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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 OSTI2022

Process-microstructure relationship of laser processed thermoelectric material Bi2Te3

Additive manufacturing allows fabrication of custom-shaped thermoelectric materials while minimizing waste, reducing processing steps, and maximizing integration compared to conventional methods. Establishing the process-structure-property relationship of laser additive manufactured thermoelectric materials facilitates enhanced process control and thermoelectric performance. This research focuses on laser processing of bismuth telluride (Bi 2 Te 3 ), a well-established thermoelectric material for low temperature applications. Single melt tracks under various parameters (laser power, scan speed and number of scans) were processed on Bi 2 Te 3 powder compacts. A detailed analysis of the transition in the melting mode, grain growth, balling formation, and elemental composition is provided. Rapid melting and solidification of Bi 2 Te 3 resulted in fine-grained microstructure with preferential grain growth along the direction of the temperature gradient. Experimental results were corroborated with simulations for melt pool dimensions as well as grain morphology transitions resulting from the relationship between temperature gradient and solidification rate. Samples processed at 25 W, 350 mm/s with 5 scans resulted in minimized balling and porosity, along with columnar grains having a high density of dislocations.

Oztan, Cagri↗
DOE OSTI2024

Designing Particle Morphologies for Materials with Solid Transport Limitations: A Case Study of Lithium and Manganese Rich Cathode Oxides

A lithium and manganese rich nickel-manganese-cobalt oxide (LMR-NMC) cathode is a promising candidate for next-generation batteries due to its high specific capacity, low cost, and low cobalt content. However, the material suffers from poor rate capability due to the diffusion limitations of lithium in the cathode particles. Understanding the material performance requires careful control of the morphology of the cathode particles, taking into account the primary and agglomerated diffusion pathways and the presence of pores, some of which could be closed from electrolyte infiltration. Here, in this study, we use a microstructure-based mathematical model combined with experimental data to understand the role of the complex cathode particle morphology in the rate performance of the material. Scanning electron microscopy images of cathodes made under different synthesis conditions, which results in different agglomerate morphologies, serve as the input into the mathematical model. The model is then compared to rate data to understand the controlling parameters. The presence of intra-agglomerate closed pores results in a large agglomerate diffusion length in comparison to the ideal condition, where the primary particles are agglomerated in an open and dispersed manner such that the entire interfacial area is available for electrochemical reaction. Smaller primary and agglomerate diffusion lengths result in better electrochemical performance. This points us toward designing the morphology of the cathode particles to compensate for the diffusion limitation of LMR-NMC while maximizing the density.

Tewari, Deepti↗
DOE OSTI2025

Radiation‐Resistant Aluminum Alloy for Space Missions in the Extreme Environment of the Solar System

Future human exploration of the solar system demands advanced materials capable of withstanding extreme environments, particularly exposure to solar energetic particle radiation. Current material selection criteria for space applications prioritize a high strength-to-weight ratio, high corrosion resistance and manufacturability, favoring age-hardenable Al-based alloys. However, conventional precipitation-hardened Al alloys suffer from irradiation-assisted dissolution of strengthening phases at doses as low as 0.2 displacements-per-atom (dpa), undermining their performance. Furthermore, these alloys develop radiation-induced defects, such as dislocation loops and voids, even at low doses. This study presents a novel ultrafine-grained (UFG) Al-based alloy, designed using the crossover alloying concept and strengthened by T-phase precipitates, featuring a chemically-complex structure with 162 atoms in its unit cell composed of Mg 32 (Zn,Al) 49 . It is showed that T-phase precipitates have exceptional radiation tolerance up to 24 dpa. Owing to the nanoscale UFG structure, dislocation loops are suppressed, and voids are only observed beyond 75 dpa. Microtensile tests up to 20 dpa confirm the preservation of mechanical performance under irradiation. The results underline the potential of this alloy as a radiation-resistant, lightweight material for future space applications. Three key strategies enable this performance: (i) stabilization of a UFG microstructure, (ii) T-phase precipitation featuring a highly negative Gibbs free energy and chemically-complex giant unit cell, and (iii) precise process control to prevent grain growth during heat treatment and irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tensile creep properties of a CrMnFeCoNi high-entropy alloy

Tensile creep tests were performed on a CrMnFeCoNi high-entropy alloy at temperatures from 1023 K to 1173 K. A uniform stress exponent 3.7 ± 0.1 was found across all temperatures. The apparent activation energies of creep under various applied stresses were determined to be around 230 kJ/mol and decrease with increasing stress, indicating a stress-assisted, thermally activated behavior. Steady-state creep microstructures feature no subgrain formation and high dislocation density within grains. Based on our results, the creep rate of CrMnFeCoNi is believed to be controlled by both dislocation-dislocation interactions and dislocation-lattice interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of carbonation on leaching of constituents from a cementitious waste form for treatment of low activity waste at the DOE Hanford site

Carbonation can be a major aging process during disposal of alkaline cementitious waste forms and can impact constituent leaching by changes in material alkalinity, pore structure, and controlling mineral phases. The effect of carbonation on the leaching of major and trace constituents from Cast Stone, a cementitious waste form developed to treat high salt content low activity waste, was studied through a combination of leaching experiments and reactive transport simulations. Diffusive transport of constituents in the waste form was evaluated using reactive transport modeling of diffusion-controlled leaching test results and a geochemical speciation model derived from pH-dependent leaching. Comparisons between Cast Stone materials aged under nitrogen, air, and 2% carbon dioxide in nitrogen showed that carbonation impacts solubility, physical retention and observed diffusivity of major and trace constituents. Carbonation under 2% CO 2 decreased the diffusion-controlled leaching of chromium by two orders of magnitude. Finally, modeling results suggest that carbonation may also decrease solubility of technetium while changes to microstructure by carbonation increases effective diffusivity of constituents in Cast Stone.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

A mechanistic interpretation of Nelson curves for PVP failures under high temperature hydrogen attack

As an empirically established design criterion, Nelson curves that relate the service temperature and the allowable hydrogen partial pressure have been developed and utilized for more than sixty years in pressure vessels and piping (PVP) safety design. Despite a relatively clear thermodynamic understanding of the high-temperature-hydrogen-attack (HTHA) problem, the detailed fracture process on the microstructural length scales, however, remains elusive, and a quantitative assessment of the PVP lifetime under HTHA from the available creep fracture dataset is still not possible. This work develops a microstructure-informed and micromechanics-based model by incorporating a synergy between hydrogen transport and intergranular-cavity-based fracture process. Based on the available creep lifetime data of C-0.5Mo steels, we are able to calibrate material constitutive parameters, and then conduct nonlinear finite element simulations that reveal a real-time stress-induced hydrogen diffusional transport along grain boundaries, coupled with a microstructure-explicit failure process, from which Nelson curves can be computed. Such failure analyses allow us to delineate two distinct regimes on the Nelson curves, i.e., dislocation-creep-controlled or grain boundary diffusion-assisted cavity growth. More importantly, we found that a small change of the pipe thickness and applied stresses can significantly shift these lifetime curves. However, these two parameters are usually not provided in Nelson curves, thus limiting their usage in material selection and safety design. In conclusion, this discrepancy can clearly be mitigated by extensive parametric studies from our micromechanical modeling/simulation framework.

36 MATERIALS SCIENCE↗
DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inferring topological transitions in pattern-forming processes with self-supervised learning

Abstract The identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes.

Abram, Marcin↗
DOE OSTI2020

Lower length scale informed improvements to Bison U-Pu-Zr fuel swelling model

Due to renewed interest in metallic fuels from the U-(Pu)-Zr material system, significant improvements have recently been made to BISON's capability to simulate metallic fuel, particularly in the area of swelling. In this report, lower length scale simulations that have been conducted to inform the engineering-scale models in BISON during FY20 are described. For the high-temperature regions of the fuel that are dominated by the $\gamma$ phase of U-(Pu)-Zr, two new models for swelling have implemented in BISON. Phase-field simulations were conducted to calculate parameters for these and other BISON models that control when gaseous swelling ceases and fission gas release begins. For the low-temperature regions of the fuel dominated by the phase of uranium, microstructurally resolved simulations of pore growth and crystal deformation were performed to understand the effect of irradiation-generated crystal shape changes. Conformally meshed microstructures with pores were simulated with a crystal plasticity model and with a burnup eigenstrain model. Plastic deformation will not cause pore size to increase; burnup eigenstrain (irradiation-induced crystal shape changes) will increase the volume of existing porosity and cause it to become anisotropic in shape. Additional model development work for early-stage fission gas cluster/bubble formation in the $\gamma$ phase were performed, by coupling with defect clustering within cluster dynamics framework. Sensitivity analysis were conducted to identify the critical thermo-kinetic parameters needed for rigorous assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A novel microstructural approach for TRIP/TWIP β-Ti alloys using intermetallic precipitation

Interest in metastable β-Ti has grown, due to the exceptional work hardening caused by TRansformation and TWinning Induced Plasticity (TRIP/TWIP). A current challenge is increasing the yield strength, while maintaining the benefits of TRIP/TWIP. This study presents the first report of TRIP/TWIP in a metastable β-Ti alloy containing intermetallic compounds, specifically Ti silicide precipitates. Silicon (Si) additions enable the formation of silicide particles, which can be precipitated, dissolved, and controlled through tailored heat treatments. In-situ synchrotron x-ray diffraction during tensile testing reveals primary {332}<113> twinning activates first, followed by TRIP, leading to extensive work hardening and uniform elongation in the solutionized state. In conclusion, these findings open a novel microstructural approach to the design of multiphase TRIP/TWIP titanium alloys, relying on intermetallic precipitation rather than allotropes such as α or ω phase, and lay the groundwork for future studies on intermetallic precipitation strengthening.

Materials science↗
DOE OSTI2020

Ion diffusion coefficients in poly(3-alkylthiophenes) for energy conversion and biosensing: role of side-chain length and microstructure

Conductive polymers are promising materials as active elements for energy storage and conversion devices due to mixed ion–electron conduction. The ion diffusion coefficient is a relative measure of the efficacy of ion transport, allowing for comparison between materials and electrochemical conditions. In this work, diffusion coefficients of hexafluorophosphate (PF6–) counterions in poly(3-alkylthiophene) (P3AT) materials are measured as a function of both side-chain length and microstructure using electrochemical impedance spectroscopy (EIS). For semi-crystalline films, the diffusion coefficient is found to be anomalous and nearly independent of applied electrochemical potential. Here, the anomalous behavior of diffusion indicates that spin casting yields compact films with an enthalpic barrier to ion transport, attributed to ionic trapping. Diffusion coefficient values ~10 –11 cm 2 s –1 were measured for all films, indicating interchain spacing, in the absence of strong intermolecular interactions with the electrolyte, is not a viable design strategy to control ion transport. For the prototypical system of poly(3-hexylthiophene), we observe almost no potential dependence in ion transport for regioregular and regiorandom films of comparable molecular weight, with both exhibiting anomalous diffusion. Alternatively, changing the microstructure of poly(3-hexylthiophene) to a mostly amorphous, ion-imprinted structure yields ~500× increase in the diffusion coefficient to ~2 × 10 –8 cm 2 s –1 at 0.8 V vs. Ag/Ag + with behavior closer to ordinary diffusion. Collectively, these results indicate new insight into ion transport in conductive polymers, where ionic trapping effects can be mitigated through electrodeposition protocols over post-synthesis processing (i.e. spin coating).

14 SOLAR ENERGY↗
DOE OSTI2021

Direct Optical Lithography of Colloidal Metal Oxide Nanomaterials for Diffractive Optical Elements with 2π Phase Control

Spatially patterned dielectric materials are ubiquitous in electronic, photonic, and optoelectronic devices. These patterns are typically made by subtractive or additive approaches utilizing vapor-phase reagents. On the other hand, recent advances in solution-phase synthesis of oxide nanomaterials have unlocked a materials library with greater compositional, microstructural, and interfacial tunability. However, methods to pattern and integrate these nanomaterials in real-world devices are less established. In this work, we directly optically pattern oxide nanoparticles (NPs) by mixing them with photosensitive diazo-2-naphthol-4-sulfonic acid and irradiating with widely available 405 nm light. We demonstrate the direct optical lithography of ZrO 2 , TiO 2 , HfO 2 , and ITO NPs and investigate the chemical and physical changes responsible for this photoinduced decrease in solubility. Micron-thick layers of amorphous ZrO 2 NPs were patterned with micron resolution and shown to allow 2 pi phase control of visible light. We also show multilayer patterning and use it to fabricate features with different thicknesses and distinct structural colors. Upon annealing at 400 degrees C, the deposited ZrO 2 structures have excellent optical transparency across a wide wavelength range (0.3-10 mu m), a high refractive index (n = 1.84 at 633 nm), and are optically smooth. We then fabricate diffractive optical elements, such as binary phase diffraction gratings, that show efficient diffractive behavior and good thermal stability. Different oxide NPs can also be mixed prior to patterning, providing a high level of material tunability. This work demonstrates a general patterning approach that harnesses the processability and diversity of colloidal oxide nanomaterials for use in photonic applications.

2𝜋 phase shift↗
DOE OSTI2022

Mechanical behavior of bimetallic stainless steel and gray cast iron repairs via directed energy deposition additive manufacturing

The utility of gray cast iron in engine components remains tied to the mechanical performance and cost. Repair and remanufacturing of castings offer economical and sustainable benefits; however, high thermal input from traditional fusion-based welding is unable to restore the original mechanical quality owing to brittle microstructures and porosity formed in situ. Directed energy deposition (DED) is an additive manufacturing method that has received considerable interest for repairs owing to the highly controllable nature of the process. Despite this, few works have connected the effect of DED parameters on actual interfacial strength. Consequently, distinct DED parameter combinations were identified to maximize the strength and fatigue life of the repaired cast iron. Further, high speed melt pool imaging and residual stress measurements are provided to aid in the understanding of the metallurgical quality and strength seen in these structures. In general, higher scanning speeds and lower thermal gradients promoted comparable tensile strength to that of the original gray cast iron. The results presented here provide a foundation to tune in the DED process to generate the required mechanical quality as a starting point for future process advancements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhancing the multi-functional properties of renewable lignin carbon fibers via defining the structure–property relationship using different biomass feedstocks

Lignin has been explored extensively as a renewable precursor for carbon materials, considering its abundance as a major component of plant cell walls and its sustainability as a byproduct of both lignocellulosic biorefinery and the paper-making industry. Despite the extensive efforts to define the process–property relationship, it remains largely unknown how lignin biosynthesis and its chemistry would impact the resultant carbon fiber properties, for both mechanical and electroconductive performances. Such inadequate understanding fundamentally limits feedstock design and selection to improve carbon fiber properties toward broader commercial applications. Using lignin from a broad range of biomass feedstocks for carbon fiber manufacturing, we have fundamentally explored the structure–function relationship between lignin chemistry and carbon fiber performance. Specifically, lignin extracted from hardwood (sugar maple), softwood (loblolly pine and red cedar), and herbaceous plants (corn stover and switchgrass) was used for carbon fiber manufacturing, considering the very different lignin structures from these feedstocks. Linear regression models were established to define the relationship between carbon fiber mechanical properties and lignin structural characteristics. The results highlighted that the content of β-O-4 linkages correlates significantly with the tensile strength and elastic modulus of lignin carbon fibers, indicating that more linear β-O-4 linkages would promote the carbon fiber mechanical performance. Moreover, electroconductive properties are essential for broader energy device application of lignin-based carbon fibers, yet the mechanisms controlling their electroconductivity are largely unknown. Furthermore, we hereby demonstrated that a higher β-O-4 content also promotes the electroconductivity of lignin carbon fibers. Microstructure analysis further revealed that the crystallite size and content of the pre-graphitic turbostratic carbon structure in lignin-based carbon fibers were enhanced as the β-O-4 linkages increased. The content of β-O-4 linkages has shown a strong correlation with the crystallite content in a linear regression model. This study thus revealed the underlying mechanisms regarding how the lignin structure in planta defines the resultant carbon fiber properties. Moreover, the study also highlighted the correlation between the mechanical and electroconductive properties of lignin-based carbon fibers, both of which were defined by the lignin structure.

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

Protocol-dependent frictional granular jamming simulations: cyclical, compression, and expansion

Granular matter takes many paths to pack in natural and industrial processes. The path influences the packing microstructure, particularly for frictional grains. We perform discrete element modeling simulations of different paths to construct packings of frictional spheres. Specifically, we explore four stress-controlled protocols implementing packing expansions and compressions in various combinations thereof. We characterize the eventual packed states through their dependence of the packing fraction and coordination number on packing pressure, identifying non-monotonicities with pressure that correlate with the fraction of frictional contacts. These stress-controlled, bulk-like particle simulations access very low-pressure packings, namely, the marginally stable limit, and demonstrate the strong protocol dependence of frictional granular matter.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Demonstration of Dimensional and Microstructural Postirradiation Examination Capabilities on MiniFuel Disk Specimens

This work demonstrates two new MiniFuel postirradiation examination (PIE) capabilities, laser profilometry and serial sectioning, for future use on alpha-U specimens for the upcoming Mini-99 irradiation campaign. Representative U–Mo disk specimens—3 mm in diameter and approximately 0.75 mm in thickness—were subjected to dimensional measurements using standard calipers, as well as a new Keyence CL-3000 series laser profilometry system designed to measure the surface of both sides of the disk simultaneously. The laser profilometry system captured thickness gradients across the specimens as large as 0.2 mm. Additionally, because of the limitations inherent to the use of calipers, caliper measurements consistently overestimated the initial volume of MiniFuel disk specimens compared to the laser profilometry method by up to 5%. However, the results show that optimal data collection and analysis procedures are crucial to avoiding significant artifacts during data collection that can lead to large errors (approximately 10%). Serial sectioning using a combination of ion milling and electron imaging was also demonstrated for the analysis of micron-sized control volumes. Nanometer- to micron-scale porosity was resolved on a representative U–Mo specimen before irradiation, even when the surface of the specimen appeared free of porosity. Future efforts to relate microstructural features (porosity, fission product distributions, and so on) to macroscale effects (dimensional changes, thermal conductivity degradation) require accurate characterization methods such as those demonstrated in this work. These advanced PIE capabilities will be applied to optimized alpha-U specimens for the upcoming Mini-99 neutron irradiation campaign.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗