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Record W1971293863 · doi:10.1103/physrevb.63.045316

Coherent control of the nonradiative decay of excitons in asymmetric quantum well structures

2001· article· en· W1971293863 on OpenAlexafffund
Seyed M. Sadeghi, H. M. van Driel

Bibliographic record

VenuePhysical review. B, Condensed matter · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicQuantum optics and atomic interactions
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsExcitonAtomic physicsBiexcitonCoupling (piping)PhotonPopulationCondensed matter physicsQuantum mechanicsMaterials science

Abstract

fetched live from OpenAlex

We theoretically propose coherent control of the nonradiative decay of excitons in infrared-coupled asymmetric quantum wells. This is done by (i) inducing intraband multiphoton quantum coherences between exciton states associated with three conduction subbands (E1, E2, and E3), and (ii) adjusting the contributions of these coherences using competition between one- and two-photon coupling of these states. The intraband excitonic transitions are caused by one or two infrared laser beams resonant with the intersubband transitions between E1, E2, and E3. We show that by tuning these beams to allow coupling of the E1--HH1, E2--HH1, and E3--HH1 excitons with different configurations one can coherently manipulate the quantum efficiency of exciton emission. In an asymmetric double ${\mathrm{G}\mathrm{a}\mathrm{A}\mathrm{s}/\mathrm{A}\mathrm{l}}_{x}{\mathrm{Ga}}_{1\ensuremath{-}x}\mathrm{As}$ quantum well this could lead to nearly complete destruction of the emission spectrum, or to the immunity of excitons from enhancement of their nonradiative decay rates via the intraband transitions. We discuss these effects in terms of coherent population trapping of excitons and show how field-coherent destruction effects influence the multiphoton excitonic coherences.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.009
GPT teacher head0.295
Teacher spread0.286 · 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 designTheoretical or conceptual
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

Citations4
Published2001
Admission routes2
Has abstractyes

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