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Record W2313049885 · doi:10.1515/htmp-2014-0132

CVD Diamond Coating on Al-Interlayered FeCoNi Alloy Substrate: An Interfacial Study

2015· article· en· W2313049885 on OpenAlexafffund
Y.S. Li, Xiaoyu Sun, Lizhong Yang, E.Z. Kurmaev, Q. Yang

Bibliographic record

VenueHigh Temperature Materials and Processes · 2015
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDiamondNucleationSubstrate (aquarium)Raman spectroscopyChemical vapor depositionAlloyCoatingScanning electron microscopeChemical engineeringLayer (electronics)AdhesionComposite materialNanotechnologyOpticsChemistry

Abstract

fetched live from OpenAlex

Abstract In this study, an Al thin film interlayer of 80 nm thick has been applied on FeCoNi alloy substrate which possesses a low coefficient of thermal expansion, to enhance the interfacial adhesion of diamond films produced by microwave plasma-enhanced chemical vapor deposition. Characterization of the top deposit, interlayer and the underlying substrate was performed by Raman spectroscopy, energy dispersive X-ray analysis, X-ray photoelectronic spectroscopy, X-scanning electron microscopy and X-ray diffraction. The Al interlayer has effectively inhibited the formation of graphitic carbon and markedly enhanced the nucleation, growth and adhesion of diamond films. The beneficial role Al plays is primarily attributed to the formation of an alumina barrier layer on the substrate surface, as verified by interfacial analysis.

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 score0.005

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 head0.027
GPT teacher head0.291
Teacher spread0.264 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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