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Record 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 on OpenAlexaff
Mojtaba Momeni, Mehran Behazin, J.C. Wren

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsWestern University
Fundersnot available
KeywordsOxideCorrosionDissolutionAlloyOverpotentialMetalMaterials scienceFlux (metallurgy)ThermodynamicsInorganic chemistryElectrochemistryChemistryMetallurgyPhysical chemistryElectrodePhysics

Abstract

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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, k MO (t), changes with time as the oxide grows but the rate constant of dissolution, k diss , 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; f k-MO (t) = k MO (t)/(k MO (t) + k diss ) and f k-diss (t) = 1 - f k-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.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.273
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2015
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