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Record W4399945279 · doi:10.1016/j.apsusc.2024.160584

Insight into the corrosion behaviors and mechanism of arc discharge plasma nitrided H13 steel in molten Al-Si

2024· article· en· W4399945279 on OpenAlex

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

VenueApplied Surface Science · 2024
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNitridingElectric arcMetallurgyMaterials sciencePlasmaArc (geometry)CorrosionPlasma arc weldingChemistryComposite materialLayer (electronics)EngineeringMechanical engineeringElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

AISI H13 steels were nitrided via arc discharge plasma nitriding (ADPN) with various arc currents (55, 70, 85, and 100 A) at 400 °C. The surface microhardness, wear resistance, and molten Al-Si corrosion resistance of nitrided H13 steel exhibited significant enhancements. The surface microhardness and wear resistance of nitrided H13 steel were significantly improved due to the lubricity property of the ɛ-Fe 3 N phase formed on the surface and the solid solution strengthening of the α N . Meanwhile, the nitrided layer led to a change in the corrosion mechanism from dissolution to pitting. Compressive internal stresses in the nitrided layer inhibited crack formation and promoted passivation layer formation. The high-efficiency ADPN process not only yields a wear-resistant surface but also provides a potential solution for improving the corrosion resistance against molten alloys.

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.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.218
Teacher spread0.208 · 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