journal article·2023-10

Microstructure evolution of iron precipitates in (Fe, He)-irradiated 6H-SiC: A combined TEM and multiscale modeling

N. Daghbouj, A.T. AlMotasem, J. Vesely, B.S. Li, H.S. Sen, M. Karlik, J. Lorinčík, F.F. Ge, L. Zhang, V. Krsjak, O. Laguta, M. Callisti, T. Polcar

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Abstract

Microstructure of radiation-induced Iron phases were investigated in a 6H-SiC subjected to Iron and Helium bombardment with a damage level of 8 dpa. The microstructural evolution before and after annealing was investigated by combining transmission electron microscopy (TEM, STEM-EDS), automated crystal phase and orientation imaging (ACOM-TEM), secondary ion mass spectroscopy (SIMS), and atomic scale simulations. The irradiation amorphized the entire damaged layer which contains an embedded band of He bubbles located at peak damage concentration. After annealing, the amorphous layer recrystallized into a polycrystalline 6H-SiC where the Fe profile significantly changed to form Fe-rich clusters. ACOM-TEM reveals the formation of large cubic FeSi clusters and small bcc-Fe precipitates located at the 6H-SiC grain boundaries. The type and size distribution of the precipitates greatly depend on the Fe profile. Fe-Si compounds form around the Fe peak concentration, while, bcc Fe precipitates tend to be more homogeneously distributed. Density functional theory (DFT) calculations demonstrate that the formation of Fe dimers and trimers in the 1st nearest neighbor is energetically favorable. A combined Monte Carlo/Classical molecular dynamic (MMC/MD) technique reveals that the Fe atoms prefer to form large clusters in accordance with experimental results. MD annealing simulations reveal the formation of stable bcc Fe at high temperatures. The phase transition starts at the cluster-matrix interface around 620 K and the cluster is fully transformed at 700 K.
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Research context

OpenAlex topics: Silicon Carbide Semiconductor Technologies · Fusion materials and technologies · Advanced ceramic materials synthesis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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