MétaCan
Menu
Back to cohort
Record W4407319015 · doi:10.1016/j.matchar.2025.114836

Direct observation of the influence of grain orientation on the corrosion of pipeline steels

2025· article· en· W4407319015 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials Characterization · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Failure Mechanisms
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCorrosionMetallurgyPipeline (software)Orientation (vector space)Grain boundaryMicrostructureMechanical engineeringGeometry

Abstract

fetched live from OpenAlex

This study aims to correlate the crystallographic texture of pipeline steels with their corrosion resistance. Electron backscatter diffraction (EBSD) was used to investigate the impact of grain orientation on the corrosion behavior of steels in various corrosive environments. Corrosion is influenced by several microstructural features, such as phase composition, grain size, grain boundary character, dislocation density, and crystallographic texture. To analyze the influence of specific grain orientations on corrosion resistance, steel samples were subjected to planned thermomechanical treatments to obtain samples with similar microstructures but different crystallographic textures. These samples were then exposed to controlled corrosive environments, and their microstructural changes were mapped using EBSD, scanning electron microscopy, and optical 3D profilometry. The results obtained indicated that corrosion response depends on grain orientations. Notably, the steel with more 〈111〉//ND and 〈100〉//ND grains exhibited improved corrosion resistance compared to the steel with a more random texture. The resistance of grains to active dissolution in all the corrosive solutions was in this order: {110} < {111} < {100}. The corrosion products were analysed using XPS, revealing variations in the composition and nature of the surface films on the samples across the selected electrolytes. • The corrosion behavior of specific grain orientations in pipeline steels was assessed in various environments. • Steel samples with similar microstructures but different crystallographic textures were examined. • SEM, EBSD, XPS and 3D optical profilometry were employed to map texture and grain-specific corrosion behavior. • <111>//ND and < 100>//ND texture exhibited superior corrosion resistance than random texture. • Corrosion resistance followed {110} < {111} < {100}, with <100>//ND being the most resistant to dissolution.

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 score0.037
Threshold uncertainty score0.289

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 science0.0000.000
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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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