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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Complex Oxides under Simulated Electric Field: Determinants of Defect Polarization in AB O 3 Perovskites

Abstract Polarization of ionic and electronic defects in response to high electric fields plays an essential role in determining properties of materials in applications such as memristive devices. However, isolating the polarization response of individual defects has been challenging for both models and measurements. Here the authors quantify the nonlinear dielectric response of neutral oxygen vacancies, comprised of strongly localized electrons at an oxygen vacancy site, in perovskite oxides of the form AB O 3 . Their approach implements a computationally efficient local Hubbard U correction in density functional theory simulations. These calculations indicate that the electric dipole moment of this defect is correlated positively with the lattice volume, which they varied by elastic strain and by A‐site cation species. In addition, the dipole of the neutral oxygen vacancy under electric field increases with increasing reducibility of the B‐site cation. The predicted relationship among point defect polarization, mechanical strain, and transition metal chemistry provides insights for the properties of memristive materials and devices under high electric fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure‐Dependent Sodium Storage Mechanisms in Hard Carbon Anodes

Sustainable energy storage is essential to support the transition to renewables and meet the increasing demand for energy. Sodium‐ion batteries (NIBs) are attractive for grid‐scale energy storage due to the abundance and low cost of sodium, sustainability of other battery components, and electrochemical performance. Hard carbon (HC) is a leading anode material for NIBs, but its complex microstructure complicates the understanding of sodium storage mechanisms. Using X‐ray total scattering and density functional theory calculations, this study clarifies how HC's microstructural variations influence sodium storage across the slope (high potential) and plateau (low potential) regions of the potential capacity curve. In the slope region, sodium initially adsorbs at high‐binding energy defect sites and subsequently intercalates between graphene layers, adsorbing at low‐binding energy defect sites, correlating with different slopes observed during initial sodiation. Initial irreversibility arises from sodium trapping at surface defects and solid electrolyte interface formation. In the plateau region, sodium simultaneously intercalates and fills pores, influenced by pore size, interlayer spacing, and defect concentration. HCs with larger pore sizes form larger sodium clusters. In conclusion, the proposed mechanism underscores the role of microstructure engineering in enhancing HC performance and advancing NIBs for grid‐scale energy storage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Theoretical insights into the surface physics and chemistry of redox-active oxides

Redox active oxides are ubiquitous in materials applications, including catalysis, photovoltaics, self-cleaning glasses, chemical sensors, and electronic components. Their utility derives from their unique ability to access multiple metal charge states within a finite energy window. However, this property also confounds our ability to study reducible oxides, because it leads to structural, compositional, and electronic complexities that elude simplistic models of materials structure and function. Oxygen vacancies play a critical role in shaping the functional properties of such oxides; most notably, they lead to mobile charge imbalances that impact surface processes at significant distances from the originating defect. Atomistic simulations are inherently equipped to illuminate these phenomena at a fundamental level; however, reducible oxides pose great challenges due to the high level of electron correlation needed to correctly describe them. Knowing how defects form, couple, propagate, agglomerate, or repel each other and influence the surface properties of reducible oxides is only now coming into the grasp of modern theory and simulation capabilities. This knowledge is also key to discovering and controlling emergent materials processes at nanometer scale and beyond. The manuscript was written with support of all three authors from U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. Work by RR and VG was performed at Pacific Northwest National Laboratory (PNNL), which is a multi-program laboratory operated by Battelle for DOE under Contract DE AC05 76RL01830. AS was supported under Award DE-SC0007347.

Rousseau, Roger J.↗
DOE OSTI2021

Normal and anomalous self-healing mechanism of crystalline calcium silicate hydrates

The origin of different stability of crystalline calcium silicate hydrates was investigated. The tobermorite crystal has been used as an analog of cement hydrate that is being mostly manufactured material on earth. Normal tobermorite is thermally unstable and transforms to amorphous at low pressure. Meanwhile, anomalous tobermorite with high Al content does not significantly transform under high pressure or high temperature. Conducted X-ray absorption spectroscopy explains the weak stability of normal tobermorite which was originally hypothesized by the role of zeolitic Ca ions in the cavities of silicate chains. Atomic simulations reproduced the experimentally observed trend of pressure behavior once the ideal structures were modified to account for the Al content as well as the chain defects. The simulations also suggested that the stability of tobermorite under stress could be rationalized as a self-healing mechanism in which the structural instabilities were accommodated by a global sliding of the CaO layer.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
DOE OSTI2022

Physics-based model of irradiation creep for ferritic materials under fusion energy operation conditions

Irradiation creep is known to be an important process for structural materials in nuclear environments, potentially leading to creep failure at temperatures where thermal creep is generally negligible. While there is a great deal of data for irradiation creep in steels and zirconium alloys in light water reactor conditions, much less is known for first wall materials under fusion energy conditions. Lacking suitable fusion neutron sources for detailed experimentation, modeling, and simulation can help bridge the dose-rate and spectral-effects gap and produce quantifiable expectations for creep deformation of first wall materials under standard fusion conditions. In this paper, we develop a comprehensive model for irradiation creep created from merging a crystal plasticity representation of the dislocation microstructure and a defect evolution simulator that accounts for the entire cluster dimensionality space. Both approaches are linked by way of a climb velocity that captures dislocation-biased defect absorption and a dislocation strengthening term that reflects the accumulation of defect clusters in the system. Here, we carry out our study in Fe under first wall fusion reactor conditions, characterized by a fusion neutron spectrum with average recoil energies of 20 keV and a damage dose rate of ≈3×10 –7 dpa/s at temperatures between 300 and 800 K.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Irradiation creep model for α-Fe: Dislocation climb in the presence of stress fields (Final Report)

Irradiation creep is known to be an important process for structural materials in nuclear environments, potentially leading to creep failure at temperatures where thermal creep is generally negligible. While there is a great deal of data for irradiation creep in steels and zirconium alloys in light water reactor conditions, much less is known for first wall materials under fusion energy conditions. Lacking suitable fusion neutron sources for detailed experimentation, modeling, and simulation can help bridge the dose-rate and spectral-effects gap and produce quantifiable expectations for creep deformation of first wall materials under standard fusion conditions. In this work, we present a comprehensive model for irradiation creep created from merging a crystal plasticity representation of the dislocation microstructure and a defect evolution simulator that accounts for the entire cluster dimensionality space. Both approaches are linked by way of a climb velocity that captures dislocation-biased defect absorption and a dislocation strengthening term that reflects the accumulation of defect clusters in the system. We carry out our study in Fe under first wall fusion reactor conditions, characterized by a fusion neutron spectrum at temperatures between 300 and 800°K.

36 MATERIALS SCIENCE↗
NASA NTRS1977

Effect of preload on the fatigue and static strength of composite laminates with defects

The effect of a preload cycle on the structural performance of three graphite/epoxy composite laminates was studied. The layups studied were a laminate typical of general purpose structures (L1), a laminate representative of a filament wound tank (L2), and a laminate representative of turboengine fan blades. The effects of three sizes of simulated initial defects were studied. The tests developed static strength data, fatigue to failure data, and residual static data after application of a predetermined number of fatigue cycles. For L1 specimens, there was a slight trend for the static strength to be greater for preloaded specimens. After application of cyclic loading, however, the influence of preloading was insignificant. In L2 and L3 specimens there was no consistent difference in the static or fatigue results between preloaded and nonpreloaded specimens.

Porter, T. R.↗
NASA NTRS2002

Evolutionary Design of a Robotic Material Defect Detection System

During the post-flight inspection of SSME engines, several inaccessible regions must be disassembled to inspect for defects such as cracks, scratches, gouges, etc. An improvement to the inspection process would be the design and development of very small robots capable of penetrating these inaccessible regions and detecting the defects. The goal of this research was to utilize an evolutionary design approach for the robotic detection of these types of defects. A simulation and visualization tool was developed prior to receiving the hardware as a development test bed. A small, commercial off-the-shelf (COTS) robot was selected from several candidates as the proof of concept robot. The basic approach to detect the defects was to utilize Cadmium Sulfide (CdS) sensors to detect changes in contrast of an illuminated surface. A neural network, optimally designed utilizing a genetic algorithm, was employed to detect the presence of the defects (cracks). By utilization of the COTS robot and US sensors, the research successfully demonstrated that an evolutionarily designed neural network can detect the presence of surface defects.

Ballard, Gary↗
DOE OSTI2022

Kinetic Model of Acid Gas Induced Defect Propagation in Zeolitic Imidazolate Frameworks

Understanding the degradation of nanoporous materials under exposure to common acid gas contaminants (e.g., SO 2 , CO 2 , NO 2 , and H 2 S) is essential to elongate their lifetime and thus enable their practical applications in separations and catalysis. Previous theoretical investigations have focused on the formation of isolated point defects, which are insufficient to provide direct insights into the long-term evolution of the bulk properties of materials such as zeolitic imidazolate frameworks (ZIFs) under sustained acid gas exposure. Here, to bridge this divide in both length and time scales, we developed a first-principles lattice-based kinetic model to simulate the defect propagation and bulk material breakdown in ZIFs. This model closely reproduces the experimentally measured macroscopic evolution of the time-dependent bulk materials proprieties and also yields important new insights regarding the autocatalytic nature of ZIF degradation and the spatial distribution of defects. Our results suggest new experimental directions to identify nascent defect clusters in degraded ZIFs and avenues to mitigate degradation under challenging conditions of acid gas exposure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

Experimental Calibration of a Numerical Model of Prepreg Tack for Predicting AFP Process Related Defects

Wrinkles, puckers, and fiber bridging are among the major defects encountered in the Automated Fiber Placement (AFP) process, and are all different manifestations of fiber misalignment. The main driver for these defects are the residual stresses introduced in the tow during the deposition stage by the AFP head. In contrast, the tack between the deposited tape and the substrate is the resisting force against the formation of such defects. Tack may be defined as the ability of a material to form a bond immediately on contact with another surface. Tack is a very complex phenomenon that is influenced by a variety of process parameters including temperature, head pressure and speed, as well as degree of cure, moisture content, and surface roughness A physics-based modeling framework for simulation of tack was developed in this study that allows for prediction of tack response. The developed tack model is incorporated in the AFP placement modelling framework developed to simulate AFP defects.

Forghani, Alireza↗
DOE OSTI2025

Defect modeling in semiconductors: the role of first principles simulations and machine learning

Abstract Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2021

Comparison of Biotite Elastic Properties Recovered by Spherical Nanoindentations and Atomistic Simulations — Influence of Nano-Scale Defects in Phyllosilicates

Phyllosilicate minerals, due to their sheets structure and morphology, are known to cause anisotropy in bulk rock properties and make the bulk rock more compliant. Accurately characterizing the micromechanical behavior of phyllosilicate minerals from laboratory observations, which eventually translates to the bulk rock behavior, is still challenging due to their fine-grained nature. Recent advances in atomistic simulations open the possibility of theoretically investigating such mineral mechanical behavior. We compare the elastic properties of biotites recovered by spherical nanoindentation with those predicted from density functional theory (DFT) simulations to investigate to what extent theoretical predictions reproduce actual phyllosilicate properties. Spherical nanoindentation was conducted using schist rocks from Poorman Formation, South Dakota, USA, to recover continuous indentation stress-strain curves. Loading in the layer-normal orientation shows an average indentation modulus (M) of about 35 GPa, while loading in the layer-parallel orientation gives a higher average of about 95 GPa. To facilitate comparison, the elastic stiffness constants (cij) determined from DFT were converted to indentation modulus (M) using solutions proposed in this study. The majority of the nanoindentation modulus results are below the values inferred from the simulation results representing ideal defect-free minerals. We suggest that crystal defects present at the nano-scale, potentially ripplocations, are the dominant cause of the lower indentation modulus recovered from nanoindentation compared to those inferred from DFT simulations. Results highlight the importance of acknowledging the defects that exist down to the nano-scale as it modifies the mechanical properties of phyllosilicates compared to its pure defect-free form.

58 GEOSCIENCES↗
DOE OSTI2022

Elucidating size effects on the yield strength of single-crystal Cu via the Richtmyer–Meshkov instability

Capturing the dynamic response of a material under high strain-rate deformation often demands challenging and time consuming experimental effort. While shock hydrodynamic simulation methods can aid in this area, a priori characterizations of the material strength under shock loading and spall failure are needed in order to parameterize constitutive models needed for these computational tools. Moreover, parameterizations of strain-rate-dependent strength models are needed to capture the full suite of Richtmyer–Meshkov instability (RMI) behavior of shock compressed metals, creating an unrealistic demand for these training data solely on experiments. Herein, we sweep a large range of geometric, crystallographic, and shock conditions within molecular dynamics (MD) simulations and demonstrate the breadth of RMI in Cu that can be captured from the atomic scale. In this work, yield strength measurements from jetted and arrested material from a sinusoidal surface perturbation were quantified as Y RMI = 0.787 ± 0.374 GPa, higher than strain-rate-independent models used in experimentally matched hydrodynamic simulations. Defect-free, single-crystal Cu samples used in MD will overestimate Y RMI , but the drastic scale difference between experiment and MD is highlighted by high confidence neighborhood clustering predictions of RMI characterizations, yielding incorrect classifications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Simulation of the Impact of Point Defects and Edge Dislocations on X-Ray Diffraction in Hexagonal (Ni,Co) 1+2 x Ti 1– x O 3 Thin Films

In this work, a computer code for simulating high-resolution X-ray diffraction (XRD) data from disordered crystals with arbitrary spatial composition and local lattice parameters is developed. Simulated patterns are compared with the experimental data collected on a single phase, highly crystalline (Ni 0.42 Co 0.58 ) 2.22 Ti 0.39 O 3 solid-solution thin film exhibiting a large number of subsidiary minima. As a case study, the edge dislocations in hexagonal (Ni,Co) 3 O 3 thin films with Burgers vector parallel and perpendicular to the a-axis and c-axis, respectively, are modeled. No peak profiles are assumed and thus issues related to profile fitting are avoided. Both macroscopic features, such as film thickness, and atomic-scale structure, such as dislocations, are simultaneously modeled. Simulations are run on a desktop machine. Commonly applied database-based phase identification routines can result in wrong phase identification, unless intensities are properly modeled. Modeling tools of diffractometer manufacturers are compared with the present approach. An example of how simulated reciprocal lattice patterns can be used to choose relevant measurement geometries in defected thin films is given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Revealing a Pathway for Low-Temperature Recrystallization in Germanium

Thermally activated annealing in semiconductors faces inherent limitations, such as dopant diffusion. Here, a nonthermal pathway is demonstrated for a complete structural restoration in predamaged germanium via ionization-induced recovery. By combining experiments and modeling, this study reveals that the energy transfer of only 2.4 keV nm −1 from incident ions to target electrons can effectively annihilate pre-existing defects and restore the original crystalline structure at room temperature. Moreover, it is revealed that the irradiation-induced crystalline-to-amorphous (c/a) transformation in Ge is reversible, a phenomenon previously considered unattainable without additional thermal energy imposed during irradiation. For partially damaged Ge, the overall damage fraction decreases exponentially with increasing fluence. Surprisingly, the recovery process in preamorphized Ge starts with defect recovery outside the amorphous layer and a shrinkage of the amorphous thickness. After this initial stage, the remaining damage decreases slowly with increasing fluence, but full restoration of the pristine state is not achieved. These differences in recovery are interpreted in the framework of structural differences in the initial defective layers that affect recovery kinetics. This study provides new insights on reversing the c/a transformation in Ge using highly-ionizing irradiation and has broad implications across materials science, radiation damage mitigation, and fabrication of Ge-based devices.

athermal recovery↗
DOE OSTI2026

Formation of carbon homonuclear bonds in β-SiC under neutron irradiation at various temperatures and neutron doses

To elucidate radiation defect processes in SiC, Raman spectroscopy was systematically applied to high-purity, polycrystalline β-SiC that was neutron irradiated at a range of temperature and dose conditions. The analysis specifically focused on formation of carbon homonuclear bonds by irradiation; these bonds were indicated by D and G bands and amorphous carbon peaks. Intensity of the carbon peaks relative to SiC peaks significantly decreased in the case of high temperature and/or high neutron dose of 500 °C to 29 displacements per atom (dpa) and about 800 °C to 1.38 and 29 dpa. The absence of carbon bond peaks under those conditions was explained by growth of stoichiometric defect clusters, consistent with previous atomistic simulations on SiC defect stability. The lack of Raman bands associated with carbon clusters under high-temperature and high-dose radiation conditions accounts for the resistance of SiC to phase separation under irradiation. The findings further suggest that material compositions and chemical properties that are inherently resistant to chemical disordering under high-dose radiation conditions are indicative of the long-term durability of ceramic compounds in radiation environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ionization-driven competitive (recovery) process in pre-damaged KTaO 3 : A brief review

The nuclear (S n ) and electronic (S e ) energy dissipation processes have been considered to be independent and largely uncorrelated, influencing our understanding of ion–solid interaction and damage processes in the last decades. Recently, however, it has become more generally accepted that S n and S e are coupled as they interact both in time and space. To decouple these processes, separating these effects in experiments using sequential dual-beam irradiations have become accepted as the logical path to advance the understanding of complex interactions between S e and pre-existing defects that may be created from displacement events. This experimental approach has been recently applied to studies of KTaO 3 to reveal new insights into this critical research topic. Here, we offer a forward-looking and comprehensive perspective on the fundamental coupling between Se and pre-existing defects in KTaO 3 . The origins behind the competitive two-stage phase transition process leading to damage healing are revealed and discussed. Furthermore, the evidence resulting from synergistic effects is also included for comparison. Additionally, our findings are rationalized using both Se and the ion velocity as key parameters. We highlight how the inelastic thermal spike (i-TS) calculations provide insights into the nature of this coupled process and further confirm that the ion velocity effect governs annealing kinetics. This work emphasizes that through the introduction of a small amount of local disorder in materials, MeV ion irradiation (i.e., not extreme S e ) may also be one additional option in subsequent material modification and functionalization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗