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Record W4225349174 · doi:10.1109/tmtt.2022.3156936

Compact Patch Rectennas Without Impedance Matching Network for Wireless Power Transmission

2022· article· en· W4225349174 on OpenAlexaff
Changjun Liu, Hang Lin, Zhongqi He, Zhizhang Chen

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2022
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsRectennaImpedance matchingElectrical engineeringAntenna (radio)Schottky diodeRadio frequencyElectrical impedanceGround planeMicrowavePower transmissionMicrowave transmissionPatch antennaElectronic engineeringPower (physics)EngineeringDiodeTelecommunicationsPhysicsRectificationVoltage

Abstract

fetched live from OpenAlex

In microwave wireless power receivers, impedance matching networks can maximize power transmission from RF to dc. However, they require circuit components and physical space to implement. To circumvent the problem, we present two compact patch rectennas that do not require matching networks. They comprise a patch antenna and rectifying Schottky diodes which are mounted on the ground plane and connected to the patch antenna through metallic vias. The positions of the vias are chosen in such a way that the input impedance of the antenna and that of the rectifying units are conjugately matched. The two rectenna types are all fabricated and tested: one with one rectifying unit and the other with double rectifying units, respectively. Measurements show that the rectenna with the single rectifying unit has the peak RF-dc conversion efficiency of 77% at 2.45 GHz and the dynamic power range (>60%) is 10 dB. The rectenna with the double rectifying units has the peak conversion efficiency of 74.14% at 2.45 GHz and the dynamic power range (>60%) is 10 dB. Both rectennas are good candidates for integrated microwave wireless power receivers due to the removal of the impedance matching networks and the suppression of harmonic components.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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.

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

Citations63
Published2022
Admission routes1
Has abstractyes

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