Quantitative methods for the APT analysis of thermally aged RPV steels

Styman, Paul; Hyde, J.M.; Wilford, K.; Smith, George Davey

doi:10.1016/j.ultramic.2012.12.003

Cited by 37 publications

(25 citation statements)

References 20 publications

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“…Finally, for the cluster analysis method, an N min value selected using the established protocol of Styman et al [24]. In this, for the chosen d max value, a cluster size distribution is generated and compared with that from a randomized dataset with the same overall chemical composition.…”

Section: Apt Analysesmentioning

confidence: 99%

Ion-irradiation-induced clustering in W–Re and W–Re–Os alloys: A comparative study using atom probe tomography and nanoindentation measurements

et al. 2015

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This study examines clustering and hardening in W-2 at.% Re and W-1 at.% Re-1 at.% Os alloys induced by 2 MeV W + ion irradiation at 573 and 773 K. Such clusters are known precursors to the formation of embrittling precipitates, a potentially life-limiting phenomenon in the operation of fusion reactor components. Increases in hardness were studied using nanoindentation. The presence of osmium significantly increased postirradiation hardening. Atom probe tomography analysis revealed clustering in both alloys, with the size and number densities strongly dependent on alloy composition and irradiation temperature. The highest cluster number density was found in the ternary alloy irradiated at 773 K. In the ternary alloy, Os was found to cluster preferentially compared to Re. The implications of this result for the structural integrity of fusion reactor components are discussed. Crown

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Section: Apt Analysesmentioning

confidence: 99%

Ion-irradiation-induced clustering in W–Re and W–Re–Os alloys: A comparative study using atom probe tomography and nanoindentation measurements

et al. 2015

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“…By coupling recent advances in near-atomic resolution characterization techniques in three dimensions, such as atom probe tomography (APT) [11,12], with atomistic simulation tools such as kinetic lattice Monte Carlo (KLMC) [13], it is possible to study the precipitation pathways of multicomponent alloys in a quantitative manner. Here, we use these techniques to model the pathway of Cu-MnNiSi co-precipitation observed in irradiated (and thermally aged) Cu-bearing low-alloy steels [14][15][16]. Specifically, we focus on the mechanism of a transition of the coprecipitate from a Cu-core-MnNiSi-shell structure to a Cu-core-MnNiSi-appendage structure.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamics and kinetics of core-shell versus appendage co-precipitation morphologies: An example in the Fe-Cu-Mn-Ni-Si system

Shu¹,

Wells²,

Almirall³

et al. 2018

Acta Materialia

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What determines precipitate morphologies in co-precipitating alloy systems? We focus on alloys of two precipitating phases, with the fast-precipitating phase acting as heterogeneous nucleation sites for a second phase manifesting slower kinetics. Kinetic lattice Monte Carlo simulations show that the interplay between interfacial and ordering energies, plus active diffusion paths, strongly affect the selection of core-shell verses appendage morphologies. We study a FeCuMnNiSi alloy using the combination of atom probe tomography and simulations, and show that the ordering energy reduction of the MnNiSi phase heterogeneously nucleated on a pre-existing copper-rich precipitate exceeds the energy penalty of a predominantly Fe/Cu interface, leading to initial appendage, rather than core-shell, formation. Diffusion of Mn, Ni and Si around and through the Cu core towards the ordered phase results in subsequent appendage growth. We further show that in cases with higher primary precipitate interface energies and/or suppressed ordering, the coreshell morphology is favored.3

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“…For samples with large clusters and significant solute depletion from the matrix, there typically was a range of d max values that yielded a relatively constant number density [18]. In these samples with an extremely high density of very small clusters, a slight change in d max resulted in a correspondingly large change in the precipitate number density and volume fraction.…”

Section: Methodsmentioning

confidence: 99%

“…Selection of a very large d max resulted in the detection of clusters that would be present in any random solid solution. Styman et al [18] reported that for a typical RPV steel, these "random" clusters can be avoided by using d max 0.50 nm and N min ! 24 atoms, although the steel analyzed in their paper had a considerably higher solute (Cu, Ni, Mn, and Si) content (z4.25%) compared to the steel presented here (z2.5%).…”

Section: Methodsmentioning

confidence: 99%

Hardening and microstructural evolution of A533b steels irradiated with Fe ions and electrons

Watanabe

Arase

Yamamoto

et al. 2016

Journal of Nuclear Materials

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Quantitative methods for the APT analysis of thermally aged RPV steels

Cited by 37 publications

References 20 publications

Ion-irradiation-induced clustering in W–Re and W–Re–Os alloys: A comparative study using atom probe tomography and nanoindentation measurements

Ion-irradiation-induced clustering in W–Re and W–Re–Os alloys: A comparative study using atom probe tomography and nanoindentation measurements

Thermodynamics and kinetics of core-shell versus appendage co-precipitation morphologies: An example in the Fe-Cu-Mn-Ni-Si system

Hardening and microstructural evolution of A533b steels irradiated with Fe ions and electrons

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