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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 OSTI2025

Controllable oxygen vacancy defect engineering of BiVO 4 porous structures for room temperature NH 3 detection

Controlling structural features of sensing material while judiciously introducing vacancy defect states for revamping the electronic properties of the sample to obtain its superior gas sensing performance, is quite rare. Herein, we report for the first time, the room temperature (RT) ammonia (NH 3 ) detection of peanut-like porous bismuth vanadate (BiVO 4 ) with a stable monoclinic phase. The developed BiVO 4 possess abundant oxygen vacancies and porosity by virtue of calcinations (400–800 ℃). BiVO 4 calcined at 400 ℃ exhibits high selectivity towards NH 3 with a maximum response of 1421 @ 270 ppm at RT, which is 2.5 fold enhanced compared to without calcined BiVO 4 (response of 547 @ 270 ppm NH 3 ). The oxygen defective BiVO 4 appeared highly durable and stable even under high humid conditions (∼60 %). Besides, the porosity of BiVO 4 not only enhances the specific surface area (19.8 m 2 /g) but also results in fast diffusion of NH 3 molecules, leading to a reduction in the decay time (34 s for 90 ppm NH 3 ). The density functional theory (DFT) uncovers that the oxygen vacancy formation in BiVO 4 augments the NH 3 sensing capabilities by enhancing the adsorption energy of NH 3 . This work provides insight into the sensing mechanism of increased response caused by defect engineering and porosity, which will be favourable for fabricating high-performance NH 3 sensors at RT.

DFT analysis↗
DOE OSTI2025

Solution-Processed Temperature-Adaptive Radiative Paint as a Thermal Imaging Sensitizer

Thermography detects mid-infrared radiation from surfaces based on the Stefan-Boltzmann law, mapping surface temperatures and potentially revealing subsurface thermal activity. Recent developments offer an alternative strategy to traditional camera-based improvements: a thermal imaging sensitizer (TIS) coating whose emissivity increases sharply with local temperature, amplifying small thermal variations into high-contrast signals. Existing TIS structures are nanofabricated solid membranes and face significant challenges in fabrication complexity. Here, in this study, we present a solution-processed, liquid form of TIS, termed temperature-adaptive radiative paint (TARP), to address these limitations. TARP offers drastically reduced fabrication costs, scalability to large areas, applicability to curved surfaces, and an extended operating temperature range, while maintaining the function of temperature amplification. Application of TARP enhances small temperature contrast by more than 3 times, substantially improving ambient thermography and enabling broader applications such as detection of structural defects and hot spots in electronic components.

paint↗
DOE OSTI2024

DOC-DICAM: Domain Aware One Class Defect Identification in Composite Aerostructure Material

Fiber-reinforced composites are a common material used in the design of aircraft structures due to their good tensile strength and resistance to compression. During the manufacturing process, these structures are thoroughly inspected for flaws and defects to ensure structural integrity during commercial use. Non-destructive testing (NDT) is a collection of inspection methods that allow inspectors to evaluate material without altering it. Due to the high safety standards in aerospace manufacturing, the NDT process is done manually and can be a significant bottleneck in the development workflow. In this paper, we develop an AI-based assistance tool to drastically reduce inspection time. Typical AI workflows require large amounts of annotated data, but defects rarely occur resulting in strong class imbalance. To overcome this, we formulate the problem of defect identification as an anomaly detection task in which our primary focus is learning non-defect characteristics. To do this, we develop a multi-task self-supervised learning framework that embeds problem specific domain knowledge into the deep learning model. We verify our method using fuselage data generated in a production environment. As a result, we show that our method can effectively identify defects and requires minimal training and inference time.

anomaly detection↗
DOE OSTI2024

Atomic-scale visualization of defect-induced localized vibrations in GaN

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identification and Suppression of Point Defects in Bromide Perovskite Single Crystals Enabling Gamma‐Ray Spectroscopy

Abstract Methylammonium lead tribromide (MAPbBr 3 ) stands out as the most easily grown wide‐band‐gap metal halide perovskite. It is a promising semiconductor for room‐temperature gamma‐ray ( γ ‐ray) spectroscopic detectors, but no operational devices are realized. This can be largely attributed to a lack of understanding of point defects and their influence on detector performance. Here, through a combination of crystal growth design and defect characterization, including positron annihilation and impedance spectroscopy, the presence of specific point defects are identified and correlated to detector performance. Methylammonium (MA) vacancies, MA interstitials, and Pb vacancies are identified as the dominant charge‐trapping defects in MAPbBr 3 crystals, while Br vacancies caused doping. The addition of excess MABr reduces the MA and Br defects and so enables the detection of energy‐resolved γ ‐ray spectra using a MAPbBr 3 single‐crystal device. Interestingly, the addition of formamidinium (FA) cations, which converted to methylformamidinium (MFA) cations by reaction with MA + during crystal growth further reduced MA defects. This enabled an energy resolution of 3.9% for the 662 keV 137 Cs line using a low bias of 100 V. The work provides direction toward enabling further improvements in wide‐bandgap perovskite‐based device performance by reducing detrimental defects.

Ni, Zhenyi↗
DOE OSTI2024

Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection

Composite materials are widely used in aircraft structures because of their superior mechanical properties. However, their complex failure modes require sophisticated inspection methods to ensure structural integrity. Ultrasonic testing (UT) is a common non-destructive inspection (NDI) technique for aircraft composites that can detect internal and external defects with high resolution and accuracy. Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials. Here, we evaluated our approach on material with multiple thicknesses or domains comprising various defects. Our experimental results demonstrate higher accuracy and F1-Score, with only 10% training epochs compared to baseline MoE.

composite materials↗
DOE OSTI2024

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗
DOE OSTI2025

Integrating Crack Detection and Pipe Shape Optimization for Enhanced Sewage System Durability

Crack detection in underground reinforced concrete pipes has been essential in determining the state of stormwater infrastructure. Detection models have been implemented for detecting cracks and other defects in pipes using CCTV footage for stormwater drainage systems. In addition, Finite element models have been used to determine optimum shapes and pipe thickness for different boundary conditions such as header pipes in power plants. The concept of shape optimization emerges as a crucial factor in power plant design and operation, with the potential to maximize performance while minimizing the use of materials. Shape optimization not only enhances efficiency but also contributes to reducing the environmental footprint. This paper discusses the integration of both topics by using the cracks detected in underground pipes as boundary conditions for shape optimization of the pipes. A machine learning model has been developed which uses limited data for training and outlines the location of detected cracks. A shape optimization methodology is proposed in which ANSYS modules are used to analyze fluid flow and then optimize the shape of the pipe. The crack detection model developed has been applied to a crack detected in lab setting and machine learning model used has an accuracy of 98% using a random forest algorithm.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Characterization of dangling bond defects at the crystalline Si/SiO x interface in a polycrystalline Si passivating contact solar cell at room temperature with electrically detected magnetic resonance spectroscopy

Monocrystalline silicon solar cells can achieve photoconversion efficiencies exceeding 26%; however, performance-limiting defects that trap carriers continue to be a challenge. In this work, we have characterized Si solar cells with tunneling SiO x /polycrystalline-Si (poly-Si) passivating contacts (TOPCon) on As-doped Czochralski Si wafers with electrically detected magnetic resonance (EDMR) spectroscopy. We fabricated 2 × 20 mm 2 TOPCon-like mini solar cells with edge passivation alongside larger 4 cm 2 sister cells and obtained similar device characteristics. We performed EDMR spectroscopy at 300 K on two minicells with different degrees of surface passivation based on the recombination parameter, J o , values of 40 and 310 fA/cm2. We optimized the resolution and the signal-to-noise ratio of the EDMR response of the minicells by varying the forward bias voltage and the magnetic field modulation amplitude. We detect two distinct signals with EDMR spectroscopy, an axial-like signal at g = 2.009, 2.0087, and 2.0015, and an isotropic signal at g = 2.0024, which we attribute to Si dangling bonds (P b0 and P b centers) and boron–oxygen related defects, respectively, at or near the c-Si/SiO x interface. The EDMR signals were lower for the cell with a lower value of J o , while the ratio of the two defect populations was very similar. The EDMR signal increases with forward bias but drops to zero at bias voltages >0.5 V, consistent with interface defects within or near the boron-doped emitter depletion region. Our study demonstrates a method to fabricate minicells that can be characterized with EDMR spectroscopy to detect industrially relevant defects in TOPCon cells.

14 SOLAR ENERGY↗
DOE OSTI2024

Growth of High-Purity CsPbBr 3 Crystals for Enhanced Gamma-Ray Detection

High-quality CsPbBr 3 crystals hold significant potential for gamma-ray detection due to their remarkable optoelectronic properties. This study details an optimized production process using the Bridgman method to achieve highly pure CsPbBr 3 crystals. By implementing rigorous synthesis and purification techniques, we successfully reduced the total impurity levels to 9 ppm, as confirmed by glow discharge mass spectroscopy (GDMS). The resulting CsPbBr 3 crystals demonstrate exceptional performance, including high transparency, intense photoemission, and prolonged photoluminescence decay times. These properties facilitate superior gamma-ray detection with an energy resolution of 1.4% for the 137 Cs 662 keV gamma-rays, comparable to commercial Cd 1-x Zn x Te (CZT) detectors. Our findings underscore the critical relationship between material purity and detector performance, highlighting the potential of CsPbBr 3 as a cost-effective alternative in radiation detection applications. Further studies on defect origins and electronic states are necessary to fully leverage the capabilities of CsPbBr 3 crystals in practical high-energy radiation detection systems.

Bridgman↗
DOE OSTI2024

Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels

Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantification of Oxygen Vacancies in SrFe0.5Cr0.5O3−δ Thin Films: Correlating Lattice Expansion with Oxidation State Variability

Oxygen vacancies (VO), even in the dilute regime, can significantly impact the physical and chemical properties of complex oxides. It is however challenging to reliably detect and adequately quantify such defects in thin film samples where the VO concentration and distribution may further vary in response to interfacial strain. Here, we present a method to quantify VO concentrations in SrFe0.5Cr0.5O3−δ (SFCO) epitaxial thin films. Through coherent Bragg rod analysis, X-ray absorption spectroscopy, and theoretical modeling, we systematically correlate the c-axis lattice expansion detected in SFCO films with a VO concentration gradient. Our results reveal a nearly linear relationship between oxygen off-stoichiometry and out-of-plane lattice expansion in coherently strained SFCO films, with δ increasing from 0.32 to 0.52 and the lattice expanding by ∼1.0%. Moreover, a significant decrease in Cr oxidation state (from Cr5+ to Cr3+) is observed as the VO concentration increases, while the Fe oxidation state remains fixed at Fe3+. Our findings provide a reliable way to quantify oxygen stoichiometry in complex oxides, offering a pathway to design materials with precisely tailored structure–property relationships.

Hossain, Mohammad Delower↗
DOE OSTI2025

Comprehensive defect evaluation of advanced nuclear fuels using high-resolution acoustic signals and optimized sensor separation

Graphite pebble composite structures based on TRistructural-ISOtropic (TRISO) particles are being developed as core nuclear fuels in advanced power reactors, promising safe operation at increased temperatures. Ensuring the structural integrity of these nuclear fuels requires comprehensive and accurate non-destructive evaluation (NDE) techniques to characterize defects and damage in the pebbles. However, traditional acoustic evaluation methods face limitations in defect characterization due to the highly attenuative, and geometrically and compositionally complex nature of these structures. This study proposes an improved acoustic NDE technique for accurate detection and classification of anticipated relevant defects and damage in graphite pebbles using high-resolution acoustic signals and optimized transmit-receive sensor networks. The proposed approach utilizes a triangular three-sensor network as the base unit, comprising three transmit-receive sensors. The sensor separation distance, as well as acoustic excitation center frequency, pulse-width, and bandwidth are optimized to enhance spatial resolution and improve signal-to-noise ratio, enabling effective characterization of the smallest size and widest range of defects in pebbles. Furthermore, the use of the triangular sensor configuration instead of a more conventional transmit-receive sensor pair expands the inspection region from a one-dimensional linear path to a two-dimensional area, increasing spatial coverage. To mitigate challenges associated with processing of complex acoustic signals arising from high-frequency, high-bandwidth excitation in these structures, a machine-learning-based signal processing algorithm is integrated with the sensor network. In the machine-learning-based algorithm, multi-domain features are extracted from the acoustic signals to capture intricate signal characteristics, significantly improving defect identification and classification compared to traditional approaches. The proposed acoustic NDE technique offers considerable promise for practical and reliable defect/damage diagnostics of advanced nuclear pebble fuels.

42 ENGINEERING↗
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↗
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 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↗