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Zhu, Yuanyuan

Publications and source records attributed to Zhu, Yuanyuan.

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

MultiTaskDeltaNet

Change Detection-based Image Segmentation for operando ETEM with Application to Carbon Gasification Kinetics

Niu, Yushuo↗
DOE OSTI2024

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↗
DOE OSTI2023

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↗
DOE OSTI2022

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↗
DOE OSTI2021

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↗
DOE OSTI2020

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↗
DOE OSTI2019

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↗
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↗