damaged and intact zones, enabling appropriate parameterization and representation of processes for long‐term modeling. This approach may be used in further improving the predictive models and ensuring the safe long‐term disposal of radioactive waste in rock salt.

58 GEOSCIENCES↗
DOE OSTI2025

Emulation of the calculations of final r -process abundance patterns with a neural network

This work explores the construction of a fast emulator for the calculation of the final pattern of nucleosynthesis in the rapid neutron capture process (the r-process). An emulator is built using a feed-forward artificial neural network (ANN). We train the ANN with nuclear data and relative abundance patterns. We take as input the β-decay half-lives and the one-neutron separation energy of the nuclei in the rare-earth region. The output is the final isotopic abundance pattern. In this work, we focus on the nuclear data and abundance patterns in the rare-earth region to reduce the dimension of the input and output space. We show that the ANN can capture the effect of the changes in the nuclear physics inputs on the final r-process abundance pattern in the adopted astrophysical conditions. We employ the deep ensemble method to quantify the prediction uncertainty of the neural network emulator. The emulator achieves a speed-up by a factor of about 20 000 in obtaining a final abundance pattern in the rare-earth region. The emulator may be utilized in statistical analyses such as uncertainty quantification, inverse problems, and sensitivity analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Large-scale real-time signal processing in physics experiments: the ALICE TPC FPGA pipeline

For LHC Run 3, the ALICE Time Projection Chamber was upgraded to operate in continuous readout mode. Interaction rates of up to 50 kHz in Pb-Pb collisions require real-time processing of more than 3 TB s -1 of raw detector data. This requirement is met by a custom FPGA-based processing pipeline that performs the complete front-end data treatment fully in-stream, including common-mode correction, pedestal subtraction, ion-tail filtering, zero suppression, and dense data packing. A central element of the design is a highly parallel common-mode correction algorithm operating directly on the streaming data. It robustly identifies signal-free readout channels on a time-bin basis and applies pad-dependent scaling to compensate for local variations in capacitive coupling in the GEM readout. In combination with pedestal subtraction and ion-tail filtering, this enables accurate baseline restoration under extreme high-occupancy conditions, preventing signal loss while efficiently suppressing noise prior to zero suppression. The pipeline operates continuously at the full detector bandwidth and reduces the raw input rate of approximately 3 TB s -1 to about 900 GBps for Pb-Pb collisions at the target interaction rate. Overall, it represents a large-scale FPGA-based real-time signal-processing implementation for high-energy physics detector readout.

Digital signal processing (DSP)↗
DOE OSTI2025

Constraining nuclear mass models using 𝑟-process observables with multiobjective optimization

Modeling nuclear masses, particularly for nuclei far from stability, remains a key objective in nuclear physics. One contemporary approach is machine learning (ML), which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (𝑟 process), which in principle could inform ML models. Here, we introduce a multiobjective optimization approach using the Pareto front algorithm. We show that this technique, capable of identifying models that generate 𝑟-process abundances aligning with both solar and stellar data, is a promising method to select ML models with reliable extrapolation power.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗