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Magnetic field driven interminiband charge transfer in InGaAs/InP superlattices

2015· article· en· W1602020311 on OpenAlexafffund
Yu. A. Pusep, Bruno G. Tavares, L. Fernandes dos Santos, Ray LaPierre

Bibliographic record

VenueJournal of Physics Condensed Matter · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaConselho Nacional de Desenvolvimento Científico e TecnológicoCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSuperlatticeCondensed matter physicsElectronPolarization (electrochemistry)Magnetic fieldQuantum Hall effectPhotoluminescenceMaterials sciencePhysicsChemistryOptoelectronicsQuantum mechanics

Abstract

fetched live from OpenAlex

The characteristic energies, occupancies and polarizations of the minibands formed by the Γ-Γ and Γ-Xz interlayer electon tunnelings in the InGaAs/InP superlattices are studied in the regime of the integer quantum Hall effect by polarization resolved photoluminescence. Accordingly, the magnetic field induced shrinkage of the interminiband gap, predicted by the theory, and as a consequence, the redistribution of charge over the superlattice minibands and the depolarization of the quantum Hall electron states are observed at odd filling factors. The response of the electrons residing in the InGaAs/InP superlattice minibands to the magnetic field is found very similar to the corresponding response of the electrons confined in the symmetric and anti-symmetric two-dimensional minibands of GaAs/AlGaAs double quantum wells. The presented results are evidence of the formation of the correlated states in multi-component electron systems formed in semiconductor multiple layers at odd filling factors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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 designObservational
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

Citations3
Published2015
Admission routes2
Has abstractyes

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