MétaCan
Menu
Back to cohort
Record W2588981081 · doi:10.1038/srep42139

Corrosion resistance of monolayer hexagonal boron nitride on copper

2017· article· en· W2588981081 on OpenAlexafffund
Farzaneh Mahvash, Shimaa Eissa, Tarik Bordjiba, Ana C. Tavares, Thomas Szkopek, Mohamed Siaj

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsInstitut National de la Recherche ScientifiqueMcGill UniversityUniversité du Québec à Montréal
FundersFonds de recherche du Québec – Nature et technologiesUniversité de MontréalNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCorrosionMaterials scienceChemical vapor depositionCopperCoatingHexagonal boron nitrideBoronMonolayerBoron nitrideMetallurgyLayer (electronics)Thermal stabilityChemical engineeringChemical stabilityCyclic voltammetryNitrideElectrochemistryNanotechnologyChemistryElectrodeGrapheneOrganic chemistry

Abstract

fetched live from OpenAlex

Hexagonal boron nitride (hBN) is a layered material with high thermal and chemical stability ideal for ultrathin corrosion resistant coatings. Here, we report the corrosion resistance of Cu with hBN grown by chemical vapor deposition (CVD). Cyclic voltammetry measurements reveal that hBN layers inhibit Cu corrosion and oxygen reduction. We find that CVD grown hBN reduces the Cu corrosion rate by one order of magnitude compared to bare Cu, suggesting that this ultrathin layer can be employed as an atomically thin corrosion-inhibition coating.

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 score0.003
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)0.0010.000

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 head0.029
GPT teacher head0.308
Teacher spread0.278 · 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

Citations142
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicGraphene research and applicationsFrench-language works237,207