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Growth of Si-Doped Polycrystalline Diamond Films on AlN Substrates by Microwave Plasma Chemical Vapor Deposition

2015· article· en· W2169300608 on OpenAlexvenueno aff
Vadim Sedov, А. В. Хомич, Victor Ralchenko, Artem Martyanov, Sergey Savin, O. N. Poklonskaya, N. S. Trofimov

Bibliographic record

VenueJournal of Coating Science and Technology · 2015
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsChemical vapor depositionMaterials scienceMicrowaveOptoelectronicsDopingDiamondCrystallitePlasmaPlasma-enhanced chemical vapor depositionPolycrystalline diamondNanotechnologyChemical engineeringMetallurgyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Microcrystalline diamond films doped with silicon have been grown on aluminum nitride substrates by a microwave plasma CVD. The doping has been performed via adding silane in various concentrations to CH4-H2 reaction gas mixture in course of the deposition process. The films produced at the substrate temperatures of 750 to 950°C have been characterized by SEM, AFM, Raman and photoluminescence (PL) spectroscopy to assess the effect of Si doping on the diamond structure. The doped films showed bright photoluminescence of silicon-vacancy (SiV) color centers at 738 nm wavelength as well as noticeable side band at 723 nm. The optimum doping condition (SiH4/CH4 = 0.6%), that maximize the SiV PL emission, was determined for the range of silane concentrations SiH4/CH4 (0.0 - 0.9%) explored. A further PL enhancement can be achieved by increase in the substrate temperature. The applied in situ doping from gas phase is shown to be an easy and effective method to incorporate Si in diamond in a controllable way.

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 score0.001
Threshold uncertainty score0.004

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 head0.013
GPT teacher head0.258
Teacher spread0.244 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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