On the iron content of Mn-Ni-Si-rich clusters that form in reactor pressure vessel steels during exposure to neutron irradiation
Source abstract
Abstract
Solute cluster formation during service is the primary cause of embrittlement in reactor pressure vessel (RPV) steels. Atom probe tomography (APT) has consistently shown that the solute clusters are enriched in Cu, P, Mn, Ni, and Si compared to the matrix. However, there is pronounced disagreement within the literature as to the Fe content of the solute clusters in low-Cu RPV steels; some authors report Fe contents in excess of 80 at.% whilst others attribute all measured Fe in the clusters to aberrations from APT and report Fe-free features. This discrepancy has profound implications for determining whether cluster formation is radiation-induced or radiation-enhanced. In this article, we perform a systematic analysis of the published literature to demonstrate the variance present in the measured Fe content of solute clusters characterised by APT. We investigate potential explanatory variables for this variation and discuss the difficulties in performing statistical analyses on these data. To determine the impact of APT aberrations on the measured Fe content of solute clusters, we combine results from: simulations that account for the limited spatial precision in APT; models that predict ion trajectories in the presence of non-uniform electrostatic fields ; and experimental data. Our results show that APT aberrations can introduce large amounts of artificial Fe into solute clusters, especially at the small sizes (radius <1.5 nm) typically observed in RPV steels. We also show, however, that some of the variance in Fe content in the literature may be explained by real differences in the clusters’ Fe contents.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Materials Characterization Techniques · Fusion materials and technologies · Microstructure and Mechanical Properties of Steels
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (71)
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