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Record W2016320596 · doi:10.1149/06105.0089ecst

(Invited) Carbon Nanotube Based Photonics

2014· article· en· W2016320596 on OpenAlexaff
Adrien Noury, Xavier Le Roux, Étienne Gaufrès, Laurent Vivien, Nicolas Izard

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversité de Montréal
FundersMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheAgence Nationale de la Recherche
KeywordsCarbon nanotubeMaterials scienceSiliconPhotoluminescencePhotonicsSilicon photonicsResonatorWaveguideOptoelectronicsPolymerNanotechnologyWavelengthLight emissionComposite material

Abstract

fetched live from OpenAlex

Semiconducting carbon nanotubes (s-SWNT) are efficiently extracted from a raw nanotube powder using an ultracentrifugation method with a conjugated polymer as extracting agent. This method leads to obtention of metallic-free s-SWNT samples, displaying strong photoluminescence properties. An integration scheme to couple s-SWNT optical properties with silicon waveguide using processes compatible with silicon technology is proposed, and we demonstrated the emission of s-SWNT throught the silicon waveguide at a wavelength of 1.3 µm. This integration scheme is used to couple s-SWNT with silicon microring resonators. Sharp emission peaks originating from light coupled and resonning inside the microring are observed, with quality factors around 3000.

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: none
Teacher disagreement score0.649
Threshold uncertainty score0.451

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 head0.006
GPT teacher head0.188
Teacher spread0.182 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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