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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fracture behavior of irradiation induced nanocrystalline UO 2 studied by in-situ mechanical testing in transmission electron microscopy

Uranium Dioxide (UO 2 ) is widely used as a fuel in current light water reactors (LWRs). Upon accumulation of radiation damage, LWR UO 2 fuel pellets start to develop a different microstructure at the pellet periphery when fuel burnup exceeds 45–50 GWd/tHM. The resulting porous, nanocrystalline microstructure is one of the most prominent microstructural changes occurring in such fuel. Its fracture mechanisms, which causes fuel fine fragmentation, could impact safety limits when the cladding breaches. Direct measurements of these properties are challenging, therefore a surrogate obtained via ion irradiation can be used. In this study, multiple microcantilevers were fabricated by focused ion beam from both fresh UO 2 and UO 2 irradiated with 84 MeV Xe 26+ ions to a peak dose of 1357 displacements per atom (dpa). Further, the irradiation produced a pseudo high burnup structure approximately 2 µm below the surface. In-situ nano-mechanical bending tests were conducted to investigate the fracture behavior and the effect of the surrogate UO 2 high burnup structure on local fracture properties. Fresh UO 2 fuel was observed to fracture in transgranular mode without nucleation or movement of dislocations. However, the Xe-irradiated nanocrystalline microcantilevers fractured along the grain boundaries, with no influence from the pre-existing micro-cracks in the microcantilever. Fracture toughness for this type of surrogate high burnup UO 2 structure is reported for the first time in literature. Both the fracture stress and toughness show degradation for UO 2 as a result of Xe-irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Formation and growth of cerium (III) oxalate nanocrystals by liquid-cell transmission electron microscopy

Using rare earth elements as analogs for actinide series elements enables studying the chemistry of these radioactive materials without the inherent risks. A fundamental understanding of the nucleation and growth of actinide oxalates is crucial for processing nuclear waste. Using in-situ liquid cell TEM (LC-TEM), we show particle attachment processes occurring during the nucleation and growth of cerium oxalate. The early stages of the observed non-classical growth mechanisms have important implications for particle evolution and predictions of final particle morphology. In this work, we observed particle formation via monomer-by-monomer addition or the particle aggregation pathway depending on the selected precursor concentration. At the highest precursor concentration at a high dose rate, we observed particle alignment in a branch structure is due to the interfacial instability. It was demonstrated that in-situ LC-TEM is an invaluable tool in studying the cerium oxalate formation and ultimately a path to understanding actinide chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning of In-situ Temperature Reconstruction from Metal-nanoparticle Thermometry on Transmission Electron Microscopy

Utilizing Python programming language and third-party libraries, 2D temperature fields were reconstructed from TEM images demonstratively and a GUI was created. Local temperatures can be fast read on TEM at the nanoscale based on the nanoparticle thermometry. The Artificial Intelligence technique would speed up the in-situ heating TEM research and make real-time in-situ temperature monitoring possible at nanoscale.

42 ENGINEERING↗
DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗