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Record W2789379651 · doi:10.1039/c7nr09183d

Single-mode surface plasmon distributed feedback lasers

2018· article· en· W2789379651 on OpenAlexaff
Elham Karami Keshmarzi, R. Niall Tait, Pierre Berini

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsMaterials scienceLasing thresholdLaser linewidthLaserSurface plasmonOptoelectronicsPlasmonGratingOpticsSurface plasmon polaritonPhysicsWavelength

Abstract

fetched live from OpenAlex

Single-mode surface plasmon distributed feedback (DFB) lasers are realized in the near infrared using a two-dimensional non-uniform long-range surface plasmon polariton structure. The surface plasmon mode is excited onto a 20 nm-thick, 1 μm-wide metal stripe (Ag or Au) on a silica substrate, where the stripe is stepped in width periodically, forming a 1st order Bragg grating. Optical gain is provided by optically pumping a 450 nm-thick IR-140 doped PMMA layer as the top cladding, which covers the entire length of the Bragg grating, thus creating a DFB laser. Single-mode lasing peaks of very narrow linewidth were observed for Ag and Au DFBs near 882 nm at room temperature. The narrow linewidths are explained by the low spontaneous emission rate into the surface plasmon lasing mode as well as the high quality factor of the DFB structure. The lasing emission is exclusively TM polarized. Kinks in light-light curves accompanied by spectrum narrowing were observed, from which threshold pump power densities can be clearly identified (0.78 MW cm-2 and 1.04 MW cm-2 for Ag and Au DFB lasers, respectively). The Schawlow-Townes linewidth for our Ag and Au DFB lasers is estimated and very narrow linewidths are predicted for the lasers. The lasers are suitable as inexpensive, recyclable and highly coherent sources of surface plasmons, or for integration with other surface plasmon elements of similar structure.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.999

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)0.0000.002

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 head0.017
GPT teacher head0.249
Teacher spread0.231 · 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 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

Citations45
Published2018
Admission routes1
Has abstractyes

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