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Record W4403215296 · doi:10.1021/acsphotonics.4c01231

Broadband Terahertz Hybrid Twisted Horn Antenna for the Electric Field Polarization Manipulation of Spintronic Terahertz Radiation Emitters

2024· article· en· W4403215296 on OpenAlexafffund
Basem Y. Shahriar, A. Y. Elezzabi

Bibliographic record

VenueACS Photonics · 2024
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsTerahertz radiationTerahertz gapBroadbandOptoelectronicsElectric fieldPolarization (electrochemistry)PhotomixingOpticsHorn antennaSpintronicsMaterials scienceTerahertz spectroscopy and technologyTerahertz metamaterialsAntenna (radio)PhysicsRadiation patternSlot antennaFar-infrared laserTelecommunicationsLaserEngineering

Abstract

fetched live from OpenAlex

With wireless communication frequencies steadily approaching the terahertz (THz) band, research into devices for on-chip wireless THz communication systems is at its zenith. In this work, we utilize two-photon lithography techniques to fabricate a continuous twisted waveguide exhibiting at least 90% electric field polarization rotation efficiency from 1.0 to 3.0 THz, as well as a hybrid twisted pyramidal horn antenna capable of both polarization rotation and beaming, with a 10 dB bandwidth of 1.7 THz (from 0.3 to 2.0 THz), providing a maximum gain of 12 dB at 1.1 THz, and having a wideband operating region with 90–100% polarization rotation efficiency from 0.3 to 1.75 THz. Due to their versatility and their innate ability to perform multiple functions such as guiding and beaming THz radiation from spintronic THz radiation emitters while manipulating their electric field polarization, we envision such structures to be used for on-chip applications similar to their microwave counterparts, eliminating the need for multiple elements such as antennas, waveguides, and polarizers on dense chips where device footprint comes at a premium.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.488

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 head0.007
GPT teacher head0.224
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 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

Citations4
Published2024
Admission routes2
Has abstractyes

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