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Intense conductivity suppression by edge defects in zigzag MoS<sub>2</sub>and WSe<sub>2</sub>nanoribbons: a density functional based tight-binding study

2016· article· en· W2526208875 on OpenAlexaff
F W N Silva, Ana Luiza Mariano Torres Costa, Lei Liu, Eduardo B. Barros

Bibliographic record

VenueNanotechnology · 2016
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsNanoacademic Technologies
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsZigzagConductanceDensity functional theoryMaterials scienceCondensed matter physicsFermi levelEnhanced Data Rates for GSM EvolutionTight bindingVacancy defectConductivityFermi energySIESTA (computer program)Density of statesElectronic structureElectronAb initio quantum chemistry methodsPhysicsComputational chemistryChemistryMoleculeGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

The effects of edge vacancies on the electron transport properties of zigzag MoS2/WSe2 nanoribbons are studied using a density functional theory (DFT)-based tight-binding model with a sp(3)d(5) basis set for the electronic structure calculation and applying the Landauer-Büttiker approach for the electronic transport. Our results show that the presence of a single edge vacancy, with a missing MoS2/WSe2 triplet, is enough to suppress the conductance of the system by almost one half for most energies around the Fermi level. Furthermore, the presence of other single defects along the same edge has little effect on the overall conductance, indicating that the conductance of that particular edge has been strongly suppressed by the first defect. The presence of another defect on the opposite edge further suppresses the quantum conductance, independently of the relative position between the two defects in opposite edges. The introduction of other defects cause the suppression to be energy dependent, leading to conductance peaks which depend on the geometry of the edges. The strong conductance dependence on the presence of edge defects is corroborated by DFT calculations using SIESTA, which show that the electronic bands near the Fermi energy are strongly localized at the edge.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.232
Teacher spread0.216 · 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.

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

Citations9
Published2016
Admission routes1
Has abstractyes

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