Diffusion of small He clusters in bulk and grain boundaries in α-Fe
Source abstract
Abstract
The diffusion properties of He interstitials and He clusters in the bulk and grain boundaries (GBs) of α-Fe have been studied using molecular dynamics with a newly developed Fe–He potential. The low migration energy barrier for a single He interstitial in the bulk is consistent with that obtained using ab initio methods. Small He clusters can migrate at low temperatures, but at higher temperatures they will kick out a self-interstitial atom (SIA) and become trapped by the vacancy, forming an He-vacancy complex. It is of great interest to note that small He n-vacancy clusters ( n < 5) in the bulk are able to absorb an SIA, and the clusters become mobile again. Trapping and de-trapping of He clusters by emitting and absorbing an SIA represent an important dynamic process that provides a mechanism for the diffusion of He clusters and the nucleation of He bubbles in bulk Fe, particularly under irradiation in which numerous SIAs and vacancies are constantly being produced. A single He interstitial can migrate one-dimensionally or two-dimensionally within GBs, depending on the GB structure. Small interstitial He n clusters ( n ∼ 1–10) can easily kick out an SIA, and become trapped by the vacancy, while the SIA quickly diffuses away from the clusters, disappearing into the GB, such that de-trapping of the He clusters by absorbing an SIA is less likely to occur. This suggests that small He clusters may be treated as relatively immobile defects in GBs. The different behavior of He clusters in the bulk compared to their behavior in GBs may explain the different He bubble sizes experimentally observed in the bulk and in GBs in reduced activation ferritic/martensitic steels that have been simultaneously neutron irradiated and He implanted.
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Research context
OpenAlex topics: Fusion materials and technologies · Nuclear Materials and Properties · Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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