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Record W2034213579 · doi:10.1063/1.4916232

Metamaterial electromagnetic energy harvester with near unity efficiency

2015· article· en· W2034213579 on OpenAlexaff
Thamer S. Almoneef, Omar M. Ramahi

Bibliographic record

VenueApplied Physics Letters · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMetamaterialMetamaterial absorberResistive touchscreenEnergy harvestingMaterials scienceResistorAbsorption (acoustics)OptoelectronicsElectrical impedanceElectromagnetic radiationEnergy conversion efficiencyPower (physics)Electrical engineeringOpticsPhysicsComposite materialEngineeringTunable metamaterialsVoltage

Abstract

fetched live from OpenAlex

We present the design of a metamaterial medium for electromagnetic energy harvesting based on the full absorption concept. A metamaterial slab was designed comprising 13 × 13 electrically small cells, each loaded with an 82 Ω resistor which mimics the input impedance of a rectification circuitry. Unlike earlier designs of metamaterial absorbers, here the power absorption is mostly dissipated across a resistive load instead of the dielectric substrate. This implies that effective electromagnetic energy harvesting can be achieved. The power is channeled through a via connected to each cell. For a design optimized at 3 GHz, simulation and experimental results show power absorption efficiency of 97% and 93%, respectively.

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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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

Citations253
Published2015
Admission routes1
Has abstractyes

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