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Record W2596418769 · doi:10.1063/1.4978321

A metasurface for conversion of electromagnetic radiation to DC

2017· article· en· W2596418769 on OpenAlexafffund
Mohamed El Badawe, Thamer S. Almoneef, Omar M. Ramahi

Bibliographic record

VenueAIP Advances · 2017
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResonatorRectennaRectifier (neural networks)DiodeResistive touchscreenRadiationEnergy conversion efficiencyPower (physics)OptoelectronicsPhysicsPower densityElectromagnetic radiationEnergy harvestingElectrical engineeringOpticsMaterials scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

We present a metasurface electromagnetic energy harvester based on electrically small resonators. An array of 8× 8 cross resonators was designed to operate at 3GHz. Unlike earlier designs of metasurface harvesters where each resonator was connected to a single rectifier or load, in this work the received power by all resonators is channeled to a single rectifier which in turn channels the DC energy to a single 50Ω resistive load. The critical advantage of the proposed structure is maximizing power density per diode which maximizes the diode turn-on time. We show through simulation and measurements that the proposed metasurface harvester provides Radiation to DC conversion efficiency of more than 40%.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.246
Teacher spread0.237 · 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 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

Citations93
Published2017
Admission routes2
Has abstractyes

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