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Record W4205858342 · doi:10.1109/ojnano.2021.3135478

Carbon-Based THz Microstrip Antenna Design: A Review

2021· review· en· W4205858342 on OpenAlexafffund
Guanxuan Lu, Jiaqi Wang, Zhemiao Xie, John T. W. Yeow

Bibliographic record

VenueIEEE Open Journal of Nanotechnology · 2021
Typereview
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTerahertz radiationMicrostripMicrostrip antennaAntenna (radio)Omnidirectional antennaMaterials sciencePatch antennaOptoelectronicsComputer scienceElectronic engineeringOpticsTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

Increasing demands for high-speed wireless communication have stimulated the development of novel optoelectrical devices. Typically, terahertz (THz) wave, is much advantageous because of its relatively high-resolution transportation and strong penetrability property. One of the electromagnetic devices, the antenna, plays a key role in future THz devices. However, there are few review publishments related to carbon-based THz microstrip antenna designs. In this article, we list the basic figure of merits for evaluating antennas. We also show the developing microstrip antenna structures. Importantly, we summarize the current progress of THz microstrip antennas using different dimensional carbon materials, such as carbon nanotubes, graphene, and carbon foams. This review will lay a solid foundation for carbon-based THz microstrip antenna design, and furthermore provide novel sights for other THz antenna designs.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.070
GPT teacher head0.340
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2021
Admission routes2
Has abstractyes

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