MétaCan
Menu
Back to cohort
Record W4398250782 · doi:10.1002/andp.202400010

Transmissions in Gapped Graphene Exposed to Tilting and Oscillating Barriers

2024· article· en· W4398250782 on OpenAlexaff
Miloud Mekkaoui, Ahmed Jellal, Abderrahim El Mouhafid

Bibliographic record

VenueAnnalen der Physik · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsCanadian Quantum Research Center
Fundersnot available
KeywordsGraphenePhysicsCondensed matter physicsStatistical physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract The transmissions of fermions are investigated through gapped graphene structures by employing a combination of double barrier tilting and a time‐oscillating potential. The latter introduces additional sidebands into the transmission probability that manifest at energy levels determined by the frequency and incident energy. These sidebands arise from the absorption or emission of photons generated by the oscillating potential. It is demonstrated that the tilting and positioning of the scattering events within the barriers play a crucial role in determining the peak of tunneling resistance. In particular, the presence of a mid‐barrier‐embedded scatter leads to a transition from a peak to a cusp when the incident energy reaches the Dirac point within a barrier. Additionally, it is that introducing a time‐varying potential results in transmissions dispersing across both the central band and the sidebands.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.304
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAnnalen der PhysikSame topicGraphene research and applicationsFrench-language works237,207