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Record W3194669066 · doi:10.1103/physrevb.105.085404

Pump-induced terahertz anisotropy in bilayer graphene

2022· preprint· en· W3194669066 on OpenAlexafffund
Angelika Seidl, Roozbeh Anvari, Marc M. Dignam, Péter Richter, Thomas Seyller, H. Schneider, M. Helm, Stephan Winnerl

Bibliographic record

VenuePhysical review. B./Physical review. B · 2022
Typepreprint
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsBilayer grapheneTerahertz radiationGrapheneCondensed matter physicsPolarization (electrochemistry)AnisotropyElectronPhysicsBilayerMaterials scienceMolecular physicsOpticsChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

We investigate the intraband nonlinear dynamics in doped bilayer graphene in the presence of strong, linearly polarized, in-plane terahertz fields. We perform degenerate pump-probe experiments with 3.4-THz fields on doped bilayer graphene at low temperature ($12\phantom{\rule{0.16em}{0ex}}\mathrm{K}$) and find that when the pump is copolarized with the probe beam, the differential pump-probe signal is almost double that found in the cross-polarized case. We show that the origin of this pump-induced anisotropy is the difference in the average electron effective mass in the probe direction when carriers are displaced in $k$ space by the pump either parallel or perpendicular to the direction of the probe polarization. We model the system using both a simple semiclassical model and a Boltzmann equation simulation of the electron dynamics with phenomenological scattering and find good qualitative agreement with experimental results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 head0.021
GPT teacher head0.355
Teacher spread0.334 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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