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Enregistrement W3185251279 · doi:10.1149/ma2021-0118800mtgabs

Electrochemistry of High Temperature Corrosion of Alloys in Molten Salts Relevant to Future Nuclear Reactors

2021· article· en· W3185251279 sur OpenAlexaff
Touraj Ghaznavi, Roger Newman

Notice bibliographique

RevueECS Meeting Abstracts · 2021
Typearticle
Langueen
DomaineMaterials Science
ThématiqueNanoporous metals and alloys
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésCorrosionMaterials scienceMolten saltMetallurgyDissolutionAlloyEutectic systemElectrolyteCoolantIntermetallicElectrochemistryChemical engineeringChemistryThermodynamics

Résumé

récupéré en direct d'OpenAlex

Eutectic molten salts are coolant candidates for molten salt-cooled nuclear reactors; however, alloy corrosion is the key materials-compatibility issue [1]. Corrosion in molten salts may involve thermodynamic considerations, thermal gradient-driven corrosion, dissimilar material corrosion, dealloying, and impurity-driven corrosion [2, 3]. New alloys must be developed, and their corrosion behaviour merits special attention at a fundamental level. We aim to understand industrial alloy behaviour through study of model alloys, leading to insights relevant to materials performance in Molten Salt Reactors. In the field of molten salt corrosion, dissolution of alloying elements is mostly discussed in terms of one-dimensional diffusion of a more easily dissolved element from the bulk to the surface, which necessarily involves lattice diffusion of metals. We report on electrochemical study of corrosion mechanisms in Fe-(Cr)-Ni model and industrial alloys, and report on a study of critical alloy compositions and porosity formation upon dealloying of one or more electrochemically reactive components in molten salts. Dealloying is selective electrolytic dissolution of one or more active elements from a metallic solid solution or intermetallic compound [4, 5]. The operative mass transport process in aqueous dealloying is diffusion of the more-noble component at the solid-electrolyte interface – enhanced by poorly-understood electrolyte effects on the diffusivity. The parting limit in dealloying is the minimum content of less-noble element(s) for dealloying, below which the dealloying is prevented by a passive layer of more-noble elements on surface formed at initial stage of corrosion. The more usual case (i.e. AgAu, CuAu, etc.) is a threshold of ca. 55-60 at. % less-noble element (Ag and Cu, respectively) [6]. According to Artymowicz et al. [7] the underlying parting limit is very close to 60 at. %, but increasing kinetics of surface diffusion could drop the parting limit to ca. 55 at. % in systems studied to that date. Fe-(Cr)-Ni model alloys were prepared using a Cold Crucible Induction Levitation Melter. Electrochemical studies were done using a well-controlled electrochemical cell for corrosion study in molten chloride salts. We have developed Mg|Mg 2+ reference electrode (RE) for our eutectic chloride salts and it is found to be a reliable RE, as far as we can see from our electrochemical measurements (open circuit potential, cyclic polarization, and polarization resistance). Impurity contents were monitored electrochemically, and final water removal was carried out using Mg. Characterization is being carried out by analytical electron microscopy, X-ray diffraction and secondary ion mass spectrometry. We found that up to a certain temperature, there is dealloying of the type observed in aqueous solutions, with porosity formation and parting limits. In brief, porosity and parting limits were observed in molten salts, exactly as predicted, except that the de-alloying threshold for electrolytic dissolution of the less-noble element(s) was dropped by several percent, compared with aqueous solutions. This is due to the very fast surface diffusion of more-noble metal in the molten chloride salt. Dealloyed layers were nearly pure nickel, but with residual Fe and/or Cr at the ligament cores in the porous structure. Correspondingly, the porosity is very coarse, and shows new features such as secondary corrosion through the ligament cores, as shown in Fig. 1. More Ni suppresses dealloying in both Fe-Ni and Fe-Cr-Ni model alloys, but more Fe and Cr promote oxide formation in binary and ternary alloys where dealloying propagates below an oxide layer of Fe and Cr in binary and ternary alloys. So, to some extent there is a balance, masking the underlying dependency of dealloying on Ni content. This type of dealloying is mediated by surface diffusion. At higher temperatures, there is a shift to different mechanisms involving lattice diffusion in the metal and porosity changes its appearance. Even then, there is not necessarily a planar dealloying front – more of a “negative dendrite” type of interface would appear [8]. At very high homologous temperature the dealloying feature will revert to that mostly described in the molten salt literature, with planar interfaces. References [1] V. Ignatiev and A. Surenkov, Journal of Nuclear Materials, 441, 592-603 (2013). [2. K. Sridharan and T.R. Allen, Corrosion in Molten Salts, in Molten Salts Chemistry, Elsevier, 241-267 (2013). [3] A. M. Kruizenga, Sandia National Laboratories, Livermore, CA, Report No. SAND2012-7594 (2012). [4] R.C. Newman, Dealloying, in Shreir's Corrosion (4th ed.). Elsevier. 2, 801-809 (2010). [5] R.C. Newman, et al., Corrosion Science, 28, 873-886 (1988). [6] A.J. Forty and P. Durkin, Philosophical Magazine A, 42, 295-318 (1980). [7] D. Artymowicz et al., Philosophical Magazine, 89, 1663-1693 (2009). [8] Q. Chen and K. Sieradzki, Journal of the Electrochemical Society, 160, C226-C231 (2013). Figure 1

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,674

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,216
Écart entre enseignants0,210 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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