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Record W2521387233 · doi:10.1021/acs.jpcc.6b06789

Tuning Magnetic States of Planar Graphene/<i>h-</i>BN Monolayer Heterostructures via Interface Transition Metal-Vacancy Complexes

2016· article· en· W2521387233 on OpenAlexafffund
Bin Ouyang, Jun Song

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeterojunctionMaterials scienceSpintronicsCondensed matter physicsDensity functional theoryGrapheneVacancy defectPlanarDangling bondMonolayerAtomic orbitalMagnetic momentBand gapAtom (system on chip)ElectronNanotechnologyFerromagnetismOptoelectronicsChemistryComputational chemistryPhysicsSilicon

Abstract

fetched live from OpenAlex

Planar graphene/ h -BN (GPBN) heterostructures promise low-dimensional magnetic semiconductor materials of tunable bandgap. In the present study, interplay between 3d transition metal (TM) atoms and single vacancies (SVs) at the armchair interface in a planar GPBN monolayer was investigated through first principle density functional theory calculations. The TM-SV complexes were found to give rise to a rich set of magnetic states, originated from the interactions between valence electrons of the TM atom with dangling orbitals at the SV. The magnetic state at a TM-SV complex was further shown to be tunable upon the application of strain and electric field. The present study suggests a route to enrich and engineer the magnetic states of planar GPBN heterostructures, providing new insights for the design of tunable low-dimensional spintronic 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: 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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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Same venueThe Journal of Physical Chemistry CSame topicGraphene research and applicationsFrench-language works237,207