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Record W2541860451 · doi:10.3390/ma10020147

Band Gap Tuning in 2D Layered Materials by Angular Rotation

2017· article· en· W2541860451 on OpenAlexaff
Javier Polanco-González, Jesús Alfredo Carranco-Rodríguez, José Luis Enríquez-Carrejo, Pierre Giovanni Mani-González, José Manuel Domínguez-Esquivel, Manuel Ramos

Bibliographic record

VenueMaterials · 2017
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMcMaster University
FundersInstituto de Ingeniería, Universidad Nacional Autónoma de MéxicoUniversity of Texas at El PasoConsejo Nacional de Ciencia y Tecnología
KeywordsCASTEPTungsten disulfideMaterials scienceHigh-resolution transmission electron microscopySeries (stratigraphy)TungstenBand gapRotation (mathematics)Molybdenum disulfideGrapheneHoneycombDensity functional theorySelenideSupercellElectronic band structureElectronic structureCondensed matter physicsComputational chemistryComposite materialNanotechnologyTransmission electron microscopyGeometryPhysicsOptoelectronicsChemistryMathematics

Abstract

fetched live from OpenAlex

We present a series of computer-assisted high-resolution transmission electron (HRTEM) simulations to determine Moiré patters by induced twisting effects between slabs at rotational angles of 3°, 5°, 8°, and 16°, for molybdenum disulfide, graphene, tungsten disulfide, and tungsten selenide layered materials. In order to investigate the electronic structure, a series of numerical simulations using density functional methods (DFT) methods was completed using Cambridge serial total energy package (CASTEP) with a generalized gradient approximation to determine both the band structure and density of states on honeycomb-like new superlattices. Our results indicated metallic transitions when the rotation approached 8° with respect to each other laminates for most of the two-dimensional systems that were analyzed.

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), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.007
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 studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.029
GPT teacher head0.290
Teacher spread0.261 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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