MultiTaskDeltaNet
Change Detection-based Image Segmentation for operando ETEM with Application to Carbon Gasification Kinetics
Niu, Yushuo↗
Engineering topics
Zhu, Yuanyuan
Publications and source records attributed to Zhu, Yuanyuan.
In-situ ETEM study of plasma-facing tungsten nanofuzz oxidation at atmospheric pressure: Microstructure evolution and substrate-free oxidation kinetics
To enable sustainable carbon-free fusion energy, managing reactor structural material degradation during normal operation as well as accident scenarios is vital. Tungsten (W) plasma-facing materials (PFMs) are susceptible to aggressive high-temperature oxidation during air-ingress fusion reactor accidents, yet there's a lack of oxidation kinetic data for irradiated tungsten. Here, in this study, we utilize atmospheric environmental transmission electron microscopy (ETEM) to present the first kinetic data for substrate-free W nanofuzz oxidation at 400 ºC and 500 ºC in 1 bar dry air. Comparison with pristine bulk W during the early parabolic stage suggests an irradiationdecelerated oxidation for W nanofuzz. Our time-resolved in-situ characterization reveals a durable amorphous surface oxide, likely promoted by high-flux He + irradiation-induced surface defects, serving as an effective passivating layer that impedes nanofuzz oxidation onset. This surface oxide layer also interfaces well with newly formed orthorhombic WO 3 , facilitated by stress relief through He bubble shrinkage, providing lasting passivating protection throughout the nanofuzz parabolic oxidation. This new finding challenges conventional notions of irradiation's negative impact on metal oxidation, and calls for advanced characterization to enhance our understanding of fusion energy materials degradation, informed by further accident modeling.
36 MATERIALS SCIENCE↗
Single-Atom Cobalt Catalysts Coupled with Peroxidase Biocatalysis for C–H Bond Oxidation
Not provided.
Materials Science↗
Thermomechanical Properties of Neutron Irradiated Al 3 Hf-Al Thermal Neutron Absorber Materials
A thermal neutron absorber material composed of Al 3 Hf particles in an aluminum matrix is under development for the Advanced Test Reactor. This metal matrix composite was fabricated via hot pressing of high-purity aluminum and micrometer-size Al 3 Hf powders at volume fractions of 20.0, 28.4, and 36.5%. Room temperature tensile and hardness testing of unirradiated specimens revealed a linear relationship between volume fraction and strength, while the tensile data showed a strong decrease in elongation between the 20 and 36.5% volume fraction materials. Tensile tests conducted at 200 °C on unirradiated material revealed similar trends. Evaluations were then conducted on specimens irradiated at 66 to 75 °C to four dose levels ranging from approximately 1 to 4 dpa. Tensile properties exhibited the typical increase in strength and decrease in ductility with dose that are common for metallic materials irradiated at ≤0.4T m . Hardness also increased with neutron dose. The difference in strength between the three different volume fraction materials was roughly constant as the dose increased. Nanoindentation measurements of Al 3 Hf particles in the 28.4 vol% material showed the expected trend of increased hardness with irradiation dose. Transmission electron microscopy revealed oxygen at the interface between the Al 3 Hf particles and aluminum matrix in the irradiated material. Scanning electron microscopy of the exterior surface of tensile tested specimens revealed that deformation of the material occurs via plastic deformation of the Al matrix, cracking of the Al 3 Hf particles, and to a lesser extent, tearing of the matrix away from the particles. The fracture surface of an irradiated 28.4 vol% specimen showed failure by brittle fracture in the particles and ductile tearing of the aluminum matrix with no loss of cohesion between the particles and matrix. The coefficient of thermal expansion decreased upon irradiation, with a maximum change of –6.3% for the annealed irradiated 36.5 vol% specimen.
36 MATERIALS SCIENCE↗
DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
Abstract In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g., lifetime) has become the major technical bottleneck. Here, we present a solution called the DefectTrack , the first dedicated deep learning-based one-shot multi-object tracking (MOT) model capable of tracking cascade-induced defect clusters in in-situ TEM videos in real-time. DefectTrack has achieved a Multi-Object Tracking Accuracy (MOTA) of 66.43% and a Mostly Tracked (MT) of 67.81% on the test set, which are comparable to state-of-the-art MOT algorithms. We discuss the MOT framework, model selection, training, and evaluation strategies for in-situ TEM applications. Further, we compare the DefectTrack with four human experts in quantifying defect cluster lifetime distributions using statistical tests and discuss the relationship between the material science domain metrics and MOT metrics. Our statistical evaluations on the defect lifetime distribution suggest that the DefectTrack outperforms human experts in accuracy and speed.
42 ENGINEERING↗
Dose rate effects on damage accumulation and void growth in self-ion irradiated tungsten
Not Available
Jiang, Weilin (ORCID:0000000183028313)↗
Direct Observation of Tungsten Oxidation Studied by In Situ Environmental TEM
In this study, in situ environmental transmission electron microscopy (ETEM) was applied, for the first time, to investigate the thermal oxidation of a pristine and a self-ion irradiated polycrystalline tungsten, using a MEMS-based gas cell at 500 oC to 900 oC in a 1 bar 2%O2/N2 gas mixture. By tracking the dynamic evolution of the tungsten oxide scale as it initiates, grows, and sublimates during a consecutive thermal oxidation experiment, we observed two distinctive tungsten oxide microstructure – one nanocrystalline o-WO3 scale grown on W{27-1 } and W{1-1 8} and another o-WO3 scale exhibits a novel highly textured nanostructure. While the two oxide microstructures shared a similar thickness of ~200 nm after 40-minute early-stage oxidation, the nanocrystalline scale on the W{27-1 } grew much more rapidly at 800 oC than the highly textured oxide at a higher temperature of 900 oC. This suggests both the microstructure of these tungsten oxide scales as well as their oxidation kinetics are highly sensitive to the tungsten surface orientation, and such correlations also change dynamically in the course of oxidation. We also discuss the tungsten oxidation mechanism, the effects of the TEM foil thickness, and the focused ion beam (FIB) Ga+ damage on in situ ETEM oxidation.
In situ ETEM, Tungsten, Thermal oxidation, Orienta↗
Suppressing irradiation induced grain growth and defect accumulation in nanocrystalline tungsten through grain boundary doping
Not Available
Cunningham, W. Streit (ORCID:0000000331663563)↗
Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods
Not Available
Pazdernik, Karl (ORCID:0000000314104006)↗
Targeted synthesis and reaction mechanism discussion of Mo 2 C based insertion-type electrodes for advanced pseudocapacitors
Mo 2 C is one of the few compounds that possess good electronic conductivity. Meanwhile, it possesses a natural 1D zigzag tunnel structure that is ideally suited for fast ion diffusion. Here, an effective approach is demonstrated for fabrication of structurally stable N-doped Mo 2 C/C nanobelts. They demonstrate high and fast energy storage ability with initial capacitances of 1139 C g -1 at 1 mV s-1, 151 C g -1 at an extremely high scan rate of 2000 mV s -1 and 208 C g -1 at a discharge current density of 200 A g -1 . After electrochemical activation of cycling, the capacity is continuously enhanced and much higher capacitances of 2523 C g -1 at 1 mV s -1 and 1403 C g -1 at 50 A g -1 are achieved after 15,000 cycles at 50 mV s -1 . Using the power law, it is evaluated that a surface-controlled capacitive process makes the main contribution to the capacity, which is over 90% when the scan rates are higher than 10 mV s -1 and still high as 73% at 1 mV s -1 . From in situ synchrotron XRD, it is found that there is a negligible change in the crystal structure and volume during charging/discharging, reflecting an insertion-type charge storage mechanism.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Design and synthesis of highly active MoVTeNb-oxides for ethane oxidative dehydrogenation
Ethane oxidative dehydrogenation (ODH) is an alternative route for ethene production. Crystalline M1 phase of Mo-V mixed metal oxide is an excellent catalyst for this reaction. Here we show a hydrothermal synthesis method that generates M1 phases with high surface areas starting from poorly soluble metal oxides. Use of organic additives allows control of the concentration of metals in aqueous suspension. Reactions leading to crystalline M1 take place at 190 °C, i.e., approximately 400 °C lower than under current synthesis conditions. The evolution of solvated polyoxometalate ions and crystalline phases in the solid is monitored by spectroscopies. Catalysts prepared by this route show higher ODH activity compared to conventionally prepared catalysts. The higher activity is due not only to the high specific surface area but also to the corrugated lateral termination of the M1 crystals, as seen by atomic resolution electron microscopy, exposing a high concentration of catalytically active sites.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel
Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.
Sainju, Rajat↗
ADVANCED-STEM-BASED DEEP LEARNING FOR SEMANTIC SEGMENTATION OF DEFECTS IN STEELS
In this work, we developed a novel deep convolutional neural network (DCNN) model, called DefectNet, for robust and automated semantic segmentation of three crystallographic defects including line dislocations, precipitates and voids commonly observed in structural metals and alloys. In previous work, we established an experimental protocol for a diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) technique and tailored it specifically for imaging defects in popular iron-based structural alloy. Thus, the DefectNet was trained over a small but high-quality DCI STEM defect image sets of a HT-9 martensitic steel before and after neutron irradiation for 111.8 dpa at 412°C. For the defect quantification that typically takes at least half an hour even for an expert, with a good model the machine learning algorithm can produce more reproducible and reliable quantification in a few seconds. This work demonstrates the feasibility of using deep learning algorithms for fast and accurate defect quantitative analysis.
Machine Learning, Radiation effects, Image analysi↗
Towards bend-contour-free dislocation imaging via diffraction contrast STEM
Dislocation imaging using transmission electron microscopy (TEM) has been an invaluable tool for characterizing crystallographic defects in metals. Compared to conventional TEM imaging, diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) with appropriate setting can provide better defect contrast with almost negligible bend contour artifacts, enabling more effective analysis of dislocation structures. Here, we investigated why STEM can suppresses bend contour, and how dislocation contrast behaves along with different STEM imaging parameters. Using a body-centered cubic HT-9 ferritic/martensitic alloy as an example, a simple procedure and operational theory are described at the beginning to help set up DCI STEM experiments. Comparing with conventional TEM and the STEM strictly complying with the principle of reciprocity, we found that a pair of STEM convergence and collection semi-angles, α S and β S , a few milliradians in size is essential for bend-contour-free defect imaging. It works in concert such that the convergence STEM probe opens up the reciprocal space, and then a comparable collection region evens out the rocking-curve oscillation and alleviates bend contours from the reciprocal space. This fundamental advantage is unique in DCI STEM. Practical guidelines regarding STEM parameters and specimen orientation and thickness are then provided for DCI STEM dislocation imaging. Lastly, we show that coupling DCI STEM with spectrum images of electron energy loss spectroscopy and of energy-dispersive X-ray Spectroscopy offers a comprehensive characterization of crystallographic defects and chemical information of complex microstructures.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The Role of Gas in Determining Image Quality and Resolution During In Situ Scanning Transmission Electron Microscopy Experiments
As gas-solid heterogeneous catalytic reactions are molecular in nature, a full mechanistic understanding of the process requires atomic scale characterization under realistic operating conditions. While atomic resolution imaging has become a routine in modern high-vacuum (scanning) transmission electron microscopy ((S)TEM), both image quality and resolution nominally degrade when reaction gases are introduced. In this work, we systematically assess the effects of different gases at various pressures on the quality and resolution of images obtained at room temperature in the annular dark field STEM imaging mode using a differentially pumped (DP) gas cell. This imaging mode is largely free from inelastic scattering effects induced by the presence of gases and retains good imaging properties over a wide range of gas mass/pressures. We demonstrate the application of the ESTEM with atomic resolution images of a complex oxide alkane oxidation catalyst MoVNbTeOx (M1) immersed in light and heavy gas environments.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗