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Record W4405968070 · doi:10.1109/map.2024.3513158

From Waves to Watts: Advancements in rectenna arrays for radio-frequency energy harvesting and wireless power transfer.

2025· article· en· W4405968070 on OpenAlexaff
Partha Pratim Shome, Debanjali Sarkar, Taimoor Khan, Naoki Shinohara, Yahia M. M. Antar

Bibliographic record

VenueIEEE Antennas and Propagation Magazine · 2025
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsRectennaEnergy harvestingWireless power transferRadio frequencyElectrical engineeringWirelessEnergy transferMaximum power transfer theoremPower (physics)Energy (signal processing)Electronic engineeringEngineeringTelecommunicationsComputer sciencePhysicsEngineering physics

Abstract

fetched live from OpenAlex

The progression of radio-frequency energy harvesting (RFEH) and wireless power transfer (WPT) has prompted exceptional advancements in rectenna arrays, enabling the transformation of electromagnetic (EM) waves into usable power. This comprehensive review article explores the state-of-the-art developments in rectenna array technology, highlighting innovative approaches and breakthroughs that have improved the efficiency and applicability of rectenna arrays. The article begins with an explanation of the fundamental principles underlying rectenna array operation. Subsequently, the diverse design approaches that have been proposed to optimize rectenna arrays for RFEH and WPT applications are explored. These approaches encompass a spectrum of key aspects, including antenna design, rectifier topology, impedance matching techniques, and materials selection. This review article aspires to provide readers with an understanding of the dynamic landscape of RFEH and WPT through rectenna array 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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.225
Teacher spread0.216 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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