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Record W2078252069 · doi:10.1039/c4fd00181h

Ultrafast dynamics of nanoplasmonic stopped-light lasing

2014· article· en· W2078252069 on OpenAlexaff
Sebastian Wuestner, T. E. Pickering, Joachim M. Hamm, A. Freddie Page, Andreas Pusch, Ortwin Hess

Bibliographic record

VenueFaraday Discussions · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsKensington Health
FundersEngineering and Physical Sciences Research CouncilLeverhulme Trust
KeywordsLasing thresholdPlasmonPolaritonSurface plasmon polaritonSurface plasmonUltrashort pulseOptoelectronicsPhysicsMaterials scienceOpticsLaser

Abstract

fetched live from OpenAlex

We study the spatio-temporal dynamics of coherent amplification and lasing in planar gain-enhanced nanoplasmonic structures and show that a singularity in the density of optical states leads to a stopped-light feedback mechanism that allows for cavity-free photonic and surface-plasmon polariton nanolasing. We reveal that in the absence of cavity-induced feedback a phase-locked superposition of a quasi dispersion-free waveguide mode promotes the dynamic formation of a subwavelength lasing mode. Simulations on the basis of a full-time domain Maxwell-Bloch Langevin approach uncover a high spontaneous emission factor β≈0.9 and demonstrate that the stopped-light lasing/spasing mechanism is remarkably robust against interface roughness. Stopped-light surface-plasmon polariton lasing is shown to be stable for gain sections of a width of down to 200 nm but in wider gain structures of the order of 1 μm the dynamics is characterised by spatio-temporally oscillating lasing surface-plasmon polaritons with typical temporal and spatial periods of smaller than 5 fs and smaller than 100 nm. Stopped-light lasing thus provides opportunities for ultrafast nanolasing and the realization of ultra-thin lasing surfaces and offers a new route to ultrafast spasing and cavity-free active quantum plasmonics.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.603

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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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 designSimulation or modeling
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

Citations9
Published2014
Admission routes1
Has abstractyes

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