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Record W2586726406 · doi:10.1109/jstqe.2017.2662658

Influence of Gap Size on Intense THz Generation From ZnSe Interdigitated Large Aperture Photoconductive Antennas (October 2016)

2017· article· en· W2586726406 on OpenAlexafffund
X. Ropagnol, X. Chai, S. Mohsen Raeis-Zadeh, Safieddin Safavi‐Naeini, Marie Kirouac-Turmel, M. Bouvier, C. Y. Côté, Matt Reid, Marc A. Gauthier, T. Ozaki

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2017
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Northern British ColumbiaAxis Photonique (Canada)University of WaterlooInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTerahertz radiationMaterials scienceCapacitanceOptoelectronicsOpticsElectric fieldPhotoconductivityEffective radiated powerAperture (computer memory)Space chargeRadiationPhysicsElectrode

Abstract

fetched live from OpenAlex

We report on the effect of gap size on the generation of intense THz pulses emitted from ZnSe interdigitated large-aperture photoconductive antennas. The use of a relatively large gap size (460, 700, and 932 μ m) was found to slightly influence the radiated THz waveforms and the spectrum but strongly affects the scaling of the radiated THz electric field where stronger saturation appeared for smaller gap sizes, contrary to the behavior expected for operation in the THz field-screening regime. We attributed these variations to the effect of the relatively large capacitance of the antennas, which tends to limit the maximum radiated THz power, and which can be linked to space charge screening. The response of the high-voltage pulse was simulated as a function of the gap size and capacitance, revealing that these variations occur on a time scale faster than the THz pulse duration, thereby impacting the radiation emission characteristics.

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)0.001
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 score0.181
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.263
Teacher spread0.247 · 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

Citations7
Published2017
Admission routes2
Has abstractyes

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