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Record W2137309441 · doi:10.5006/1.3279916

Effect of Surface Preparation on Intergranular Stress Corrosion Cracking of Alloy 600 in Hydrogenated Steam

2008· article· en· W2137309441 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCORROSION · 2008
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectropolishingMaterials sciencePolishingStress corrosion crackingIntergranular corrosionMetallurgyCorrosionAlloyCrackingOxideStress (linguistics)Composite materialChemistryElectrolyte

Abstract

fetched live from OpenAlex

In the literature it is a common belief that electropolishing mitigates primary water stress corrosion cracking (PWSCC) because it removes superficial cold work. Here, it is shown that electropolished Alloy 600 (UNS N06600) exposed to hydrogenated steam undergoes internal Cr oxidation, whereas mechanical polishing induces external oxidation. This has implications for SCC initiation, which has been tested with different surface preparations (electropolishing and mechanical polishing) using reverse-U-bend (RUB) and C-ring samples. The results show a systematic trend that mechanically polished surfaces are more resistant to stress corrosion cracking (SCC) than electropolished surfaces. The mechanism involved in this increased resistance is thought to be related to short-circuit diffusion of Cr to the surface, which promotes external rather than internal oxidation. The role of compressive stress induced by mechanical polishing is a less-likely explanation of the observed effects. Mechanical polishing does not suppress PWSCC completely because if the external oxide layer breaks, the exposed material may continue to oxidize internally.

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.

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)0.000
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 score0.039
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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