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Record W2981311343 · doi:10.1088/2399-1984/ab4eff

Top-down bottom-up graphene synthesis

2019· article· en· W2981311343 on OpenAlex
Zishuai Zhang, Alison Fraser, Siyu Ye, Géraldine Merle, Jake E. Barralet

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNano Futures · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMontreal General HospitalBallard Power Systems (Canada)McGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneNanotechnologyMaterials scienceScale (ratio)Computer sciencePhysics

Abstract

fetched live from OpenAlex

With its unique physical properties, graphene creates an exceptional platform for fundamental science and a promising material for many applications. However, the large-scale production of graphene with high quality and few layers is still challenging. To understanding the preparation methods of the graphene and the influence of their process parameters on the electronic structure is not only crucial for developing its applications in research fields, but also vital for the future graphene industrial production. This work is an overview of the most state-of-the-art research approaches and latest advancement described in the literature for synthesizing graphene and their associated properties.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.019
Threshold uncertainty score0.998

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)0.0040.003

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 head0.260
Teacher spread0.250 · 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