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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 OSTI2018

Determining the Spatial Profiles of Electron and Hole Concentration, Radiative and Non-Radiative Recombination Rate Near a Dislocation Defect by Combining Raman and Photoluminescence Imaging

For commonly utilized photoluminescence (PL) imaging, the spatial resolution is dictated by the carrier diffusion length rather than by that dictated by the optical system, such as diffraction limit. Here, we show that Raman imaging of the LO phonon-plasmon (LOPP) coupled mode can be used to recover the intrinsic spatial resolution of the optical system, as demonstrated by Raman imaging of defects in GaAs, achieving a 10-fold improvement in resolution. Furthermore, by combining Raman and PL imaging, we can independently determine the spatial profiles of the electron and hole density, radiative and non-radiative recombination rate near a dislocation defect, which has not been possible using other techniques.

14 SOLAR ENERGY↗
NASA NTRS1997

Advanced Image Processing for Defect Visualization in Infrared Thermography

Results of a defect visualization process based on pulse infrared thermography are presented. Algorithms have been developed to reduce the amount of operator participation required in the process of interpreting thermographic images. The algorithms determine the defect's depth and size from the temporal and spatial thermal distributions that exist on the surface of the investigated object following thermal excitation. A comparison of the results from thermal contrast, time derivative, and phase analysis methods for defect visualization are presented. These comparisons are based on three dimensional simulations of a test case representing a plate with multiple delaminations. Comparisons are also based on experimental data obtained from a specimen with flat bottom holes and a composite panel with delaminations.

Plotnikov, Yuri A.↗
DOE OSTI2019

Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels

Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanism is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training of a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction, and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Imaging of dark line defect growth in high-power diode laser cavities using broadband near infrared light emission from the laser cavity

An in situ and nondestructive technique is developed to image the formation and evolution of dark line defects in the cavity of a high-power diode laser. Here, the technique uses broadband near infrared emission that originates in the laser's core layers and enables defects to be imaged with high spatial resolution through the substrate. In particular, it enables defect imaging through the substrate of shorter wavelength lasers, even when the substrate is opaque near the lasing wavelength. The evolution of dark line defects during aging is studied in several devices, with correlations established between the observed characteristics of defect growth and changes in device parameters such as optical power, operating wavelength, threshold current, and slope efficiency. Gradual degradation is found to be associated with dark line defects that slowly propagate from dark spots that are present in the device interior in its fresh (unaged) condition, rather than propagating from spots that are formed during aging, suggesting a strategy to screen fresh devices for expected reliability. This defect growth phenomenon is found to be particularly evident in the dark spots near the output facet.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2018

Automated defect analysis in electron microscopic images

Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison–based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. Furthermore, these promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Dynamic imaging of crystalline defects in lithium-manganese oxide electrodes during electrochemical activation to high voltage

Crystalline defects are commonly generated in lithium-metal-oxide electrodes during cycling of lithium-ion batteries. Their role in electrochemical reactions is not yet fully understood because, until recently, there has not been an effective operando technique to image dynamic processes at the atomic level. In this study, two types of defects were monitored dynamically during delithiation and concomitant oxidation of oxygen ions by using in situ high-resolution transmission electron microscopy supported by density functional theory calculations. One stacking fault with a fault vector b/6[110] and low mobility contributes minimally to oxygen release from the structure. In contrast, dissociated dislocations with Burgers vector of c/2[001] have high gliding and transverse mobility; they lead to the formation, transport and release subsequently of oxygen related species at the surface of the electrode particles. This work advances the scientific understanding of how oxygen participates and the structural response during the activation process at high potentials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Magnetic Imaging with Spin Defects in Hexagonal Boron Nitride

Optically active spin defects hosted in hexagonal boron nitride (ℎ-BN) are promising candidates for the development of a two-dimensional (2D) quantum sensing unit. Here, we demonstrate quantitative magnetic imaging with ℎ-BN flakes doped with negatively charged boron-vacancy ($V^{-}_{B}$) centers through neutron irradiation. As a proof-of-concept, we image the magnetic field produced by Cr⁢Te 2 , a van der Waals ferromagnet with a Curie temperature slightly above 300 K. Compared to other quantum sensors embedded in 3D materials, the advantages of the ℎ-BN-based magnetic sensor described in this work are its ease of use, high flexibility, and, more importantly, its ability to be placed in close proximity to a target sample. Further, such a sensing unit will likely find numerous applications in 2D materials research by offering a simple way to probe the physics of van der Waals heterostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Laser ultrasonic imaging of subsurface defects with the linear sampling method

Laser ultrasonics is a remote nondestructive evaluation technique suitable for real-time monitoring of fabrication processes in semiconductor metrology, advanced manufacturing, and other applications where non-contact, high fidelity measurements are required. Here we investigate laser ultrasonic data processing approaches to reconstruct images of subsurface side drilled holes in aluminum alloy specimens. We demonstrate through simulation that the model-based linear sampling method (LSM) can perform accurate shape reconstruction of single and multiple holes and produce images with well-defined boundaries. We experimentally confirm that LSM produces images that represent the internal geometric features of an object, some of which may be missed by conventional imaging.

42 ENGINEERING↗
DOE OSTI2019

PTT System Performance Evaluation in 3-D Imaging of Calibrated Defects: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Research activity in this project is aimed at developing pulsed thermal tomography (PTT) architecture and algorithms for in-service non-destructive examination (NDE) of additively manufactured (AM) reactor components and materials. Recent advances in AM technology could allow cost-efficient fabrication of parts for operating reactors. However, because of stringent safety requirements, long-term performance of AM reactor components needs to be investigated before AM is widely accepted. Because of the complex shapes and significant surface roughness of components due to layer-by-layer welding process in AM, conventional methods might not be useful for in-service NDE of AM components. PTT is ideally suited for in-service NDE of AM metallic components because the method is non-contact, and offers high resolution #-D imaging of material flaws. The objective of this report is to provide initial evaluate of PTT performance sin detecting calibrated flaws in reactor structural materials. Preliminary COMSOL models were developed to conduct super computing simulations of PTT. In the experimental studies, high strength Stainless Steel 316 and Inconel 718 alloys were considered, as well as lower grade Stainless Steel 304, Nickel 200, and Hastelloy C276. Specimens investigated in this report consisted of approximately 1/4in-thick plates made out of these alloys using conventional manufacturing methods. The calibrated defects were created in the form of flat bottom holes (FBH) drilled in metallic plates. The diameters of FBH's varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. The size of the smallest FBH was limited to 1mm because conventional mechanic drills were used for creating the holes. PTT imaging results have shown that 1mm-diameter FBH located 1mm and 2mm below the surface were detectable. Larger size FBH were detectable at greater depth. For example, 6mm-diameter FBH could be detected at 8mm depth. Image contrast varied slightly between the specimens, with the best reconstructions obtained in SS316 and C276 plates. In addition, a 2/3in-thick Inconel 718 nozzle plate produced with additive manufacturing method was imaged with PTT. It was shown that PTT can scan through the plate in approximately 20s. Several modes of 3-D data visualization were explored, including using ImageJ and MATLAB software packages.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

In situ three-dimensional imaging of strain in gold nanocrystals during catalytic oxidation

The chemical properties of materials are dependent on dynamic changes in their three-dimensional (3D) structure as well as on the reactive environment. We report an in situ 3D imaging study of defect dynamics of a single gold nanocrystal. Our findings offer an insight into its dynamic nanostructure and unravel the formation of a nanotwin network under CO oxidation conditions. In situ/operando defect dynamics imaging paves the way to elucidate chemical processes at the single nano-object level towards defect-engineered nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗
DOE OSTI2018

Large spin-orbit splitting of deep in-gap defect states of engineered sulfur vacancies in monolayer WS 2

Structural defects in 2D materials offer an effective way to engineer new material functionalities beyond conventional doping in semiconductors. Specifically, deep in-gap defect states of chalcogen vacancies have been associated with intriguing phenomena in monolayer transition metal dichalcogenides (TMDs). Here, we report the direct experimental correlation of the atomic and electronic structure of a sulfur vacancy in monolayer WS 2 by a combination of CO-tip non-contact atomic force microscopy (nc-AFM) and scanning tunneling microscopy (STM). Sulfur vacancies, which are absent in as-grown samples, were deliberately created by annealing in vacuum. Two energetically narrow unoccupied defect states of the vacancy provide a unique fingerprint of this defect. Direct imaging of the defect orbitals by STM and state-of-the-art GW calculations reveal that the large splitting of 252 meV between these defect states is induced by spin-orbit coupling. The controllable incorporation and potential decoration of chalcogen vacancies provide a new route to tailor the optical, catalytic and magnetic properties of TMDs.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Imaging CdCl 2 defect passivation and formation in polycrystalline CdTe films by cathodoluminescence

Polycrystalline thin-film solar cells are attractive for low-cost photovoltaics, but their efficiencies are hindered by material quality issues. State-of-the-art CdTe solar cells use CdCl 2 annealing treatments whose effects are still being discovered at a fundamental level. Here, a series of CdTe samples with different annealing temperatures is investigated with high-resolution hyperspectral cathodoluminescence mapping measured at both room temperature and low temperature on the same microscopic areas. A statistical analysis over a large number of grains is combined with a local analysis at grain boundaries. The results elucidate the dynamic interplay between grain boundary and intragrain defect passivation and formation, in the midst of grain growth. The CdCl 2 annealing initially contributes to an increase of the grain size and the passivation of both grain boundaries and grain interiors, increasing the overall luminescence and diffusion length. For higher annealing temperatures, a further increase of grain size is counterbalanced by the rise of bulk defects. The results illustrate the tradeoffs that lead to an optimal annealing temperature, as well as new methods for understanding defect passivation and creation in thin film solar cells.

14 SOLAR ENERGY↗
DOE OSTI2020

Neural Learning Based Blind Source Separation for Detection of Material Defects in Pulsed Thermography Images

In this paper, we introduce a neural learning-based approach to blind source separation for detection of material flaws in pulsed thermography (PT) images. This approach can be used to detect internal defects (pores) in metallic Additively Manufactured (AM) materials. Such defects occur in high-strength alloys produced with direct laser sintering AM method for nuclear energy applications. Pulsed thermal imaging system utilizes a high intensity flash lamp to rapidly heat surface of sample, a high sensitivity infrared camera to capture data of surface temperature variations. The data cube obtained with PT (stack of surface temperature images at different times) can be analyzed with image processing algorithms to detect material defects. Compared with conventional nondestructive evaluation (NDE) methods, such as digital radiography and ultrasonic testing, the PT pulsed infrared thermal detection technique has advantages of one-sided non-contact measurements, fast processing of large sample areas captured in one image. Detection of small material defects requires finding features in thermal images, which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with the photothermic, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

Zhang, Xin↗