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Record W2094597587 · doi:10.1116/1.3490402

Effects of nanoscale Ni, Al, and Ni–Al interlayers on nucleation and growth of diamond on Si

2010· article· en· W2094597587 on OpenAlexafffund
Y. S. Li, Yongbing Tang, Q. Yang, Akira Hirose

Bibliographic record

VenueJournal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena · 2010
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNucleationDiamondMaterials scienceChemical vapor depositionSubstrate (aquarium)SiliconWaferCarbon fibersMaterial properties of diamondChemical engineeringCrystallographyAnalytical Chemistry (journal)NanotechnologyMetallurgyComposite materialChemistryComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

Diamond nucleation experiments on Si wafers, precoated with Ni, Al, and Ni–Al duplex intermediate layers, have been conducted in a microwave plasma enhanced chemical vapor deposition reactor. The diamond nucleation density is dependent on the thickness of the single Ni interlayer and also the ratio of Ni/Al. The diamond nucleation density increases with the Ni thickness up to approximately 100 nm. Above 100 nm, decrease in the nucleation density is observed. The nondiamond carbon concentration increases when the Ni thickness increases from 40 to 200 nm, along with a simultaneous increase of nondiamond carbon accumulation on the Si substrate surface. The diamond grown on Si with an Al interlayer is of high purity but of low nucleation density. For the Ni–Al duplex interlayer, increase of Al fraction enhances both the purity and nucleation density of diamond, and markedly reduces the formation of nondiamond carbon on the Si substrate surfaces.

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)0.003
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 score0.011
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.003
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 head0.007
GPT teacher head0.233
Teacher spread0.227 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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