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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Surface Preparation of Additively Manufactured 316H Stainless Steel for Molten Salt Applications

Additive manufacturing (AM) has the potential to revolutionize the manufacturing process and speed up deployment of advance nuclear reactors. However, more information is needed on preparing AM materials for extreme reactor environments to qualify AM materials for nuclear applications. This project studies the surface preparation of AM 316H stainless steel using acid pickling and reports a successful procedure for descaling heat-treated AM material.

316H Stainless Steel↗
DOE OSTI2020

EFRC Materials Science of Actinides (Final Report)

The Energy Frontier Research Center Materials Science of Actinides (MSA) conducted basic research from 2009-2019. The scientific personnel and participating institutes changed several times during the evolution of the Center, and in total included 24 senior investigators, four national laboratories, and ten universities. Workforce development was a motivating goal of the university-led Center, which included many undergraduate, graduate, and post-doctoral researchers. The center focused on the actinide elements, which arise from the sequential filling of the 5f electron orbitals. Although the specific themes evolved during the Center’s history, the final research themes were: (1) Nanoscale Cage Clusters, (2) Complex Ceramic and Metallic Materials, and (3) Materials Under Extreme Environments. To date, the Center has produced 254 peer-reviewed journal publications and book chapters that acknowledged only the EFRC funding, and an additional 161 journal publications and book chapters that acknowledged funding from the EFRC and additional sources (a few manuscripts are still in preparation). Broad highlights include: (1) Development of an extensive family of actinide nanomaterials that self-assemble in aqueous solution, an understanding of their formation mechanisms and stabilities, and possible uses in nuclear fuel cycles; (2) Establishment of facilities and methodologies to conduct high-temperature calorimetry on transuranium materials, and extensive thermodynamic-based studies of uranium and thorium compounds important to the nuclear fuel cycle; (3) A vastly improved understanding of the behavior of actinide materials in extreme conditions, including high radiation fields, temperatures, and pressures, as well coupled effects of two or more of these; (4) Development of resonance ultrasound spectroscopy and direct measurement of aging of plutonium alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coupling of radiation and grain boundary corrosion in SiC

Abstract Radiation and corrosion can be coupled to each other in non-trivial ways and such coupling is of critical importance for the performance of materials in extreme environments. However, it has been rarely studied in ceramics and therefore it is not well understood to what extent these two phenomena are coupled and by what mechanisms. Here, we discover that radiation-induced chemical changes at grain boundaries of ceramics can have a significant (and positive) impact on the corrosion resistance of these materials. Specifically, we demonstrate using a combination of experimental and simulation studies that segregation of C to grain boundaries of silicon carbide leads to improved corrosion resistance. Our results imply that tunning of stoichiometry at grain boundaries either through the sample preparation process or via radiation-induced segregation can provide an effective method for suppressing surface corrosion.

Materials Science↗
DOE OSTI2025

High pressure Raman spectroscopy of boron-rich boron carbides up to 50 GPa

We investigated the effects of increased boron content on the resistance to non-hydrostatic stress-induced (NHSI) local amorphous zones in boron-rich boron carbide (BxC) compounds. Using high-pressure Raman spectroscopy, we subjected B 4.3 C, B 6.4 C, and B 10.4 C to pressures up to 50 GPa and monitored their responses. Our results show that higher boron content delays the onset of NHSI local amorphous zone formation, shifting it from 35 GPa in B 4.3 C to approximately 50 GPa in B 10.4 C. This enhanced resistance is attributed to a reduction in the formation of the B 12 (CCC) polytype, which is susceptible to amorphization, and the greater flexibility of B–B–B chains. Furthermore, alternative mechanisms, such as boron vacancy-driven C–C bond formation, provide additional insights into defect-mediated structural changes that may influence the amorphization process. In conclusion, these findings highlight the dual role of boron content and defect mechanisms in improving the structural stability of B x C materials under extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Positron annihilation spectroscopy of defects in nuclear and irradiated materials- a review

Positron is the only probe that can detect individual atomic vacancies and small and large vacancy clusters induced by irradiation with remarkable sensitivity, providing information about their size, concentration, and chemical environment. The focus of this review article is to provide guidance to facilitate applications of positron annihilation spectroscopy (PAS) in irradiation-induced defect studies to advance the development of new radiation-tolerant materials. The principle of PAS, its techniques, and data analysis methods are described. PAS studies of defects in nuclear and irradiated materials are reviewed and discussed in depth. Future developments to advance PAS applications in nuclear materials research and studies of materials under extreme environments are presented.

Atomic scale defects↗
DOE OSTI2025

Radiation-induced vacancy injection in heterogeneous multiphase materials

Understanding the synergy between corrosion and defect dynamics is a key consideration in the development of advanced materials for extreme environments. Here, we reveal a surprising phenomenon for the transport of point defects induced by irradiation in a heterogeneous multiphase structure of a metal and an oxide, similar to that formed under metal corrosion that takes place in most environments. Despite the confinement of the produced damage within the oxide, vacancies were injected into the unirradiated metal and coarsened with dose. Furthermore, the results show that the nature of the oxide layer dictates the defect evolution in the metal layer. This work reveals an interesting mechanism for point defect interactions in multiphase materials, with broad implications in many fields, while also emphasizing the complex coupling between corrosion and irradiation. Corrosion leads to the formation of multiphase materials, while irradiation enhances diffusion within the heterogeneous phases, which can impact corrosion rates.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials using MALAMUTE

The Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy aims to develop and qualify additively manufactured materials for nuclear applications. One key challenge to this is the microstructural variability observed in the additively manufactured products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-throughput experimental and modeling techniques to accelerate qualification. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the additive manufacturing process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture microstructural variability is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning models to develop a digital twin for additive manufacturing that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation. The melting and subsequent solidification that occurs during the additive process is a complex phenomenon that requires multiscale multiphysics analysis. This work package focuses on understanding the role of process variabilities on the unique microstructural characteristics of additively manufactured materials. Microstructural features at the subgrain level, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. Idaho National Laboratory’s Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for additively manufactured materials in an efficient, reliable, and cost-effective way. This work focuses on capturing the microstructural variabilities at the subgrain level that are often missing in the part-scale models. In fiscal year 2025, we significantly advanced upon our work in the last fiscal year, in terms of the predictive capabilities of the physics-based and ML models, by adding the capabilities to capture subgrain-level micro-segregation during solidification using phase-field model and to predict the time-dependent dynamics of the AM process through the MOGPAR model. The alloy solidification model in MOOSE incorporates the thermodynamic properties and free energy relevant to 316 stainless steel. The model demonstrates the Cr and Ni segregation that occurs during solidification, including that the rate of solidification. The microstructural evolution model is connected to the process conditions via the surrogate model developed in this work. This enables predictions of the final microstructure in conjunctions with the manufacturing process. This work supports AMMT's rapid qualification goals by laying the foundation for an efficient and cost-effective model establishing the PSPP correlation for AM. The generated microstructures and predicted micro-segregation can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work helps to identify the key microstructural features at the subgrain level that are significant in property and performance predictions of additively manufactured components. This work will also provide inputs to the large-scale process variability models to reevaluate and validate assumptions and simplifications made in the part-scale models. Furthermore, through active learning this work can help identify the data need from both modeling and experimental sides for development of a robust digital twin for additive manufacturing and accelerate the AMMT's qualification efforts.

36 - MATERIALS SCIENCE↗
DOE OSTI2023

Design and development of equi-atomic refractory High Entropy Alloys for use in radiation environments

Development of new structural materials that can withstand the extreme environments of nuclear reactors where the materials are exposed to high dose rate of ~ 30 200 dpa, high temperatures of the order of 500 1000 o C and tens of years of operation is vital for exploiting the “smallest carbon footprint energy source” to its fullest, in order to deal with the energy crisis worldwide. Recently, HEAs have shown superior irradiation properties over conventional alloys like higher resistance to defect formation, lower void swelling, limited irradiation hardening and higher microstructural stability under irradiation, making them potential structural material candidates for reactors. Proper characterization and testing of these materials are essential before they can replace the conventional alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗
DOE OSTI2020

Experimental and Computational Studies on Superhard Material Rhenium Diboride under Ultrahigh Pressures

An emerging class of superhard materials for extreme environment applications are compounds formed by heavy transition metals with light elements. In this work, ultrahigh pressure experiments on transition metal rhenium diboride (ReB 2 ) were carried out in a diamond anvil cell under isothermal and non-hydrostatic compression. Two independent high-pressure experiments were carried out on ReB 2 for the first time up to a pressure of 241 GPa (volume compression V/V 0 = 0.731 ± 0.004), with platinum as an internal pressure standard in X-ray diffraction studies. The hexagonal phase of ReB 2 was stable under highest pressure, and the anisotropy between the a-axis and c-axis compression increases with pressure to 241 GPa. The measured equation of state (EOS) above the yield stress of ReB 2 is well represented by the bulk modulus K 0 = 364 GPa and its first pressure derivative K 0 ´ = 3.53. Corresponding density-functional-theory (DFT) simulations of the EOS and elastic constants agreed well with the experimental data. DFT results indicated that ReB 2 becomes more ductile with enhanced tendency towards metallic bonding under compression. The DFT results also showed strong crystal anisotropy up to the maximum pressure under study. The pressure-enhanced electron density distribution along the Re and B bond direction renders the material highly incompressible along the c-axis. Our study helps to establish the fundamental basis for anisotropic compression of ReB 2 under ultrahigh pressures

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-Temperature-Performance Cyanate Ester Composites with Carboranes

Cyanate ester (CE) is an important class of materials among high-temperature performance thermosets. It is used in aerospace launch vehicles, heat sinks, booms, and trusses of satellites etc. due to its high glass transition temperatures (>220°C), excellent thermal stability, and low flammability. Current approaches to improve the thermal stability of CE include incorporation of siloxanes or phosphorus- based flame retardants (PFRs). In this work, we have explored boron-based hydroxy (PD) and epoxy (EP) functionalized carborane additives to improve the thermal properties of CE. Carborane fillers were solvent blended at various mass loadings in the resin and cured to study their effect on thermal properties. PD and EP carboranes react with CE to form iminocarbonate and oxazolidinone linkages respectively. Cure kinetics studies at different wt% loadings explained that carboranes catalyze the curing reaction by reducing curing activation energy by about 54% and 26% for 10 wt% loadings of PD and EP carboranes respectively. In addition, carborane-filled cyanate ester (CE) nanocomposites demonstrate an exceptionally high thermal stability as compared to the pristine resin in air and inert environment. Our thermogravimetric analysis (TGA) experiments show that the ultimate char yield of the resin can be increased from 0% to as high as 76% and 82% with 30 wt% PD and EP carborane loading respectively at 1000 °C in air. The initial degradation temperature T d;5 of the composites decreased with increasing carborane loadings in both air and argon. For instance, T d;5 for CE was 465 and 471.6 °C in argon and air while that for P20 was 437.4 and 452.1 °C. Modulated TGA studies gave evidence of the effect of carboranes on degradation kinetics and the mechanism of the resin in air and inert environment. The effect of bonding between carboranes and CE at various loadings on the thermal expansion of the matrix was also studied using Thermomechanical Analyzer (TMA). PD carborane reduced the T g for P20 to about 225 °C while CE had T g >350 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Ultrafine-grained Al-Mg-Zr Alloy Processed by Shear-Assisted Extrusion with High Thermal Stability

Shear-assisted processing and extrusion (ShAPE) is used to consolidate Al-4.2Mg-1.6Zr-0.25Si-0.1Fe (wt.%) powders. The extruded alloy exhibits: (i) near-zero porosity; (ii) an attractive combination of tensile yield strength (~220 MPa), ultimate strength (330 MPa) and elongation (>20%); and (iii) excellent thermal stability at 400 ºC. The ultra-fine grain size in the gas-atomized powders – created and stabilized by primary L12-Al3Zr submicron precipitates - is maintained after consolidation and subsequent exposure at 400 ºC, contributing twice as much to strength as Mg in solid-solution. Electron microscopy reveals the microstructure of the L12-Al3Zr precipitates, and other precipitates (Mg2Si and Al3Fe) and dispersoids (oxides).

ShAPE, Friction Extrusion, aluminum powder, Shear ↗
DOE OSTI2026

Radiation damage in tungsten under high-energy He-ion irradiation

Tungsten (W) is a primary candidate for plasma-facing components (PFCs) in fusion reactors and hybrid fission-fusion reactors. However, W suffers from severe microstructural damage under Helium (He) ion irradiation in operation conditions. Key irradiation parameters, including incident ion energy, fluence, and irradiation temperature, are known to determine the formation of He bubbles and dislocation loops, which ultimately lead to the degradation of mechanical properties, such as radiation hardening and ductile-to-brittle transition temperature (DBTT) shifts. Various strategies, for instance, interface engineering and alloying, have been developed to enhance He irradiation tolerance in W to address these challenges. Studies in the recent two decades have enhanced the understanding of the underlying mechanisms that drive defect evolution and mechanical performance degradation, thereby offering more insights into the design of more resilient W-based materials for extreme environments.

Liu, Yinghang [Purdue University]↗
DOE OSTI2022

Investigating laser ablated plume dynamics of carbon and aluminum targets

Recently acquired high-resolution images of nanosecond laser ablation plumes suggest a strong correlation between the internal plume structure and the type of material being ablated. However, the details of this relation are currently not well understood. In this work, we attempt to explore this correlation using a 2D radiation hydrodynamics model to study the dependence of internal plume structure formation on the ablation material. Here spatio-temporal emission maps and plume expansion velocities from experimental measurements are compared with the model predictions, including synthetic emission maps. The shape and expansion rate of an outer air plume region are found to be in good agreement for both carbon and aluminum, as are the inner material plume dynamics for carbon ablation. The largest disagreement is observed in the case of a polished aluminum target, where the chaotic inner plume features seen in the experimental images are not observed in the model. The possible physical mechanisms responsible for this discrepancy are discussed. This effort constitutes a continued development toward a predictive model of ablation plume dynamics and chemistry for various materials in extreme environments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Tiny Bubbles: Combined HR(S)TEM and 4D-STEM Analysis of Sub-Nanometer He Bubbles in Au

Irradiation produces a distribution of defect sizes in materials, with the smallest defects often below one nanometer in size and approaching the scale of a single unit cell in metals. While high-resolution scanning transmission electron microscopy (STEM)-based imaging can directly image structures at this level, techniques such as four-dimensional STEM (4D-STEM) enable characterization of materials across large fields of view, capturing a more representative volume that can be valuable for quantifying defects, their distributions, and the associated strain fields. Here we present a combined HRSTEM and 4D-STEM approach to study the model system of He bubble implantation in an Au thin film. The present work is of general interest for the study of materials in extreme environments, as it demonstrates an effective way to characterize even the tiniest sub-nanometer sized He bubbles in addition to larger irradiation defects.

atomic-resolution STEM↗