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Record W4389902325 · doi:10.13052/2023.aces.j.380707

Nanoarray of Vivaldi Rectenna for Infrared-energy Harvesting

2023· article· en· W4389902325 on OpenAlexaff
Wided Amara, Abdelaziz Alzahrani, Ameni Mersani, Donia Oueslati, Ahmed Y. Elsharabasy, Bandar Hakim, Hatem Rmili, Atef Z. Elsherbeni

Bibliographic record

VenueThe Applied Computational Electromagnetics Society Journal (ACES) · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsMcMaster University
FundersKing Abdulaziz University
KeywordsRectennaTerahertz radiationOptoelectronicsMaterials scienceDiodeInfraredEnergy harvestingRectificationElectrical engineeringOpticsEnergy (signal processing)VoltageEngineeringPhysics

Abstract

fetched live from OpenAlex

This article presents the design of an array of rectennas operating at 28.3 THz for infrared (IR) energy harvesting applications. The basic element of the array consists of a Vivaldi-dipole rectenna composed of two arms made with different conductors (gold and titanium). A metal-insulator-metal (MIM) tunnel diode is used to rectify the THz ac current. The proposed MIM diode consists of a very thin layer of Al2O3 sandwiched between the two metal electrodes. Arrays of two, three, and four rectennas are investigated. The improvement of the energy captured by coupling several elements in the same structure with a common gap is also investigated. This array architecture, without feeding network, may reduce the number of rectifying diodes and, therefore, decrease losses and increase the overall efficiency. Finally, it has been found that the four-elements rectenna array has a maximum electric field intensity of 62.4 × 104 V/m at 28.3 THz.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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