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Record W2889754693 · doi:10.1021/acs.nanolett.8b03605

Tunneling into the Vortex State of NbSe<sub>2</sub> with van der Waals Junctions

2018· article· en· W2889754693 on OpenAlexfundno aff
Tom Dvir, Marco Aprili, C. H. L. Quay, Hadar Steinberg

Bibliographic record

VenueNano Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTopological Materials and Phenomena
Canadian institutionsnot available
FundersH2020 European Research CouncilMinistry of Science and Technology, IsraelAzrieli FoundationIsrael Science FoundationAgence Nationale de la Recherche
Keywordsvan der Waals forceCondensed matter physicsQuantum tunnellingVortexVan der Waals surfaceVan der Waals radiusState (computer science)Materials scienceScanning tunneling microscopeVan der Waals strainNanotechnologyPhysicsChemistryQuantum mechanicsThermodynamicsMoleculeComputer science

Abstract

fetched live from OpenAlex

We have performed device-based tunneling spectroscopy of NbSe2 in the vortex state with a magnetic field applied both parallel and perpendicular to the a–b plane. Our devices consist of layered semiconductors placed on top of exfoliated NbSe2 using the van der Waals transfer technique. At zero field, the spectrum exhibits a hard gap, and the quasiparticle peak is split into low- and high-energy features. The two features, associated with the effective two-band nature of superconductivity in NbSe2, exhibit markedly distinct responses to the application of magnetic field, suggesting an order-of-magnitude difference in the spatial extent of the vortex cores of the two bands. At energies below the superconducting gap, the hard gap gives way to vortex-bound Caroli–de Gennes–Matricon states, allowing the detection of individual vortices as they enter and exit the junction. Analysis of the subgap spectra upon application of parallel magnetic field allows us to track the process of vortex surface formation and spatial rearrangement in the bulk.

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 score0.042
Threshold uncertainty score0.341

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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