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MoS<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> Composite Coatings on A356 Alloy for Friction Reduction

2012· article· en· W2108649118 on OpenAlexaff
Zhi Jing Peng, Tse Cheng, Xueyuan Nie

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceLubricationTribologyCeramicDry lubricantPlasma electrolytic oxidationCoatingAlloyMetallurgyOxideFriction coefficientComposite numberComposite materialAluminiumAluminum oxideElectrolyte

Abstract

fetched live from OpenAlex

A plasma electrolytic oxidation (PEO) ceramic coating process was used to form ceramic MoS2/oxide coatings on aluminum. The tribological properties of the PEO-MoS2 coatings were evaluated by sliding wear tests under the dry and lubricate conditions at the room temperature. The test results showed that the solid lubrication MoS2 can be integrated into the coatings for friction reduction. The role of solid lubrication in reducing friction coefficient has been exhibited more significantly in the oil test condition than in the dry test condition.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0020.004
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.045
GPT teacher head0.327
Teacher spread0.283 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2012
Admission routes1
Has abstractyes

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