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Enregistrement W2268201198 · doi:10.1149/ma2015-02/14/710

Mcb (Mass and Charge Balance) Model Simulation of Corrosion of Co-Cr Alloy Stellite-6

2015· article· en· W2268201198 sur OpenAlexaff
Mojtaba Momeni, Mehran Behazin, J.C. Wren

Notice bibliographique

RevueECS Meeting Abstracts · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueEngineering Applied Research
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésOxideCorrosionDissolutionAlloyOverpotentialMetalMaterials scienceFlux (metallurgy)ThermodynamicsInorganic chemistryElectrochemistryChemistryMetallurgyPhysical chemistryElectrodePhysics

Résumé

récupéré en direct d'OpenAlex

Understanding of oxide formation and growth on an alloy is very important in predicting its corrosion behaviour. Several models for oxide growth as well as metal dissolution kinetics in the presence of an oxide layer have been developed. These models focus on describing the transport of charged species through the oxide film and the electrochemical reactions at the metal/oxide and oxide/solution interfaces, although the detailed description and formulation of these processes vary. However, these models do not consider the type of oxide that can form. Consequently, the models have limited capability of predicting changes in the corrosion rate with time as corrosion progresses, or the dependence of corrosion kinetics on the solution redox conditions. Recently, we have developed a corrosion model that can predict the rates of metal oxidation, oxide growth and dissolution simultaneously as a function of time. The model imposes reaction thermodynamics constraints, and mass and charge balance (MCB) requirements on corrosion reaction rates and hence is labelled the MCB model [1]. The mass and charge balance requirements dictate that the flux of metal cations created by oxidation of metal atoms at the m|ox interface (the oxidation flux) must be equal to the sum of the fluxes of the metal cations forming an oxide at the oxide/solution interface (the oxide formation flux) and the flux of metal cations dissolving into solution (the dissolution flux). The oxidation flux is calculated by using a modified Butler-Volmer equation with an effective overpotential that is defined as a function of the equilibrium potential of the metal oxidation and the potential drop across the oxide layer that is growing. Both the oxide formation dissolution fluxes have a first-order dependence on the oxidation flux. The first-order rate constant for the oxide formation follows an Arrhenius dependence with an activation energy that increases linearly with oxide thickness. The dissolution rate constant depends on surface hydration and the solution environment (pH and temperature), but is independent of the oxide thickness. Consequently, under constant solution conditions the rate constant for the oxide formation, kMO(t), changes with time as the oxide grows but the rate constant of dissolution, kdiss, is constant with time. Due to a mass balance constraint and competition between the two processes for the metal ions, the oxide formation and dissolution fluxes cannot vary independently. The fraction of the oxidation flux that leads to oxide formation or dissolution depends on their rate constants; fk-MO(t) = kMO(t)/(kMO(t) + kdiss) and fk-diss(t) = 1 - fk-MO(t), respectively. In this paper, we present MCB model simulation results of potentiostatic polarization experiments performed on Co-Cr alloy Stellite-6 [2]. The simulations results are compared with the experimental measurements of the corrosion current as a function of time and the final composition and structure of the oxide(s) that formed. In these simulations, the parameters such as rate constants, exchange current density and field strength (or specific potential drop) across an oxide layer were kept constant for a specific pH and temperature. The main rate parameter that varies with pH and temperature was . This rate constant ratio is higher under conditions which promote oxide formation over dissolution, such as high pHs where the solubility of metal cations is low. The MCB model with the same model parameters was then applied to different sets of experimental data which include measurements of corrosion potential as a function of time and determination of the amounts of dissolved metals in coupon corrosion tests conducted in sealed quartz vials. The excellent agreement between the model results and experimental data over a range of polarization potential, pH and temperature indicates that the MCB model is a valuable method for simulating time-dependent corrosion behaviour while an oxide film is changing. References [1] M. Momeni, J.C. Wren, Faraday Discussions (2015) DOI 10.1039/C4FD00244J. [2] M. Behazin et al. Electrochimica Acta 134 (2014) 399–410.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,057
Score d'incertitude au seuil0,113

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,028
Tête enseignante GPT0,273
Écart entre enseignants0,245 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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
Publié2015
Routes d'admission1
Résumé présentoui

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