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Record W4285802426 · doi:10.3390/coatings12071011

Effect of hBN on Corrosion and Wear Performances of DC Electrodeposited NiW and NiW–SiC on Brass Substrates

2022· article· en· W4285802426 on OpenAlexaff
Mina Dadvand, O. Savadogo

Bibliographic record

VenueCoatings · 2022
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceMetallurgyCeramicSilicon carbideCorrosionBoron carbideBrassTungsten carbideComposite numberNickelTungstenWaferSilicon nitrideComposite materialSiliconNanotechnologyCopper

Abstract

fetched live from OpenAlex

Crack-free and uniform nickel–tungsten (NiW) coatings and their composite coatings filled with ceramic particles such as silicon carbide (SiC) and hexagonal-boron nitride (hBN) were deposited on brass substrates by applying direct current (DC) waveforms. Among all coatings, NiW–SiC–hBN coatings displayed the noblest corrosion potential (−0.49 V) and lowest current density (4.36 × 10−6 A·cm−2). It also seems that addition of hBN and SiC ceramic particles to NiW matrix remarkably improved the wear performance of the NiW coatings. However, NiW–hBN exhibited the lowest wear volume (48.84 × 103 µm3) and the friction coefficient of 0.1 due to ultra–low friction coefficient of hBN particles.

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)0.000
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 score0.014
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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
Published2022
Admission routes1
Has abstractyes

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