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Evaluation of Discrepancies in Theoretical and Experimental RF Energy Harvesting Efficiency From Measured Diode Parameters

2024· article· W7131175377 on OpenAlexaff
Xiaoqiang Gu, Maninder Bir Gulshan, Thomas Micallef, Roni Khazaka, Keyu Wu

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsPolytechnique MontréalMcGill University
Fundersnot available
KeywordsSchottky diodeDiodeSpiceEnergy harvestingReverse leakage currentEquivalent circuitLeakage (economics)Radio frequency

Abstract

fetched live from OpenAlex

Discrepancies in modeled and measured RF energy harvesting efficiency based on Schottky diodes are common. A number of factors contribute to this discrepancy, the diode model used in the simulation being one of them. This work starts with measurements of diode dc I–V curves, which reveal that diode reverse leakage current is often underestimated on the datasheet. Theoretical analysis shows that the underestimated reverse leakage current of diodes would result in higher RF energy harvesting efficiency. This explains that theoretical analysis based on the traditional SPICE diode model commonly has overestimated harvesting efficiency performance. Using the fitted model based on measured I–V curves, the circuit analysis shows lower harvesting efficiency, closer to the results obtained from experiments.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.260
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

Citations0
Published2024
Admission routes1
Has abstractyes

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