MétaCan
Menu
Back to cohort
Record W3111083811 · doi:10.3390/s20247023

Numerical Analysis of MIM-Based Log-Spiral Rectennas for Efficient Infrared Energy Harvesting

2020· article· en· W3111083811 on OpenAlexaff
Ali Yahyaoui, Ahmed Y. Elsharabasy, Jawad Yousaf, Hatem Rmili

Bibliographic record

VenueSensors · 2020
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsMcMaster University
FundersKing Abdulaziz University
KeywordsRectennaSpiral antennaInsulator (electricity)OptoelectronicsEnergy harvestingMaterials scienceRectificationResponsivityElectrodeFeed hornAntenna (radio)Electrical engineeringRadiation patternPhysicsEnergy (signal processing)EngineeringVoltageCoaxial antenna

Abstract

fetched live from OpenAlex

This work presents the design and analysis of a metal-insulator-metal (MIM)-based optical log spiral rectenna for efficient energy harvesting at 28.3 THz. To maximize the benefits of the enhanced field of the proposed nano-antenna in the rectification process, the proposed design considers the antenna arms (Au) as the electrodes of the rectifying diode and the insulator is placed between the electrode terminals for the compact design of the horizontal MIM rectenna. The rectifier insulator, Al2O3, was inserted at the hotspot located in the gap between the antennas. A detailed analysis of the effect of different symmetric and asymmetric MIM-configurations (Au-Al2O3-Ag, Au-Al2O3-Al, Au-Al2O3-Cr, Au-Al2O3-Cu, and Au-Al2O3-Ti) was conducted. The results of the study suggested that the asymmetric configuration of Au-Al2O3-Ag provides optimal results. The proposed design benefits from the captured E-field intensity, I-V, resistivity, and responsivity and results in a rectenna that performs efficiently.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSensorsSame topicEnergy Harvesting in Wireless NetworksFrench-language works237,207