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Record W2170255884 · doi:10.1109/icmens.2005.60

Injecting and controlling spin populations and currents in semiconductors using optically induced quantum interference effects

2006· article· en· W2170255884 on OpenAlexaff
H. M. van Driel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum and electron transport phenomena
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsExcitationFemtosecondSemiconductorPhysicsSpin (aerodynamics)LaserOptoelectronicsPolarization (electrochemistry)Condensed matter physicsElectronSpin engineeringCircular polarizationSpin polarizationAtomic physicsOpticsChemistryMagnetic fieldQuantum mechanics

Abstract

fetched live from OpenAlex

It is widely known that interband optical excitation of semiconductors such as GaAs using circularly polarized light can induced a net electron spin polarization because of the spin-orbit interaction which modifies the valence band states. In the last few years we have undertaken a series of theoretical and experimental investigations to explore how all-optical processes can be used to inject and control not only pure spin populations but also pure spin currents and pure charge currents or combinations of these. The various excitation schemes can be understood in terms of quantum interference of absorption pathways or, on a macroscopic level, nonlinear optical processes. The controlled currents and spin populations are typically generated by femtosecond laser pulses and detected via pump-probe techniques based on induced transmission changes or emitted THz radiation.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.024
GPT teacher head0.281
Teacher spread0.256 · 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
Published2006
Admission routes1
Has abstractyes

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