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Record W2197887374 · doi:10.1038/srep18154

Interfacial valence electron localization and the corrosion resistance of Al-SiC nanocomposite

2015· article· en· W2197887374 on OpenAlexafffund
Sareh Mosleh-Shirazi, Guomin Hua, Farshad Akhlaghi, Xianguo Yan, Dongyang Li

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSuncor Energy Incorporated
KeywordsCorrosionMaterials scienceNanocompositeWork functionValence electronGalvanic cellComposite numberGalvanic corrosionElectronComposite materialNanoparticleMetallurgyNanotechnologyLayer (electronics)

Abstract

fetched live from OpenAlex

Microstructural inhomogeneity generally deteriorates the corrosion resistance of materials due to the galvanic effect and interfacial issues. However, the situation may change for nanostructured materials. This article reports our studies on the corrosion behavior of SiC nanoparticle-reinforced Al6061 matrix composite. It was observed that the corrosion resistance of Al6061 increased when SiC nanoparticles were added. Overall electron work function (EWF) of the Al-SiC nanocomposite increased, along with an increase in the corrosion potential. The electron localization function of the Al-SiC nanocomposite was calculated and the results revealed that valence electrons were localized in the region of SiC-Al interface, resulting in an increase in the overall work function and thus building a higher barrier to hinder electrons in the nano-composite to participate in corrosion reactions.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations69
Published2015
Admission routes2
Has abstractyes

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