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Frank-van der Merwe Growth in Bilayer Graphene

2021· preprint· en· W4210806143 on OpenAlexaff
Haozhe Wang, Zhenpeng Yao, Gang Seob Jung, Qichen Song, Marek Hempel, Tomás Palacios, Gang Chen, Markus J. Buehler, Alán Aspuru‐Guzik, Jing Kong

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsVector InstituteUniversity of TorontoCanadian Institute for Advanced Research
FundersAir Force Office of Scientific ResearchBasic Energy SciencesMultidisciplinary University Research InitiativeU.S. Department of EnergyU.S. Department of DefenseDivision of Materials ResearchOffice of ScienceOffice of Naval ResearchHarvard UniversityNational Science Foundation
KeywordsBilayer grapheneGrapheneBilayerMaterials scienceMonolayerStackingLayer (electronics)Condensed matter physicsNanotechnologyOptoelectronicsChemistryMembranePhysics

Abstract

fetched live from OpenAlex

Bilayer graphene has attracted significant interest due to its unique properties, including fascinating electrical behavior when one layer is slightly rotated relative to the other. However, the quality of large-area bilayer graphene is often limited by the layer-plus-island growth mode in which islands of thicker graphene present as unavoidable impurities. Here, we report the observation of the layer-by-layer, Frank-van der Merwe (FM) growth mode in bilayer graphene where multilayer impurities are suppressed. Instead of the conventional surface adhesive energy, it is found that interface adhesive energy is possible to be tuned with an oxidative pretreatment. The FM-grown bilayer graphene is of AB-stacking or with small-twisting-angle (θ = 0-5°), which is more mechanically robust compared to monolayer graphene, facilitating a free-standing wet transfer technology.

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 score0.002

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 head0.029
GPT teacher head0.293
Teacher spread0.264 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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