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Diamond Deposition on Graphite in Hydrogen Microwave Plasma

2018· article· en· W2889212855 on OpenAlexvenueno aff
Jiaqi Zhu, Kaili Yao, Bing Dai, Victor Ralchenko, Guoyang Shu, Jiwen Zhao, Kang Liu, Lei Yang, A. P. Bolshakov, Jiecai Han

Bibliographic record

VenueJournal of Coating Science and Technology · 2018
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsGraphiteDiamondMicrowavePlasmaMaterials scienceHydrogenDeposition (geology)Composite materialChemistryComputer sciencePhysicsGeologyNuclear physicsOrganic chemistryTelecommunications

Abstract

fetched live from OpenAlex

Hydrogen plasma etching of graphite generates radicals that can be used for diamond synthesis by chemical vapor deposition (CVD). We studied the etching of polycrystalline graphite by a hydrogen microwave plasma, growth of diamond particles of the non-seeded graphite substrates, and characterized the diamond morphology, grain size distribution, growth rate, and phase purity. The graphite substrates served simultaneously as a carbon source, this being the specific feature of the process. A disorder of the graphite surface structure reduces as the result of the etching as revealed with Raman spectroscopy. The diamond growth rate of 3 – 5 µm/h was achieved, the quality of the produced diamond grains improving with growth time due to inherently nonstationary graphite etching process

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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