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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Probability of brittle failure

A methodology was developed for collecting statistically representative data for crack initiation and arrest from small number of test specimens. An epoxy (based on bisphenol A diglycidyl ether and polyglycol extended diglycyl ether and cured with diethylene triamine) is selected as a model material. A compact tension specimen with displacement controlled loading is used to observe multiple crack initiation and arrests. The energy release rate at crack initiation is significantly higher than that at a crack arrest, as has been observed elsewhere. The difference between these energy release rates is found to depend on specimen size (scale effect), and is quantitatively related to the fracture surface morphology. The scale effect, similar to that in statistical strength theory, is usually attributed to the statistics of defects which control the fracture process. Triangular shaped ripples (deltoids) are formed on the fracture surface during the slow subcritical crack growth, prior to the smooth mirror-like surface characteristic of fast cracks. The deltoids are complementary on the two crack faces which excludes any inelastic deformation from consideration. Presence of defects is also suggested by the observed scale effect. However, there are no defects at the deltoid apexes detectable down to the 0.1 micron level.

Kim, A.↗
NASA NTRS1993

Radiation effects in Ga(0.47)In(0.53)As solar cells

The effects of irradiating Ga(0.47)In(0.53)As p-i-n junctions with 1 MeV electrons were measured using Deep Level Transient Spectroscopy (DLTS) and both dark and illuminated (1 sun, air mass zero (AM0)) current-voltage (I-V) measurements. The I-V measurements were made over the range 100K less than T less than 350K. Temperature coefficients of the Ga(0.47)In(0.53)As photovoltaic parameters are presented which follow the same general behavior as other solar cell materials (e.g. Si and GaAs). Fits of the dark I-V data to the two term diode equation before irradiation were satisfactory, yielding an estimated band-gap energy of 0.79 eV. The recombination component of the dark current was found to increase linearly with fluence. DLTS detected two radiation-induced defect levels one shallow (E(sub c)-0.10 eV) and one near mid-gap (E(sub c)-0.29 eV), and it is the near mid-gap level which is expected to be the cause of the dark current increase. The radiation-induced degradation of the open circuit voltage is shown to be accurately predicted from the dark I-V measurements. The degradation of the open circuit voltage dominated the radiation response of the photovoltaic parameters while the short circuit current was only moderately degraded. This behavior is qualitatively explained in terms of the base thickness and dopant level. Appropriate changes in the device structure are suggested which should increase the radiation resistance. Isochronal thermal annealing induced recovery in the photovoltaic parameters at approximately 400K, coinciding with an annealing stage of the near mid-gap defect level.

Walters, R. J.↗
NASA NTRS2004

Compact Microscope Imaging System With Intelligent Controls Improved

The Compact Microscope Imaging System (CMIS) with intelligent controls is a diagnostic microscope analysis tool with intelligent controls for use in space, industrial, medical, and security applications. This compact miniature microscope, which can perform tasks usually reserved for conventional microscopes, has unique advantages in the fields of microscopy, biomedical research, inline process inspection, and space science. Its unique approach integrates a machine vision technique with an instrumentation and control technique that provides intelligence via the use of adaptive neural networks. The CMIS system was developed at the NASA Glenn Research Center specifically for interface detection used for colloid hard spheres experiments; biological cell detection for patch clamping, cell movement, and tracking; and detection of anode and cathode defects for laboratory samples using microscope technology.

McDowell, Mark↗
NASA NTRS2004

On-Orbit Software Analysis

The On-Orbit Software Analysis Research Infusion Project was done by Intrinsyx Technologies Corporation (Intrinsyx) at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC). The Project was a joint collaborative effort between NASA Codes IC and SL, Kestrel Technology (Kestrel), and Intrinsyx. The primary objectives of the Project were: Discovery and verification of software program properties and dependencies, Detection and isolation of software defects across different versions of software, and Compilation of historical data and technical expertise for future applications

Moran, Susanne I.↗
DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Defect Complexes in CrSBr Revealed Through Electron Microscopy and Deep Learning

Atomic defects underpin the properties of van der Waals materials, and their understanding is essential for advancing quantum and energy technologies. Scanning transmission electron microscopy is a powerful tool for defect identification in atomically thin materials, and extending it to multilayer and beam-sensitive materials would accelerate their exploration. Here, we establish a comprehensive defect library in a bilayer of the magnetic quasi-1D semiconductor CrSBr by combining atomic-resolution imaging, deep learning, and calculations. We apply a custom-developed machine learning work flow to detect, classify, and average point vacancy defects. This classification enables us to uncover several distinct Cr interstitial defect complexes, combined Cr and Br vacancy defect complexes, and lines of vacancy defects that extend over many unit cells. We show that their occurrence is in agreement with our computed structures and binding energy densities, reflecting the intriguing layer interlocked crystal structure of CrSBr. Our ab initio calculations show that the interstitial defect complexes give rise to highly localized electronic states. These states are of particular interest due to the reduced electronic dimensionality and magnetic properties of CrSBr and are, furthermore, predicted to be optically active. Our results broaden the scope of defect studies in challenging materials and reveal new defect types in bilayer CrSBr that can be extrapolated to the bulk and to over 20 materials belonging to the same FeOCl structural family.

deep learning↗
NASA NTRS1997

Lithium-ion drifting: Application to the study of point defects in floating-zone silicon

The use of lithium-ion (Li(+)) drifting to study the properties of point defects in p-type Floating-Zone (FZ) silicon crystals is reported. The Li(+) drift technique is used to detect the presence of vacancy-related defects (D defects) in certain p-type FZ silicon crystals. SUPREM-IV modeling suggests that the silicon point defect diffusivities are considerably higher than those commonly accepted, but are in reasonable agreement with values recently proposed. These results demonstrate the utility of Li(+) drifting in the study of silicon point defect properties in p-type FZ crystals. Finally, a straightforward measurement of the Li(+) compensation depth is shown to yield estimates of the vacancy-related defect concentration in p-type FZ crystals.

Silicon↗
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↗
NASA NTRS2007

NDE Methods for the External Tank Spray on Foam Insulation

The foam thermal protection system (TPS) of the space shuttle External Tank (ET) has provided some unique challenges to the nondestructive testing community. Three nondestructive evaluation methods have been developed to identify defects in the foam TPS of the ET. Terahertz imaging and backscatter radiography have been developed to identify voids in thick foam regions while shearography has been developed to identify shallow delaminations, shallow voids and crush damage in the foam. The basic theory of operation along with factors, determined from a series of designed experiments, affecting the results of these methods will be described. Results from both test panels and flight tank inspections will be provided, along with predicted probability of detection estimates to show the range in defect sizes and types that can be readily detected.

Walker, James L.↗
DOE OSTI2026

Localizing tetrahedral aluminum in nitrate-bearing gibbsite to constrain defect-impurity coupling

The enhanced radiolytic stability of gibbsite (α-Al(OH) 3 ) containing trace nitrate (NO 3 − ) is a phenomenon in nuclear waste management, but its structural origins remain unresolved. Motivated by the detection of minority tetrahedral aluminum (T d ) defects in synthetic gibbsite, we hypothesized that these sites may participate in NO 3 − retention or mediate H 2 suppression. To evaluate this, we combined orthogonal techniques comprised of spatially selective solid-state 27 Al MAS NMR, comparative spectroscopy, and density functional theory (DFT) modeling. Paramagnetic editing and dynamic nuclear polarization (DNP) MAS NMR confirm that T d defects are confined to the particle interior. DFT calculations reveal no energetic stabilization of NO 3 − near T d sites. Comparative NMR analysis shows that T d is also present in chloride-bearing gibbsite, which exhibits high radiolytic hydrogen yields. These three independent disqualifications rule out T d as a structural contributor to nitrate-mediated suppression and narrow the scope of defect-driven explanations. The findings redirect mechanistic attention away from coordination defects and toward redox-active impurity pathways, providing a refined foundation for understanding radiation tolerance in Al(OH) 3 .

Graham, Trent R. [Pacific Northwest National Labor↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2d materials↗
DOE OSTI2025

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.

2D materials↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.

2D materials↗
NASA NTRS2010

Automatic Review of Abstract State Machines by Meta Property Verification

A model review is a validation technique aimed at determining if a model is of sufficient quality and allows defects to be identified early in the system development, reducing the cost of fixing them. In this paper we propose a technique to perform automatic review of Abstract State Machine (ASM) formal specifications. We first detect a family of typical vulnerabilities and defects a developer can introduce during the modeling activity using the ASMs and we express such faults as the violation of meta-properties that guarantee certain quality attributes of the specification. These meta-properties are then mapped to temporal logic formulas and model checked for their violation. As a proof of concept, we also report the result of applying this ASM review process to several specifications.

Arcaini, Paolo↗
NASA NTRS1998

Study Methods to Characterize and Implement Thermography Nondestructive Evaluation (NDE)

The limits and conditions under which an infrared thermographic nondestructive evaluation can be utilized to assess the quality of aerospace hardware is demonstrated in this research effort. The primary focus of this work is on applying thermography to the inspection of advanced composite structures such as would be found in the International Space Station Instrumentation Racks, Space Shuttle Cargo Bay Doors, Bantam RP-1 tank or RSRM Nose Cone. Here, the detection of delamination, disbond, inclusion and porosity type defects are of primary interest. In addition to composites, an extensive research effort has been initiated to determine how well a thermographic evaluation can detect leaks and disbonds in pressurized metallic systems "i.e. the Space Shuttle Main Engine Nozzles". In either case, research into developing practical inspection procedures was conducted and thermographic inspections were performed on a myriad of test samples, subscale demonstration articles and "simulated" flight hardware. All test samples were fabricated as close to their respective structural counterparts as possible except with intentional defects for NDE qualification. As an added benefit of this effort to create simulated defects, methods were devised for defect fabrication that may be useful in future NDE qualification ventures.

Walker, James L.↗
NASA NTRS2014

Nondestructive Evaluation (NDE) for Inspection of Composite Sandwich Structures

Composite honeycomb structures are widely used in aerospace applications due to their low weight and high strength advantages. Developing nondestructive evaluation (NDE) inspection methods are essential for their safe performance. Flash thermography is a commonly used technique for composite honeycomb structure inspections due to its large area and rapid inspection capability. Flash thermography is shown to be sensitive for detection of face sheet impact damage and face sheet to core disbond. Data processing techniques, using principal component analysis to improve the defect contrast, are discussed. Limitations to the thermal detection of the core are investigated. In addition to flash thermography, X-ray computed tomography is used. The aluminum honeycomb core provides excellent X-ray contrast compared to the composite face sheet. The X-ray CT technique was used to detect impact damage, core crushing, and skin to core disbonds. Additionally, the X-ray CT technique is used to validate the thermography results.

Zalameda, Joseph N.↗
DOE OSTI2026

Subterahertz Spin Relaxation Dynamics of Boron-Vacancy Centers in Hexagonal Boron Nitride

Quantum sensors based on spin defects have become powerful tools for detecting faint magnetic signals, yet their operation remains confined to low magnetic fields and gigahertz frequencies. Extending such sensors into high-field (>0.3 T) and subterahertz regimes would enable quantum metrology across a wide range of electromagnetic phenomena and scientific applications, but has proven challenging. Here, we demonstrate that negatively charged boron vacancies ($V^−_B$) in hexagonal boron nitride can function as relaxation-based quantum sensors operating up to 0.2 terahertz and 7 T fields. Their uniform spin-orientation and persistent spin-contrast at high fields enable measurement of intrinsic spin relaxation across unexplored field regimes. We reveal a crossover in relaxation behavior, initially decreasing at low fields before rising at higher fields, consistent with the emergence of single-phonon-induced resonant noise at subterahertz frequencies. These results establish $V^−_B$ centers as a versatile platform for quantum sensing in the subterahertz, high-field regime.

boron-vacancies↗
DOE OSTI2025

Metallicity and anomalous Hall effect in epitaxially strained, atomically thin RuO 2 films

The anomalous Hall effect (AHE), a hallmark of time-reversal symmetry breaking, has been reported in rutile RuO 2 , a debated metallic altermagnetic candidate. Previously, AHE in RuO 2 was observed only in strain-relaxed thick films under extremely high magnetic fields (~50 T). Yet, in ultrathin strained films with distinctive anisotropic electronic structures, there are no reports, likely due to disorder and defects suppressing metallicity thus hindering its detection. Here, we demonstrate that ultrathin, fully strained 2 nm TiO 2 /t nm RuO 2 /TiO 2 (110) heterostructures, grown by hybrid molecular beam epitaxy, retain metallicity and exhibit a sizeable AHE at a significantly lower magnetic field (< 9 T). Density functional theory calculations reveal that epitaxial strain stabilizes a noncompensated magnetic ground state and reconfigures magnetic ordering in RuO 2 (110) thin films. These findings establish ultrathin RuO 2 as a platform for strain-engineered magnetism and underscore the transformative potential of epitaxial design in advancing spintronic technologies.

MBE↗