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Record W1970620869 · doi:10.1103/physrevb.82.155130

Acousto-optics studied in polaritonic photonic crystals

2010· article· en· W1970620869 on OpenAlexafffund
Mahi R. Singh, Chris Racknor

Bibliographic record

VenuePhysical Review B · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotonic crystalSpontaneous emissionQuantum dotPhotonicsPhotonOptoelectronicsCondensed matter physicsBand gapPhysicsMaterials scienceQuantum opticsOptics

Abstract

fetched live from OpenAlex

We have studied the acousto-optic effect on the photon transmission and the spontaneous emission in polaritonic photonic crystal. We have considered that photonic crystals are fabricated from polaritonic materials such as GaP, MgO, ${\text{LiNbO}}_{3}$, and ${\text{LiTaO}}_{3}$. A two-level quantum dot is doped in a polaritonic crystal to study the decay rate of the spontaneous emission. The decay rate of quantum dots, band structure, and photon transmission coefficient have been calculated. It is found that band-gap width and the decay rate of quantum dots depends strongly on the high-frequency dielectric constant of the polaritonic crystals while the photonic band edges vary inversely by the ratio of longitudinal- to transverse-optical phonon energies. The spontaneous decay rate of the quantum dot can be controlled by the external strain field. This finding is significant because it is well known that the spontaneous emission is source of undesirable noise in different types of electronic and optical devices. Finally, we have also found the system can be switched from transmitting state to reflecting state by applying an external strain field. These are distinct and interesting results and can be used to fabricate new types of photonic couplers and fibers which in turn can be used to fabricate all photonic switches.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.661

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 head0.011
GPT teacher head0.316
Teacher spread0.306 · 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 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

Citations8
Published2010
Admission routes2
Has abstractyes

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