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Record W3183962337 · doi:10.1039/d1nr02843j

Moiré patterns of twisted bilayer antimonene and their structural and electronic transition

2021· article· en· W3183962337 on OpenAlexafffund
Qi An, Oussama Moutanabbir, Hong Guo

Bibliographic record

VenueNanoscale · 2021
Typearticle
Languageen
FieldMaterials Science
TopicZnO doping and properties
Canadian institutionsMcGill UniversityPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsBilayerFlatteningMoiré patternMaterials scienceCondensed matter physicsSpace (punctuation)PhysicsOpticsChemistryComputer scienceComposite material

Abstract

fetched live from OpenAlex

Interlayer twisting in two-dimensional (2D) van der Waals (vdW) heterostructures often leads to a periodic moiré pattern which is a superlattice structure on top of the original atomic lattice of the 2D layers. The formation of a moiré superlattice can be accompanied by a significant structural reconstruction and ultra-flat electronic bands. The moiré superlattice is typically built with a tunable scale by controlling the rotation angle θ between the individual 2D layers. In this paper, we report the structural reconstruction and electronic transition in moiré patterns of twisted bilayer antimonene, based on Kohn-Sham density functional theory calculations. Starting from rigid moiré structures, the atomic relaxation leads to an array of high-symmetry stacking domains with soliton boundaries through a vortex-like reconstruction. For twist angle θ≤ 6.01°, the impact of the structural reconstruction on the electronic bands becomes very significant, in the appearance of flat bands at the valence band edge, and no magic angle is required for the flat bands to appear in the 2D Sb moiré patterns. Both inhomogeneous interlayer hybridization and local strain are found to be responsible for the formation of these flat electronic bands.

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 score0.011
Threshold uncertainty score0.198

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 head0.010
GPT teacher head0.215
Teacher spread0.204 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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