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Record W2091040168 · doi:10.5006/1.3287747

Effects of Iron Content on Microstructure and Crevice Corrosion of Grade-2 Titanium

2004· article· en· W2091040168 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 · 2004
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsWestern University
Fundersnot available
KeywordsCrevice corrosionMetallurgyMicrostructureMaterials scienceTitaniumCorrosion

Abstract

fetched live from OpenAlex

The effects of iron content on the microstructure and crevice corrosion of Grade-2 titanium (Ti-2) were studied using a galvanic coupling technique combined with optical microscopy and secondary ion mass spectrometry (SIMS) imaging. This study reveals that iron content has a significant effect on the microstructure and crevice corrosion behavior of Ti-2. The grain size decreased significantly with increasing iron content. For a Ti-2 material with medium iron content, crevice corrosion readily initiated and the metal exhibited extensive intergranular attack that could be associated with the more reactive iron-stabilized β-phase within the α-phase matrix, as revealed by SIMS imaging. By contrast, Ti-2 materials with low and high iron contents showed suppressed crevice attack. The smaller surface area of available grain boundaries in Ti-2 of low iron content could account for this limited attack. For the material with high iron content, SIMS imaging suggests that some TixFe intermetallic particles were formed. These particles may act as proton reduction catalysts and enhance crevice corrosion resistance via cathodic modification.

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)0.000
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 score0.005
Threshold uncertainty score0.694

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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