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Record W4403281707 · doi:10.1016/j.jmrt.2024.10.062

Microstructural characteristics of the internal oxidation zone of ferritic/martensitic steel exposed to LBE at 550°C for 1000 h

2024· article· en· W4403281707 on OpenAlexaff
Hao Wang, Fujian Zhang, Jun Xiao, Linjiang Chai, Qi Xu, Xin Yin, Ke Zhao, Ning Guo, Zhongwen Yao, Shaoyu Qiu

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsQueen's University
Fundersnot available
KeywordsMaterials scienceMartensiteMetallurgyInternal oxidationMicrostructureAlloy

Abstract

fetched live from OpenAlex

The oxidation products formed on ferritic/martensitic (F/M) steel exposed to oxygen-saturated lead-bismuth eutectic (LBE) at 550 °C for 1000 h were investigated using various characterizations. The results indicate that the corrosion products consist of three distinct layers from the inside to the outside: the inner oxidation zone (IOZ), a middle oxide layer, and the growth front facing LBE. In the IOZ, although O has penetrated the entire oxidation layer, the martensite laths and ferrite grains remain visible. However, significant segregation of Cr and O along the laths and grain boundaries has occurred, resulting in the formation of strip-shaped Cr 2 O 3 particles. In the matrix adjacent to the IOZ, Cr atoms have segregated at the grain boundaries and martensite laths, forming Cr-rich regions, while O atoms have not yet infiltrated. During the growth of Fe 3 O 4 grains, the priority formed strip-shaped Cr 2 O 3 particles are pushed toward the corrosion front until they detach from the F/M steel and disperse into the LBE, resulting in nearly pure Fe 3 O 4 grains in the middle layer. The gradient three-layer structure of the oxidation product is closely associated with the segregation of Cr and the gradient distribution of oxygen partial pressure (PO 2 ).

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)0.002
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 score0.002
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.036
GPT teacher head0.333
Teacher spread0.298 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

Explore more

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