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Record W2155879464 · doi:10.1039/c4nr04445b

Layer speciation and electronic structure investigation of freestanding hexagonal boron nitride nanosheets

2014· article· en· W2155879464 on OpenAlexaff
Jian Wang, Zhiqiang Wang, Hyunjin Cho, Myung Jong Kim, Tsun‐Kong Sham

Bibliographic record

VenueNanoscale · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsWestern UniversityCanadian Light Source (Canada)University of Saskatchewan
Fundersnot available
KeywordsHexagonal boron nitrideMaterials scienceBoron nitrideHexagonal crystal systemNanotechnologyLayer (electronics)BoronChemical engineeringCrystallographyGrapheneChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Chemical imaging, thickness mapping, layer speciation and polarization dependence have been performed on single and multilayered (up to three layers and trilayered nanosheets overlapping to form 6 and 9 layers) hexagonal boron nitride (hBN) nanosheets by scanning transmission X-ray microscopy. Spatially-resolved XANES directly from freestanding regions of different layers has been extracted and compared with sample normal and 30° tilted configurations. Notably a double feature σ* excitonic state and a stable high energy σ* state were observed at the boron site in addition to the intense π* excitonic state. The boron projected σ* DOS, especially the first σ* exciton, is sensitive to surface modification, particularly in the single layered hBN nanosheet which shows more significant detectable contaminants and defects such as tri-coordinated boron/nitrogen oxide. The nitrogen site has shown very weak or no excitonic character. The distinct excitonic effect on boron and nitrogen was interpreted to the partly ionic state of hBN. Bulk XANES of hBN nanosheets was also measured to confirm the spectro-microscopic STXM result. Finally, the unoccupied electronic structures of hBN and graphene were compared.

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 score0.067
Threshold uncertainty score0.265

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 head0.243
Teacher spread0.232 · 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

Citations22
Published2014
Admission routes1
Has abstractyes

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