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Record W2952210007 · doi:10.1088/2053-1583/ab29b2

2D printing of graphene: a review

2019· review· en· W2952210007 on OpenAlexafffund
Elahe Jabari, Farid Ahmed, Farzad Liravi, Ethan B. Secor, Liwei Lin, Ehsan Toyserkani

Bibliographic record

Venue2D Materials · 2019
Typereview
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaFedDev Ontario
KeywordsGrapheneScalabilityNanotechnologyElectronicsMaterials scienceComputer scienceProcess (computing)InkwellEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The exceptional properties of graphene have inspired widespread efforts to integrate this two-dimensional (2D) material in functional applications in recent years. Within the broad spectrum of graphene processing frameworks, low-cost methods such as scalable, liquid-phase patterning are typically considered among the most straightforward routes for device integration. These ink-based printing methods require parallel research in graphene dispersion engineering, print process optimization, and post-processing methods to enhance target functional properties for a wide range of device applications in flexible electronics, energy storage, display technologies, and sensing. This review examines the current state-of-the-art and future prospects for integrating graphene and graphene composites with these versatile printing techniques to accelerate the development of scalable and low-cost graphene-based devices.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.102
GPT teacher head0.398
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations71
Published2019
Admission routes2
Has abstractyes

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