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Record W3021578466 · doi:10.5006/c2019-13489

Characterization of Sulfur-assisted Degradation in Alloy 800

2019· article· en· W3021578466 on OpenAlexaff
Kevin Daub, J.M. Smith, S.Y. Persaud

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials Characterization Techniques
Canadian institutionsCanadian Nuclear LaboratoriesQueen's University
Fundersnot available
KeywordsDegradation (telecommunications)AlloySulfurMaterials scienceCharacterization (materials science)MetallurgyCorrosionComputer scienceNanotechnology

Abstract

fetched live from OpenAlex

Abstract Crack initiation testing was performed on Alloy 800 (Fe-33Ni-21Cr) blunt-notched tensile specimens exposed to 0.55 mol/kg sulfate solutions at 280 °C with pH280C 3. These conditions were anticipated to produce a variety of sulfur-assisted degradation phenomena in Alloy 800 for the purpose of analytical TEM characterization. Initial imaging of the notch of Alloy 800 tensile specimens revealed a combination of environmentally-assisted cracking (EAC), intergranular corrosion (IGC), and pitting corrosion occurring up to approximately 50 μm in depth. Energy dispersive x-ray spectroscopy (EDX) of EAC revealed a mixture of Ti and Cr-rich oxides, with a sulfur layer at the oxide-metal interface, either incorporated in the oxide or as a sulfide compound associated with Ti. EDX characterization of pitting corrosion indicated only minor oxide formation at the pit-metal interface, with clear identification of a nano-scale sulfur layer at the pit-metal interface, accompanied by a relatively low concentration of oxygen and major alloying elements. The sulfur layer could indicate adsorption of sulfur on the bare metal with near complete surface coverage during pitting, which effectively limits lateral oxide growth and accelerates metal dissolution in the underlying region. Further details on the mechanistic interpretation of results will be discussed.

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 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 score0.110
Threshold uncertainty score0.370

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)0.0000.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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
Published2019
Admission routes1
Has abstractyes

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