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Record W2613036408 · doi:10.5006/c2011-11210

Passivity of Nuclear Steam Generator Tube Alloy in Lead-Contaminated Crevice Chemistries with Different pH

2011· article· en· W2613036408 on OpenAlexaff
B.T. Lu, Jing‐Li Luo, Yu-Cheng Lu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsAtomic Energy (Canada)University of Alberta
Fundersnot available
KeywordsCrevice corrosionBoiler (water heating)MetallurgyMaterials scienceAlloyPassivityContaminationLead (geology)Tube (container)Nuclear engineeringWaste managementComposite materialElectrical engineeringEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract The impacts of lead contamination on the passivity of UNS N06690 alloy were studied using samples passivated in simulated CANDU steam generator (SG) crevice chemistries at 300°C and a pH300°C range from 3.22 to 9.26. X-ray photoelectron spectroscopy (XPS) and secondary ion mass spectrometry (SIMS) analyses indicate that lead entered the anodic films but the level of lead impurities incorporated into the anodic films depended heavily on solution pH. In the alkaline chemistry, the presence of lead contamination promoted the incorporation of hydrogen and calcium. The ingress of lead hindered the dehydration during the passivation. Grazing incidence X-ray diffraction (GIXRD) analysis indicated that the lead incorporation would block the formation of spinel oxides during the passivation. Such an effect was enhanced with increasing pH and was hardly observed in the acidic chemistry. When lead contamination was absent, the anodic films were depleted of chromium in alkaline solution but were enriched with chromium in the acidic solution. The presence of lead contamination could reduce both the Cr-depletion in the alkaline chemistry and the Cr-enrichment in the acidic solution. Finally, an effort was made to outline the possible mechanisms for the lead-induced passivity degradation.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.000

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 head0.023
GPT teacher head0.195
Teacher spread0.172 · 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.

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

Citations5
Published2011
Admission routes1
Has abstractyes

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