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Record W4283766251 · doi:10.1038/s41529-022-00263-z

Unveiling nano-scaled chemical inhomogeneity impacts on corrosion of Ce-modified 2507 super-duplex stainless steels

2022· article· en· W4283766251 on OpenAlexafffund
Harishchandra Singh, Yi Xiong, Ekta Rani, Shubo Wang, Mourad Kharbach, Tian Zhou, Huai Yao, Yuran Niu, Alexei Zakharov, Graham King, Frank M. F. de Groot, Jukka Kömi, Marko Huttula, Wei Cao

Bibliographic record

Venuenpj Materials Degradation · 2022
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsCanadian Light Source (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Natural Science Foundation of ChinaAcademy of FinlandOulun YliopistoUniversity of SaskatchewanCanadian Light Source
KeywordsPassivationCorrosionMaterials scienceOxideMetallurgyChromiumDuplex (building)Layer (electronics)PassivityNano-NanometreComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract The widely used stainless steels and their deformed variants are anticorrosive in ambient conditions due to passivation layers composed of chromium oxides. Conventionally, corrosion and erosion of the steels are attributed to the breakdown of such layers but seldomly to the origin that depends on surface heterogeneity at the microscopic level. In this work, the nanometer-scaled chemical heterogeneity at the surface unveiled via spectro-microscopy and chemometric analysis unexpectedly dominates the breakdown and corrosion behavior of the cold-rolled Ce-modified 2507 super-duplex stainless steels (SDSS) over its hot-deformed counterpart. Though relatively uniformly covered by a native Cr 2 O 3 layer revealed by X-ray photoemission electron microscopy, the cold-rolled SDSS behaved poorly in passivity because of locally distributed Fe 3+ rich nano-islands over the Fe/Cr oxide layer. This atomic-level knowledge provides a deep understanding of corrosion of stainless steel and is expected to benefit corrosion controls of similar high-alloyed metals.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0090.000

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 head0.028
GPT teacher head0.276
Teacher spread0.248 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuenpj Materials DegradationSame topicHydrogen embrittlement and corrosion behaviors in metalsFrench-language works237,207