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Record W2048453570 · doi:10.1109/jphot.2012.2202282

Carbon Nanotube-Based Photoconductive Switches for THz Detection: An Assessment of Capabilities and Limitations

2012· article· en· W2048453570 on OpenAlexaff
Barmak Heshmat, Hamid Pahlevaninezhad, T.E. Darcie

Bibliographic record

VenueIEEE photonics journal · 2012
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTerahertz radiationPhotoconductivityCarbon nanotubeMaterials scienceOptoelectronicsFabricationContext (archaeology)Electron mobilityDetectorNanotechnologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Carbon nanotubes possess appealing properties for terahertz (THz) applications. This work investigates the contribution of these properties in the context of THz photoconductive (PC) switches as THz detectors. The analysis engages the received THz electric field, the optical excitation, and the photocarrier dynamics of the carbon nanotube material through Drude-Smith theory and equivalent circuit model. Through this analysis, the effect of each parameter in the detected current can be investigated. Based on a realistic numerical assessment and comparison with our measurements for a conventional LT-GaAs PC switch, it is found that improvement in detected current is theoretically achievable, depending on the relative value of the imaginary photoconductivity of the CNT film. This is a parameter that can be varied through chemical treatment of the film. We found that, unlike the case of PC switches as THz emitting devices where a higher mobility is desired for higher output THz power, the detected current in the THz receiving PC switch is a nonmonotonic function of the mobility in the single-wall carbon nanotubes (SWNT) film. The capabilities and limitations revealed in this study set guidelines for fabrication and optimization of more efficient carbon nanotube-based THz receiving PC switches. The study also addresses the fabrication process and challenges.

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 score0.084
Threshold uncertainty score0.383

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 head0.036
GPT teacher head0.300
Teacher spread0.263 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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