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Record W1982814984 · doi:10.1063/1.4896668

High-efficiency passive full wave rectification for electromagnetic harvesters

2014· article· en· W1982814984 on OpenAlexaff
Mehmet Yılmaz, Bassam A. Tunkar, Sangtak Park, Karim El-Rayes, M. A. Mahmoud, Eihab Abdel‐Rahman, Mustafa Yavuz

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrical engineeringRectifier (neural networks)MOSFETSchottky diodeDiodeRectificationEnergy harvestingVoltageCapacitorRectennaEnergy conversion efficiencyPower semiconductor deviceEngineeringElectronic engineeringPower (physics)Computer sciencePhysicsTransistor

Abstract

fetched live from OpenAlex

We compare the performance of four types of full-wave bridge rectifiers designed for electromagnetic energy harvesters based on silicon diodes, Schottky diodes, passive MOSFETs, and active MOSFETs. Simulation and experimental results show that MOSFET-type rectifiers are more efficient than diode-type rectifiers, reaching voltage and power efficiency of 99% for ideal voltage source with input amplitudes larger than 800 mV. Since active MOSFETs require extra components and an external DC power supply, we conclude that passive MOSFETs are superior for micro-power energy harvesting systems. We demonstrate passive MOSFET rectifiers implemented using discrete, off-shelf components and show that they outperform all electromagnetic harvester rectifiers hitherto reported obtaining a power efficiency of 95%. Furthermore, we show that passive MOSFET rectifiers do not affect the center frequency, harvesting bandwidth, or optimal resistance of electromagnetic harvesters. We demonstrate a complete power management module by adding a capacitor to the rectifier output terminal. We found that this configuration changed the optimal resistive load from 40 Ω to 55 Ω and decreased output power efficiency to 86%.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.512

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 head0.012
GPT teacher head0.202
Teacher spread0.190 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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