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Record W2963473481 · doi:10.1103/physrevb.81.155325

Spin-dependent transport in waveguides with spatially modulated strengths of the Rashba and Dresselhaus terms of the spin-orbit interaction

2010· article· en· W2963473481 on OpenAlexafffund
P. M. Krstajić, E. Rezasoltani, P. Vasilopoulos

Bibliographic record

VenuePhysical Review B · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsCondensed matter physicsSilicon on insulatorSpin (aerodynamics)Spin–orbit interactionElectronMixing (physics)Quantum mechanicsSilicon

Abstract

fetched live from OpenAlex

We study electron transport through waveguides (WGs) in which the strengths of the Rashba $(\ensuremath{\alpha})$ and Dresselhaus $(\ensuremath{\beta})$ terms of the spin-orbit interaction (SOI) vary in space. Subband mixing, due to lateral confinement, is taken into account only between the two first subbands. For sufficiently narrow WGs the transmission $T$ exhibits a squarelike shape as a function of $\ensuremath{\alpha}$ or $\ensuremath{\beta}$. Particular attention is paid to the case of equal SOI strengths, $\ensuremath{\alpha}=\ensuremath{\beta}$, for which spin-flip processes are expected to decrease. The transmission exhibits resonances as a function of the length of a SOI-free region separating two regions with SOI present, that are most pronounced for $\ensuremath{\alpha}=\ensuremath{\beta}$. The sign of $\ensuremath{\alpha}$ strongly affects the spin-up and spin-down transmissions. The results show that the main effect of subband mixing is to shift the transmission resonances and to decrease the transmission from one spin state to another. The effect of possible band offsets between regions that have different SOI strengths and effective masses is also discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.319

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 head0.006
GPT teacher head0.262
Teacher spread0.257 · 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 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

Citations11
Published2010
Admission routes2
Has abstractyes

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