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Record W2007275785 · doi:10.1520/jai101239

Crevice Corrosion of Grade-12 Titanium

2008· article· en· W2007275785 on OpenAlexaff
X. He, James J. Noël, David W. Shoesmith

Bibliographic record

VenueJournal of ASTM International · 2008
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsWestern University
Fundersnot available
KeywordsCrevice corrosionMaterials scienceTitaniumCorrosionMetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract The crevice corrosion of ASTM grade-12 titanium (Ti-12; 0.8 wt % Ni, 0.3 wt % Mo) has been studied in neutral 0.27 mol/L NaCl at temperatures up to 120°C using a galvanic coupling technique. The results were compared to those of similar experiments conducted on ASTM grade-2 titanium (Ti-2) specimens containing various amounts of iron impurity. The microstructural properties of the Ti-12 were determined by standard metallographic techniques and transmission electron microscopy. Penetration depth profiles were determined using metallographic and image analysis techniques. The redistribution of alloying elements (Ni, Mo) and impurities (Fe), and the location of absorbed hydrogen, were determined by secondary ion mass spectrometry (SIMS) imaging. The rate and extent of crevice propagation were significantly suppressed on Ti-12 compared to Ti-2, especially at higher temperature (120°C). Up to 97 % of the total amount of crevice propagation was driven by proton reduction inside the creviced area rather than by oxygen reduction outside the crevice. SIMS imaging shows that Ni accumulates on the corroding surface, probably in the form of residual Ti2Ni particles. Proton reduction is catalyzed on these particles, leading to their hydriding and an increase in the relative area of internal cathodes compared to available anodic surface area. This self-induced “cathodic modification” effect leads to repassivation of the corroded site before extensive damage can be sustained.

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)0.000
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 score0.013
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.287
Teacher spread0.252 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

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