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Record W2080138612 · doi:10.1103/physrevb.90.045313

Strong coupling in hyperbolic metamaterials

2014· article· en· W2080138612 on OpenAlexaff
Prashant Shekhar, Zubin Jacob

Bibliographic record

VenuePhysical Review B · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStrong Light-Matter Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMetamaterialPolaritonOptoelectronicsLaser linewidthPhysicsCoupling (piping)Condensed matter physicsMaterials scienceDielectricSemiconductorPhotonicsOpticsLaser

Abstract

fetched live from OpenAlex

Nanoscale light-matter interaction in the weak-coupling regime has been achieved with unique hyperbolic metamaterial modes possessing a high density of states. Here, we show strong coupling between intersubband transitions (ISBTs) of a multiple quantum well (MQW) slab and the bulk polariton modes of a hyperbolic metamaterial (HMM). These HMM modes have large wave vectors (high-$k$ modes) and are normally evanescent in conventional materials. We analyze a metal-dielectric practical multilayer HMM structure consisting of a highly doped semiconductor acting as a metallic layer and an active multiple quantum well dielectric slab. We observe delocalized metamaterial mode interaction with the active materials distributed throughout the structure. Strong coupling and characteristic anticrossing with a maximum Rabi splitting (RS) energy of up to 52 meV is predicted between the high-$k$ mode of the HMM and the ISBT, a value approximately 10.5 times greater than the ISBT linewidth and 4.5 times greater than the material loss of the structure. The scalability and tunability of the RS energy in an active semiconductor metamaterial device have potential applications in quantum well infrared photodetectors and intersubband light-emitting devices.

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)0.000
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
Metaresearch0.0000.000
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)0.0010.000

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 head0.021
GPT teacher head0.335
Teacher spread0.314 · 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

Citations53
Published2014
Admission routes1
Has abstractyes

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