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Record W2994342889 · doi:10.1002/srin.201900536

Influence of Nickel on High‐Temperature Oxidation and Characteristics of Oxide Layers in Two High‐Strength Steels

2019· article· en· W2994342889 on OpenAlexaff
Ali Vedaei-Sabegh, Jean-Benoît Morin, Mohammad Jahazi

Bibliographic record

Venuesteel research international · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsOxideMaterials scienceAlloyThermogravimetryMicrostructureDiffusionMetallurgyLayer (electronics)Scanning electron microscopeHigh-temperature corrosionOxygenActivation energyNickelAnalytical Chemistry (journal)Chemical engineeringComposite materialChemistryThermodynamicsPhysical chemistry

Abstract

fetched live from OpenAlex

The oxidation behaviors of two high‐strength medium carbon steels with different Ni concentrations are investigated by means of differential thermal analysis equipped with thermogravimetry (TG). Four different oxidation temperatures and five oxidation times are tested. A parabolic behavior is observed for the oxide growth in both alloys; however, the relative increases in the oxidation rate from one temperature to another are not similar for both alloys, and the oxidation kinetics of the Ni‐rich steel are significantly lower than those of the low Ni alloy. On the basis of the TG results, the activation energy for the oxidation of the two alloys is determined. The influence of Ni content on the microstructure and characteristics of the different oxide layers is studied using a combination of laser confocal microscopy, electron microscopy, and X‐ray diffraction (XRD) analysis. The results revealed a clear influence of Ni on the nature and relative presence of different oxide layers. The results are interpreted in terms of the influence of Ni on the diffusion of oxygen through the oxide layer.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.317
Teacher spread0.303 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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