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Record W1947429798 · doi:10.1002/pssb.201451176

Carbon nanotubes overgrown and ingrown with nanocrystalline diamond deposited by different CVD plasma systems

2014· article· en· W1947429798 on OpenAlexaff
M. Varga, Viliam Vretenár, Viera Skákalová, Alexander Kromka

Bibliographic record

Venuephysica status solidi (b) · 2014
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsDiamondChemical vapor depositionMaterials scienceCarbon nanotubeRaman spectroscopyScanning electron microscopeComposite numberMaterial properties of diamondComposite materialNanotechnologyMicrowaveChemical engineeringPlasma etchingEtching (microfabrication)Optics

Abstract

fetched live from OpenAlex

In this work we investigate the growth of diamond film on a porous single‐wall carbon nanotubes (SWNT) paper by employing three different Chemical Vapour Deposition (CVD) systems: plasma‐assisted hot filament, focused microwave plasma and linear antenna microwave plasma. We observe that the diamond growth is strongly affected by the different conditions in each of the CVD processes resulting in different SWNT/diamond composite structures. It is also found that only the linear antenna microwave plasma CVD process allows penetration of the activated growth species into the volume of the SWNT paper, whereas using the other methods, the diamond grains are formed on the SWNT paper surface only. Moreover, the growth of diamond structures within the substrate volume is strongly influenced by the CH 4 /CO 2 /H 2 gas content ratio. A key factor influencing the formation of a compact SWNT/diamond composite is keeping the equilibrium between two competitive processes: establishing conditions where the diamond structure can grow while etching/damage of the SWNT paper is still kept at a minimum. The morphology and chemical composition of formed composite materials were characterized by scanning electron microscopy and Raman spectroscopy.

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 categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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.

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

Citations6
Published2014
Admission routes1
Has abstractyes

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