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Record W3110697139 · doi:10.1063/5.0031466

Double-walled carbon nanotube film as the active electrode in an electro-optical modulator for the mid-infrared and terahertz regions

2020· article· en· W3110697139 on OpenAlexafffund
Philippe Gagnon, François Lapointe, P. Desjardins, Richard Martel

Bibliographic record

VenueJournal of Applied Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversité de MontréalNational Research Council CanadaPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsTerahertz radiationMaterials scienceOptoelectronicsCarbon nanotubeInfraredPrismGrapheneModulation (music)AttenuationFano resonanceWaveguideOpticsNanotechnologyPlasmonPhysics

Abstract

fetched live from OpenAlex

The lack of efficient optical components operating with terahertz (THz) radiation is a limiting step in the ongoing large-scale development of this technology in fields such as telecommunication and imaging. In this work, we propose the use of double-walled carbon nanotube (DWCNT) films as the active electrode in THz modulation devices. Using six bounces in an internal total reflection configuration in a silicon waveguide prism, we achieved high attenuation from a 5 nm thin film, reaching up to −ΔT/T=6% at 50 THz, albeit with a slow speed of modulation on the order of minutes. Moreover, this attenuation −ΔT/T attains a value of 20% at 30 THz using a thicker 20 nm DWCNT film. As a consequence of doping, the modulation of a phonon-related Fano resonance is also observed in the mid-infrared, which could be used as a modulable narrow-band optoelectronic filter. Our study provides a sense of the capabilities unlocked by exploiting the optical and electronic properties of carbon nanotubes in the terahertz and infrared regimes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.364

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 integrity0.0000.001
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 head0.013
GPT teacher head0.229
Teacher spread0.217 · 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 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

Citations10
Published2020
Admission routes2
Has abstractyes

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