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Record W2773709397 · doi:10.1063/1.5005958

Control of interlayer physics in 2H transition metal dichalcogenides

2017· article· en· W2773709397 on OpenAlexaff
Kuang-Chung Wang, Teodor K. Stanev, Daniel Valencia, James Charles, Alex Henning, Vinod K. Sangwan, Aritra Lahiri, Daniel Mejia, Prasad Sarangapani, Michael Povolotskyi, Aryan Afzalian, Jesse Maassen, Gerhard Klimeck, Mark C. Hersam, Lincoln J. Lauhon, Nathaniel P. Stern, Tillmann Kubis

Bibliographic record

VenueJournal of Applied Physics · 2017
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsDalhousie University
FundersDivision of Materials ResearchNational Centre for Supercomputing ApplicationsUniversity of Illinois at Urbana-ChampaignNorthwestern UniversityMaterials Research Science and Engineering Center, Harvard UniversityPurdue UniversityDivision of Emerging Frontiers in Research and InnovationNational Science Foundation
KeywordsCondensed matter physicsTransition metalMonolayerPhotoluminescenceExcitonFermi levelElectronPhysicsCoupling (piping)Spin (aerodynamics)Materials scienceChemistryNanotechnologyQuantum mechanicsOptoelectronics

Abstract

fetched live from OpenAlex

It is assessed in detail both experimentally and theoretically how the interlayer coupling of transition metal dichalcogenides controls the electronic properties of the respective devices. Gated transition metal dichalcogenide structures show electrons and holes to either localize in individual monolayers, or delocalize beyond multiple layers—depending on the balance between spin-orbit interaction and interlayer hopping. This balance depends on the layer thickness, momentum space symmetry points, and applied gate fields. The design range of this balance, the effective Fermi levels, and all relevant effective masses is analyzed in great detail. A good quantitative agreement of predictions and measurements of the quantum confined Stark effect in gated MoS2 systems unveils intralayer excitons as the major source for the observed photoluminescence.

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 score0.032
Threshold uncertainty score0.372

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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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