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Record W4386923993 · doi:10.1038/s41529-023-00394-x

Effect of nitrogen content on corrosion behavior of high-nitrogen austenitic stainless steel

2023· article· en· W4386923993 on OpenAlex
Fengyin Gao, Yanxin Qiao, Jian Chen, Lanlan Yang, Huiling Zhou, Zhibin Zheng, Lianmin Zhang

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".

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

Venuenpj Materials Degradation · 2023
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsWestern University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceX-ray photoelectron spectroscopyPassivationCorrosionNitrogenAustenitic stainless steelScanning electron microscopeAusteniteElectron backscatter diffractionMetallurgyGrain boundaryGrain sizeElectrochemistryNuclear chemistryAnalytical Chemistry (journal)Chemical engineeringMicrostructureComposite materialElectrodeLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

Abstract A series of electrochemical tests combined with the techniques of scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and X-ray photoelectron spectroscopy (XPS) were used to study the effect of nitrogen content on the composition, structure and protectiveness of passive films, which were formed on the surfaces of high-nitrogen austenitic stainless steels (HNSS) in 0.5 mol/L NaCl solution. The results showed that the HNSS with higher nitrogen content had a larger proportion of low-angle grain boundaries, and it also had a lower corrosion current density in 0.5 mol/L NaCl solution and thus a lower corrosion rate. The existence of a larger proportion of stable oxides (e.g., Cr 2 O 3 ) in the passive films facilitates the passivation/repassivation process and contributes to the high corrosion resistance of HNSS.

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 score0.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.286
Teacher spread0.254 · 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