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Record W2959987812 · doi:10.1080/0144235x.2019.1634319

Chemical vapour deposition of graphene: layer control, the transfer process, characterisation, and related applications

2019· article· en· W2959987812 on OpenAlexafffund
Xiaohua Yang, Gaixia Zhang, Jai Prakash, Zhangsen Chen, Marc A. Gauthier, Shuhui Sun

Bibliographic record

VenueInternational Reviews in Physical Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilInstitut national de la recherche scientifiqueShastri Indo-Canadian Institute
KeywordsGrapheneChemical vapor depositionNanotechnologyMaterials scienceNanomaterials

Abstract

fetched live from OpenAlex

Graphene, one of the most promising two-dimensional (2D) nanomaterials, has gained substantial attention in several areas of materials science. Due to its unique mechanical, electrical, optical, and thermal properties, graphene-based materials have triggered both numerous fundamental studies and technological applications. Out of several synthetic methods, chemical vapour deposition (CVD) has emerged as one of the most promising methods for the production of large areas of high quality single-crystal graphene. This review introduces the fundamental growth mechanisms of CVD graphene, alongside the various parameters and substrates employed in this process. Furthermore, new developments in the CVD synthesis of monolayer and few-layer graphene are presented, as well as advanced techniques for analysing the fine structure and properties of graphene. Moreover, a detailed discussion of the transfer processes used for practical applications of CVD graphene is provided, with emphasis on their fundamental aspects. This review concludes with an outlook on presently challenging issues, prospects and applications of graphene.

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 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 score0.014
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.291
Teacher spread0.281 · 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

Citations55
Published2019
Admission routes2
Has abstractyes

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