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Record W2059693983 · doi:10.7567/jjap.52.110110

Characterizing Edge and Stacking Structures of Exfoliated Graphene by Photoelectron Diffraction

2013· article· en· W2059693983 on OpenAlexfundno aff
Fumihiko Matsui, Ryo Ishii, Hiroyuki Matsuda, Makoto Morita, S. Kitagawa, T. Matsushita, Shinji Koh, Hiroshi Daimon

Bibliographic record

VenueJapanese Journal of Applied Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsGrapheneStackingBilayer grapheneRaman spectroscopyMaterials scienceGraphiteBilayerMonolayerDiffractionNanotechnologyCrystallographyOpticsChemistryComposite materialMembrane

Abstract

fetched live from OpenAlex

The two-dimensional C 1s photoelectron intensity angular distributions (PIADs) and spectra of exfoliated graphene flakes and crystalline graphite were measured using a focused soft X-ray beam. Suitable graphene samples were selected by thickness characterization using Raman spectromicroscopy after transferring mechanically exfoliated graphene flakes onto a 90-nm-thick SiO 2 film. In every PIAD, a Kagomé interference pattern was observed, particularly clearly in the monolayer graphene PIAD. Its origin is the overlap of the diffraction rings formed by an in-plane C–C bond honeycomb lattice. Thus, the crystal orientation of each sample can be determined. In the case of bilayer graphene, PIAD was threefold-symmetric, while those of monolayer graphene and crystalline graphite were sixfold-symmetric. This is due to the stacking structure of bilayer graphene. From comparisons with the multiple scattering PIAD simulation results, the way of layer stacking as well as the termination types in the edge regions of bilayer graphene flakes were determined. Furthermore, two different C 1s core levels corresponding to the top and bottom layers of bilayer graphene were identified. A chemical shift to a higher binding energy by 0.25 eV for the bottom layer was attributed to interfacial interactions.

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 score0.031
Threshold uncertainty score0.345

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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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