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Record W2283755291 · doi:10.1149/ma2014-01/42/1577

Recent Advances on Electrically Induced Light Emission from Carbon Nanotubes

2014· article· en· W2283755291 on OpenAlexaffabout
Richard Martel

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversité de MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsCarbon nanotubeElectroluminescenceMaterials scienceIncandescenceOptoelectronicsLight emissionNanotubeNanotechnologyChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

Light emission from individual (or small bundle) and network carbon transistors based on single-walled carbon nanotubes (SWNTs) and double-walled carbon nanotubes (DWNTs) is here reviewed in relation with the different mechanisms for light emission: electroluminescence [1,2,3] and incandescence or thermal radiation [4]. These studies guide our ongoing efforts towards making efficient light sources from nanotube emitters. The approach consists of operating the nanotubes transistors in different conditions of thermalisation and of exploring for each condition the different signatures in the emission, either thermal or electroluminescent, using an analysis of the resulting near-IR spectrum. The spectra were acquired in the energy range between 0.5 and 1.3 eV using the Spectrometer Infrared of Montreal (SIMON) mounted with a HgCdTe detector and on a probe station. Three different device configurations were measured. The first is a sub-monolayer network of nanotubes interconnected together to form a percolating semiconducting layer. [5] The numerous tube-tube junctions in the networks limit the current and prevent temperature raise by joule heating at higher bias. Electroluminescence from exciton recombination is found to dominate in all conditions, evidenced by well-defined emission peaks (~200meV in width) in the near-IR (Figure 1). The emission is ascribed to a radiative relaxation of the first excitonic transition. Even at very large biases, the main component of the emission from network transistors is electroluminescence, not incandescence. The second device geometry consists of a thick (~250 nm) and suspended carbon nanotube film having low resistance and reduced thermalization. This configuration favors joule heating when operating at large source-drain voltage and allows to significantly increase the nanotube temperature. The resulting incandescence was measured and used to calibrate SIMON for blackbody emission from a thermal source of pure nanotubes. A third set of devices was finally prepared with individual SWNT and DWNT transistors operating in vacuum, which conditions is used to prevent convection and for reducing the cooling capability of the substrate. The spectra were then compared with the different spectral signatures ascribed either to electroluminescence or thermal radiation depending on the biasing conditions. The experiments show a transition from electro-luminescence at low source-drain voltage to incandescence at higher voltage, with a regime of both in-between. These results will be discussed in terms of the dominant dissipation channels and carrier scatterings in nanotubes. Last, the best conditions to maximize light emission while avoiding heating and progress towards our effort to build electroluminescent transistors at 1,55 μm wavelength using sorted SWNTs will be discussed. Figure 1 Left: SEM and infrared micrographs of a large SWNT network transistor. Right: A carbon nanotube network transistor with the electroluminescence spectra at different biases. [Adapted from Ref. 5] REFERENCES [1] J. A. Misewich, R. Martel, Ph. Avouris, J. C. Tsang, S. Heinze, and J. Tersoff, Science, 300, 783 (2003). [2] J. Chen, V. Perebeinos, M. Freitag, J. Tsang, Q. Fu, J. Liu, and P. Avouris, Science, 310, 1171 (2005). [3] L. Marty, E. Adam, L. Albert, R. Doyon, D. Ménard and R. Martel, Phys. Rev. Lett. 96, 136803 (2006). [4] D. Mann, K. K., A. Kinkhabwala, E. Pop, J. Cao, X. Wang, L. Zhang, Q. Wang, J. Guo, and H. Dai, Nat. Nano, 2, 33 (2006). [5] E. Adam, C. M. Aguirre, L. Marty, B. C. St-Antoine, F. Meunier, P. Desjardins, D. Menard, and R. Martel, Nano Lett. 8, 2351 (2008). This work was done in collaboration with : Elyse Adam1, Pierre Lévesques2, Étienne Gaufrès2, Vincent Aymong2, David Ménard1 : RQMP and 1École Polytechnique de Montréal, Département de génie physique, Montréal, Québec H3C 3A7, Canada 2Université de Montréal, Département de chimie, Montréal, Québec H3T1J4, Canada .

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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".

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Citations0
Published2014
Admission routes2
Has abstractyes

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