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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Local p‐ and n‐Type Doping of an Oxide Semiconductor via Electric‐Field‐Driven Defect Migration

Layered oxides exhibit high ionic mobility and chemical flexibility, attracting interest as cathode materials for lithium-ion batteries and the pairing of hydrogen production and carbon capture. Recently, layered oxides emerged as highly tunable semiconductors. For example, by introducing anti-Frenkel defects, the electronic hopping conductance in hexagonal manganites is increased locally by orders of magnitude. Here, local acceptor and donor doping in Er(Mn,Ti)O3 is demonstrated, facilitated by the controlled splitting of anti-Frenkel defects under applied d.c. voltage. By combining density functional theory calculations, scanning probe microscopy, atom probe tomography, and scanning transmission electron microscopy, it is shown that the oxygen defects can readily be moved through the layered crystal structure, leading to nano-sized interstitial-rich (p-type) and vacancy-rich (n-type) regions. The resulting pattern is comparable to dipolar npn-junctions and stable on the timescale of days. These findings reveal the possibility of temporarily functionalizing oxide semiconductors at the nanoscale, giving additional opportunities for the field of oxide electronics and the development of transient electronics in general.

He, Jiali↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2023

Automatic Determination of the Weak-Beam Condition in Dark Field X-ray Microscopy

Mechanical properties in crystals are strongly correlated to the arrangement of 1D line defects, termed dislocations. Recently, Dark field X-ray Microscopy (DFXM) has emerged as a new tool to image and interpret dislocations within crystals using multidimensional scans. However, the methods required to reconstruct meaningful dislocation information from high-dimensional DFXM scans are still nascent and require significant manual oversight (i.e. supervision). In this work, we present a new relatively unsupervised method that extracts dislocation-specific information (features) from a 3D dataset (x, y, Φ) using Gram-Schmidt orthogonalization to represent the large dataset as an array of 3-component feature vectors for each position, corresponding to the weak-beam conditions and the strong-beam condition. Finally, this method offers key opportunities to significantly reduce dataset size while preserving only the crystallographic information that is important for data reconstruction.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Single-atom-layer traps in a solid electrolyte for lithium batteries

In order to fully understand the lithium-ion transport mechanism in solid electrolytes for batteries, not only the periodic lattice but also the non-periodic features that disrupt the ideal periodicity must be comprehensively studied. At present only a limited number of non-periodic features such as point defects and grain boundaries are considered in mechanistic studies. Here, we discover an additional type of non-periodic feature that significantly influences ionic transport; this feature is termed a “single-atom-layer trap” (SALT). In a prototype solid electrolyte Li 0.33 La 0.56 TiO 3 , the single-atom-layer defects that form closed loops, i.e., SALTs, are found ubiquitous by atomic-resolution electron microscopy. According to ab initio calculations, these defect loops prevent large volumes of materials from participating in ionic transport, and thus severely degrade the total conductivity. This discovery points out the urgency of thoroughly investigating different types of non-periodic features, and motivates similar studies for other solid electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2022

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components (Final Technical Report)

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study. In another approach, we investigate Thermal Tomography (TT), which is a computational method for reconstruction of depth profile of internal material defects from PIT nondestructive evaluation (NDE). TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of subsurface internals defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. Performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shape imported from scanning electron microscopy (SEM) images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on TT images of elliptical defects is capable of classifying shape and orientation of irregular defects. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN. We also investigate several approaches based on Gaussian Random Circle and Bezier Curves for constructing parametric models of irregular-shape defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nanoscale electronic inhomogeneities in 1⁢ T ⁢-TaS 2

Here, we report a set of scanning tunneling microscopy (STM) and spectroscopy (STS) experiments studying native defects in CVT grown 1⁢T–TaS 2 . Six different sample surfaces from four bulk crystals were investigated. Wide area imaging reveals a prevalence of nanometer-scale electronic inhomogeneities due to native defects, with pristine regions interspersed. These inhomogeneities appear in typical as-grown crystals and coexist with a well-formed commensurate charge density wave of 1T–TaS 2 at low temperatures. Electronic inhomogeneities show up both as variations in the apparent height in STM and in the local density of states in STS; the bands can shift by 60 meV and the gap varies by more than 100 meV across inhomogeneities. These inhomogeneities are present in similar concentration across large-scale areas of all samples studied, but do not influence the charge density wave formation on local or global scales. The commensurate charge density wave exhibits long-range order and remains locally intact in the presence of these inhomogeneities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

GeSn-on-GaAs with photoconductive carrier lifetime >400 ns: role of substrate orientation and atomistic simulation

Group IV GeSn quantum material finds application in electronics and silicon-compatible photonics. Synthesizing these materials with low defect density and high carrier lifetime is a potential challenge due to lattice mismatch induced defects and tin segregation at higher growth temperature. Recent advancements in the growth of these GeSn materials on Si, Ge, GaAs, and with substrate orientations, demonstrated different properties using epitaxial and chemical deposition methods. This article addresses the effect of GaAs substrate orientation and misorientation on the materials’ properties and carrier lifetimes in epitaxial Ge 0.94 Sn 0.06 layers. With starting GaAs substrates of (100)/2°, (100)/6°, (110) and (111)A orientations, Ge 0.94 Sn 0.06 epitaxial layers were grown with an intermediate Ge buffer layer by molecular beam epitaxy and analyzed by several analytical tools. X-ray analysis displayed good crystalline quality, and Raman spectroscopy measurements showed blue shifts in phonon wavenumber due to biaxial compressive strain in Ge 0.94 Sn 0.06 epilayers. Cross-sectional transmission electron microscopy analysis confirmed the defect-free heterointerface of Ge 0.94 Sn 0.06 /Ge/GaAs heterostructure. Minority carrier lifetimes of the unintentionally doped n-type Ge 0.94 Sn 0.06 epilayers displayed photoconductive carrier lifetimes of >400 ns on (100)/6°, 319 ns on (100)/2°, and 434 ns on (110) GaAs substrate at 1500 nm excitation wavelength. On the other hand, Ge 0.94 Sn 0.06 layer showed poor carrier lifetime on (111)A GaAs substrate. The observed differences in carrier lifetimes were correlated with the formation energy of the Ge on (100)/6° and (100)/2° GaAs heterointerface using Stillinger–Weber interatomic potential model-based atomistic simulation with different heterointerfacial bonding by Synopsys QuantumATK tool. Total energy computation of 6280-atom Ge/GaAs supercell on (100)/6° leads to lower formation energy than (100)/2°, making it more thermodynamically stable. Hence, the growth of the GeSn/III–V material system using misoriented (100) substrates that are more thermodynamically stable will enhance the performances of optoelectronic devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Impact of dual-layer solid-electrolyte interphase inhomogeneities on early-stage defect formation in Si electrodes

While intensive efforts have been devoted to studying the nature of the solid-electrolyte interphase (SEI), little attention has been paid to understanding its role in the mechanical failures of electrodes. Here we unveil the impact of SEI inhomogeneities on early-stage defect formation in Si electrodes. Buried under the SEI, these early-stage defects are inaccessible by most surface-probing techniques. With operando full field diffraction X-ray microscopy, we observe the formation of these defects in real time and connect their origin to a heterogeneous degree of lithiation. This heterogeneous lithiation is further correlated to inhomogeneities in topography and lithium-ion mobility in both the inner- and outer-SEI, thanks to a combination of operando atomic force microscopy, electrochemical strain microscopy and sputter-etched X-ray photoelectron spectroscopy. Our multi-modal study bridges observations across the multi-level interfaces (Si/LixSi/inner-SEI/outer-SEI), thus offering novel insights into the impact of SEI homogeneities on the structural stability of Si-based lithium-ion batteries. Severe structural deformation during (de)lithiation is the main factor limiting the stability of Si anodes in Li-ion batteries. Here, a multi-modal approach is used to visualize these deformations in their early-stage and link them to inhomogeneities in the dual-layer solid-electrolyte interphase.

25 ENERGY STORAGE↗
DOE OSTI2020

Impact of Individual Structural Defects in GaAs Solar Cells: A Correlative and In Operando Investigation of Signatures, Structures, and Effects

Defects usually degrade device performance. Thus, many techniques and effort are devoted to studying semiconductor defects. However, it is rarely known: i) how individual defects affect device performance; ii) how the impact depends on the device operating conditions; iii) how the impact varies from one defect to another; and iv) how these variations are correlated to the microscopic-scale defect structure. To address these crucial questions, an array of correlative and spatially resolved techniques, including electroluminescence, photoluminescence, Raman, I–V characteristics, and high-resolution electron microscopy, are used to characterize dislocation defects in GaAs solar cells. Significantly, the study is carried out in a series mode and in operando. This approach provides quantitative and definitive correlation between the atomistic structure of defects and their explicit effects on device performance, thus giving unprecedented insight into defect physics.

dislocation defects↗
DOE OSTI2022

Thermal conductivity evaluation of ion irradiated Si 3 N 4 and ZrN ceramics using spatial domain thermoreflectance

Nitride ceramics have been investigated for different applications in the nuclear industry, such as space nuclear power, fusion reactor diagnostics and plasma heating, inert matrix fuels, and accident tolerant fuels. Although thermal conductivity remains one of the most important properties to track following irradiation, traditional techniques such as laser flash and xenon flash are limited to bulk sample characterization, which requires lengthy and cost-consuming neutron irradiation. This work used spatial domain thermoreflectance (SDTR) for the micrometer-scale measurement of thermal conductivity in 15 MeV Ni ion-irradiated silicon nitride and zirconium nitride from 1 to 50 dpa and 300 to 700 °C. Here, the SDTR-measured unirradiated thermal conductivity was found to be consistent with the published data on bulk samples. Electrically conductive ZrN exhibits modest reduction after irradiation which is minimal at the highest irradiation temperatures. In electrically insulating Si 3 N 4 , the reduction is more significant and unlike ZrN, the reduction remains significant even at a higher irradiation temperature. The thermal resistance evolution following irradiation was compared with lattice swelling, which was determined using grazing incidence x-ray diffraction, and radiation-induced defects were observed using transmission electron microscopy. A saturation value was observed between 15 and 50 dpa for thermal conductivity degradation in both nitride ceramics and a direct correlation with high-temperature defect recombination was observed, as well as the potential presence of additional carrier scattering mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Inferring relative dose-dependent color center populations in proton irradiated thoria single crystals using optical spectroscopy

In this work, we have utilized photoluminescence spectroscopy and optical ellipsometry to characterize the dose-dependence of the photoluminescence emission intensity and changes in optical absorption of thoria single crystals subject to irradiation with energetic protons at room- and elevated-temperatures. The photoluminescence peaks and the optical absorption bands are attributed to creation of new electronic states emerging from defects resulting from displacement damage. These bands are most likely associated with electrons trapped at the oxygen vacancy sites similar to color centers formed in other irradiated oxides and halides. Our experimental observations are supported by a standard density functional theory calculation of the electronic structure in pristine and oxygen vacancy-bearing thoria crystals. The dose-dependence of the intensity of the photoluminescence peaks is used to parameterize a rate theory model that estimates the concentration of color centers in the irradiated crystals. This parameterization provides optimized migration barrier parameters for oxygen interstitials and vacancies that simultaneously capture the optical response of the crystals irradiated at room- and elevated-temperature. These optical spectroscopy techniques offer a promising pathway to characterize the population of color centers formed at the sites of oxygen anion vacancies, particularly in irradiated nuclear fuels, where atomic-level defects cannot be readily imaged using electron microscopy. When combined with other direct and indirect characterization tools, our approach can provide new insight into defect formation and accumulation in energy materials over single atomic to extended length scales.

36 MATERIALS SCIENCE↗
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 OSTI2023

Simultaneous bright- and dark-field X-ray microscopy at X-ray free electron lasers

Abstract The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally speaking, such tools cannot probe the structural dynamics in a way that is representative of bulk behavior. Synchrotron X-ray diffraction based imaging has long mapped the deeply embedded structural elements, and with enhanced resolution, dark field X-ray microscopy (DFXM) can now map those features with the requisite nm-resolution. However, these techniques still suffer from the required integration times due to limitations from the source and optics. This work extends DFXM to X-ray free electron lasers, showing how the $$10^{12}$$ 10 12 photons per pulse available at these sources offer structural characterization down to 100 fs resolution (orders of magnitude faster than current synchrotron images). We introduce the XFEL DFXM setup with simultaneous bright field microscopy to probe density changes within the same volume. This work presents a comprehensive guide to the multi-modal ultrafast high-resolution X-ray microscope that we constructed and tested at two XFELs, and shows initial data demonstrating two timing strategies to study associated reversible or irreversible lattice dynamics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Realignment and suppression of charge density waves in the rare-earth tritellurides 𝑅⁢Te 3 (𝑅 = La, Gd, Er)

The rare-earth tritellurides have a rich phase diagram that includes charge density waves (CDWs), superconductivity, and magnetic order, offering a platform to study the interplay between these phases on a square-net system. Prior studies have shown that defects can affect the CDW characteristics in these materials, yet coupling between the CDW order and the underlying microstructure has not been studied at the nanoscale. Here we use scanning transmission electron microscopy at cryogenic temperatures to directly visualize the effects of defects on the CDW order and provide a spatially resolved microscopic correlation between the CDW transition and structural defects. Here, we show that in the presence of extended defects, such as dislocations and stacking faults, the weak orthorhombicity of the rare-earth tritellurides is lost and the material becomes pseudotetragonal. Since the orthorhombicity acts as a symmetry breaking field for the CDW transitions in rare-earth tritellurides, the presence of these extended defects modulates the energetics of the CDWs and suppresses the ground-state CDW phase at low temperature.

Charge density waves↗
DOE OSTI2021

Proton-Radiation Tolerant All-Perovskite Multijunction Solar Cells

Radiation-resistant but cost-efficient, flexible, and ultralight solar sheets with high specific power (W g -1 ) are the "holy grail" of the new space revolution, powering private space exploration, low-cost missions, and future habitats on Moon and Mars. Herein, this study investigates an all-perovskite tandem photovoltaic (PV) technology that uses an ultrathin active layer (1.56 micrometers) but offers high power conversion efficiency, and discusses its potential for high-specific-power applications. This study demonstrates that all-perovskite tandems possess a high tolerance to the harsh radiation environment in space. The tests under 68 MeV proton irradiation show negligible degradation (<6%) at a dose of 10 13 p + cm -2 where even commercially available radiation-hardened space PV degrade >22%. Using high spatial resolution photoluminescence (PL) microscopy, it is revealed that defect clusters in GaAs are responsible for the degradation of current space-PV. By contrast, negligible reduction in PL of the individual perovskite subcells even after the highest dose studied is observed. Studying the intensity-dependent PL of bare low-gap and high-gap perovskite absorbers, it is shown that the V OC , fill factor, and efficiency potentials remain identically high after irradiation. Radiation damage of all-perovskite tandems thus has a fundamentally different origin to traditional space PV.

14 SOLAR ENERGY↗
DOE OSTI2023

Cladding and Butt-Joining Dissimilar Aluminum Alloys Simultaneously via Shear Assisted Processing and Extrusion

Shear assisted processing and extrusion has been used to fabricate multi-metallic tubing that incorporates regions with clad and butt-joined structures. In this work, aluminum alloys 6061, 7075, and 1100 are combined to create a monolithic extruded tubing with weldable ends (6061), a high strength center (7075), and a corrosion-resistant cladding (1100). Optical microscopy shows a gradual, defect-free, transition between alloys at the butt-joint interfaces and a sharp transition between alloys at the clad interface. Tensile testing proved the mechanical coherency of the joint, with failure occurring outside the tri-metallic structure. In conclusion, smoothed particle hydrodynamic simulations identified a spiral pattern of the cladding alloy that leads to inconsistent cladding thickness, and identified a simplified 2-alloy combination of aluminum alloys 6082 and 2014 that accomplished the design objectives of the tri-metallic structure.

Coatings↗
DOE OSTI2020

In Situ Micro-Pillar Compression to Examine Radiation-Induced Hardening Mechanisms of FeCrAl Alloys

Here, the effects of 5 MeV Fe 2+ ion irradiation at 300°C on the microstructure evolution and deformation behavior of a FeCrAl C26M alloy are presented. It has been found that dislocation loop density increases an order of magnitude from 1 dpa to 16 dpa irradiations, whereas, the dislocation loop size saturates with increasing damage. Micropillars, 600 nm in diameter and 1.3 µm in height, were fabricated and compressed inside grains with <001>, <011> and <111> crystallographic orientations, respectively. {112} <111> has been identified as the primary slip system in both unirradiated and irradiated alloy. The increase in yield stress after irradiation is observed with measurable variation along <001> and <011> vs. along <111>. By applying the Orowan dispersed barrier model, the increase of yield stress is found mainly due to the slip resistance of radiation generated defect loops. Detailed transmission electron microscopy (TEM) studies were performed to quantify the Burgers vector and the distribution of irradiation induced dislocations at elevated strains. It is revealed that localized shear instability is caused by avalanche slip events of ½<111> dislocations gliding out of tested pillars. Simultaneously, a large number of sessile/immobile <100> dislocations formed in the vicinity of slip band, leading to the hardening at elevated strains.

36 MATERIALS SCIENCE↗