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Record W2801546340 · doi:10.1002/srin.201700566

Effect of Submicron‐Scale MnS Inclusions on Hydrogen Trapping and HIC Susceptibility of X70 Pipeline Steels

2018· article· en· W2801546340 on OpenAlexaff
Zhixian Peng, Jing Liu, Feng Huang, Qian Hu, Zhaoyang Cheng, Shuai Liu, Y. Frank Cheng

Bibliographic record

Venuesteel research international · 2018
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsNon-metallic inclusionsHydrogenMaterials scienceTransmission electron microscopyTrappingScanning electron microscopeInclusion (mineral)MetallurgyComposite materialNanotechnologyChemistryMineralogy

Abstract

fetched live from OpenAlex

In this work, the non‐metallic inclusions contained in a trial X70 pipeline steels are characterized by optical microscopy, scanning electron microscopy, energy‐dispersive X‐ray spectrum, and transmission electron microscopy. Statistical analysis is conducted to summarize the size and shape of the inclusions. The hydrogen trapping and the resulting hydrogen‐induced cracking (HIC) susceptibility of the steels are tested. Density functional theory is used to calculate the binding energy of hydrogen at MnS inclusions, and the impact of MnS inclusions on hydrogen trapping and the HIC susceptibility is evaluated. It is found that the majority of submicron scale inclusions are MnS, which serve as irreversible hydrogen traps. The content of the trapped hydrogen can be effectively decreased by controlling the size of MnS inclusions below submicron scale and distributing the inclusions uniformly in the steel. As a result, the susceptibility of the steel to HIC is reduced.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.399
Teacher spread0.356 · 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

Citations56
Published2018
Admission routes1
Has abstractyes

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