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Record W3024230216 · doi:10.1149/ma2020-0114967mtgabs

An Evaluation of Carbon Steel Corrosion in Nitrate Solution Using Electrochemical and Solution Analysis Methods

2020· article· en· W3024230216 on OpenAlexaff
M. Zakeri, Jiju M. Joseph, Otto Yong, J.C. Wren

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsOntario Power GenerationWestern University
Fundersnot available
KeywordsCorrosionOxidizing agentMaterials scienceRadiolysisCarbon steelSpent nuclear fuelHumidityMetallurgyChemistryNuclear chemistry

Abstract

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Nuclear power reactors offer a long term, cost efficient and sustainable method to produce electricity with very low greenhouse gas emissions. As nuclear reactors age and their lifetimes are extended, accurate assessment of the integrity and longevity of the reactor structural materials is increasingly important. Corrosion in the vicinity of a nuclear reactor core happens in the presence of high energy, ionizing radiation, which needs to be considered. In particular, the potential for accelerated (galvanic) corrosion attack on carbon steel (CS) adjacent to the dissimilar metal weld between CS (SA 36) and stainless steel (SS) (Type 304L) at the periphery of the annular gap must be addressed. The initial environment inside the gap of structural support for End Shield Cooling (ESC) System could be humid due to trapped water in its annular gap. The pH of the ESC System water is adjusted to around 10.4, but when it condenses onto the weld region the pH might change. The oxygen level, the humidity, and many other factors will change with time. In addition, ionizing radiation decomposes water into a range of redox active species ranging from highly oxidizing (e.g., •OH and H2O2) to highly reducing (e.g., •eaq -) whose concentrations evolve with time (1-4). Humid-air radiolysis produces nitric acid (HNO3), that can dissolve into the water (5). The radiation products will lower the pH of the water in the droplet. In this work, corrosion of CS is evaluated carefully, and the effect of all solution reactions on interfacial charge transfer reactions is considered. The effect of [NO3 -] and solution pH is studied by using potentiodynamic polarization experiments and corrosion potential measurements (Figure 1), along with solution analysis. The results of polarization measurements in the presence of 0.01 M and 0.1 M [NO3 -] (with initial pH 2.0) show that the oxidation reactions are implicitly influenced by the mass transfer of metal cations from the CS surface into the solution. Also, the comparison of the rate of corrosion measured using Tafel extrapolation method and those obtained from ICP measurements suggests that the Tafel extrapolation method enormously underestimate the corrosion rate (in this case by about 100 times for 8 h corrosion) and must be applied more carefully. In order to study the effect of pH, we have compared the electrochemical results in pH 2.0 and pH 6.0 obtained in different concentrations of nitrate. The results show that solution pH is a rate controlling factor and the metal oxidation is limited by the metal transfer even at potentials close to/at the corrosion potential, and this influence the rate of corrosion. The results of this work imply that solution reactions can alter the corrosion behavior of carbon steel and hence, its galvanic coupling with stainless steel. References: 1. J. C. Wren, in Nuclear Energy and the Environment, p. 271, American Chemical Society (2010). 2. J. W. T. Spinks and R. J. Woods, An introduction to radiation chemistry, John Wiley and Sons Inc, United States (1990). 3. J. M. Joseph, B. Seon Choi, P. Yakabuskie and J. Clara Wren, Radiat. Phys. Chem., 77, 1009 (2008). 4. P. A. Yakabuskie, J. M. Joseph and J. Clara Wren, Radiat. Phys. Chem., 79, 777 (2010). 5. R. S. Wittman, Radiolysis Model Sensitivity Analysis for a Used Fuel Storage Canister, in, p. Medium: ED; Size: PDFN, ; Pacific Northwest National Lab. (PNNL), Richland, WA (United States) (2013). Figure 1

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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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.363
Teacher spread0.294 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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