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Record W4247287899 · doi:10.1149/ma2020-02473740mtgabs

Influence of Chloride Concentration on the Pitting Probability of Copper Using Coupled Multielectrode Arrays

2020· article· en· W4247287899 on OpenAlexaffabout
Sina Matin, Adam Morgan, Arezoo Tahmasebi, Mehran Behazin, Matt Davison, James J. Noël

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNuclear Waste Management OrganizationWestern University
Fundersnot available
KeywordsCorrosionPitting corrosionCopperCathodic protectionChlorideMetallurgyErosion corrosion of copper water tubesCrevice corrosionMaterials scienceAnoxic watersAnodeChemistryElectrodeEnvironmental chemistry

Abstract

fetched live from OpenAlex

The preferred disposal method for used nuclear fuel is to seal it in containers emplaced in a deep geologic repository (DGR) in a suitable rock formation. In Canada, the container is a robust steel vessel coated with 3 mm of Cu as a corrosion barrier. Eventually, the DGR environment will become anoxic, and in the absence of oxygen copper is thermodynamically stable in water. However, since exposure conditions will initially be oxidizing due to the presence of O 2 trapped on sealing the DGR, there is a possibility of localized corrosion. Mass balance calculations show that uniform corrosion during this period will be within the designed corrosion allowance, however, since the Cu coating is relatively thin and the required lifetime is long, the risk of pitting corrosion during this period must be carefully evaluated. Pitting corrosion susceptibility is determined by the corrosion potential of the material (E corr , the potential at which the rates of all anodic and cathodic reactions are equal), and the breakdown potential (E b , the potential at which the copper oxidation rate on the surface increases abruptly due to the breakdown of the protective film), both of which are distributed parameters, due to the stochastic nature of passive film rupture and any uncontrollable variations in the structure and local environment at the metal surface. It is important to note that the analysis is based on the concept that pitting is only possible if the E corr is equal to, or more positive than the E b . In this work, a multielectrode array was used to obtain a statistically meaningful set of potential measurements through the simultaneous monitoring of 30 electrodes to determine their corrosion and breakdown potentials. From the extensive database thus acquired, we defined the E corr and E b distributions for copper electrodes exposed to solutions containing various chloride concentrations. Our previous studies showed passive behavior of copper surface exposed to high pH solutions. Therefore, a more thorough evaluation of the relative values of E corr and E b is required to determine the susceptibility to pitting. Experiments were conducted by using multielectrode array at 25 C and pH 11. In alkaline solutions, copper oxide is more stable and can form a passive film, whereas chloride promotes the breakdown of the passive film. As the chloride concentration increased, values of E corr and E b decreased, suggesting that the solubility of copper increases with increasing chloride concentration. Therefore, the stability of the passive film is decreased in the presence of chloride. In general, the possibility of pitting increases with growing overlap between the distribution curves of E corr and E b . To evaluate the possibility of pitting, histograms and distribution curves of E corr and E b in the solutions what have different chloride concentrations were compared. In lower chloride concentrations, there was a very small overlap between E corr and E b ; consequently, the possibility of pitting under these conditions is very low. On the other hand, a high chloride concentration at high pH contributed to a higher probability of pitting, as the overlap between E corr and E b distributions increased.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.264
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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