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Record W2414131422 · doi:10.1038/srep20660

Electron work functions of ferrite and austenite phases in a duplex stainless steel and their adhesive forces with AFM silicon probe

2016· article· en· W2414131422 on OpenAlexafffund
Liqiu Guo, Guomin Hua, Binjie Yang, Hao Lü, Lijie Qiao, Xianguo Yan, Dongyang Li

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaShell CanadaSuncor Energy Incorporated
KeywordsDuplex (building)Ferrite (magnet)Materials scienceSiliconAtomic force microscopyAusteniteWork (physics)Adhesive wearElectronMetallurgyNanotechnologyComposite materialMicrostructureChemistryWear resistancePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Local electron work function, adhesive force, modulus and deformation of ferrite and austenite phases in a duplex stainless steel were analyzed by scanning force microscopy. It is demonstrated that the austenite has a higher electron work function than the ferrite, corresponding to higher modulus, smaller deformation and larger adhesive force. Relevant first-principles calculations were conducted to elucidate the mechanism behind. It is demonstrated that the difference in the properties between austenite and ferrite is intrinsically related to their electron work functions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), 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

Citations71
Published2016
Admission routes2
Has abstractyes

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