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Record W2983920026 · doi:10.1039/c9nr07857f

Negative differential resistance observed on the charge density wave of a transition metal dichalcogenide

2019· article· en· W2983920026 on OpenAlexafffund
Adina Luican‐Mayer, Yuan Zhang, Andrew DiLullo, Yang Li, Brandon Fisher, Sergio E. Ulloa, Saw‐Wai Hla

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Ottawa
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of Canada
KeywordsCharge density waveCondensed matter physicsQuantum tunnellingScanning tunneling microscopeCharge densityElectronSuperconductivityDensity of statesMaterials sciencePhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Charge density waves and negative differential resistance are seemingly unconnected physical phenomena. The former is an ordered quantum fluid of electrons, intensely investigated for its relation with superconductivity, while the latter receives much attention for its potential applications in electronics. Here we show that these two phenomena can not only coexist but also that the localized electronic states of the charge density wave are essential to induce negative differential resistance in a transition metal dichalcogenide, 1T-TaS2. Using scanning tunneling microscopy and spectroscopy, we report the observation of negative differential resistance in the commensurate charge density wave state of 1T-TaS2. The observed phenomenon is explained by the interplay of interlayer and intra-layer tunneling with the participation of the atomically localized states of the charge density wave maxima and minima. We demonstrate that lattice defects can locally affect the coupling between the layers and are therefore a mechanism to realize NDR in these materials.

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 categoriesInsufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score0.999

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)0.0020.000

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 head0.030
GPT teacher head0.226
Teacher spread0.196 · 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

Citations21
Published2019
Admission routes2
Has abstractyes

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