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Optimal Load Capacitance for Triboelectric Energy Harvesters to Maximize Transient Power

2025· article· W4416727812 on OpenAlexaff
Maryam Hosseini, Mahmood Chahari, Milutin Stanaćević, Shahrzad Towfighian, Ryan Willing, Emre Salman

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsCapacitanceRectifier (neural networks)Transient (computer programming)Maximum power principlePower (physics)Triboelectric effectControl theory (sociology)Transient response

Abstract

fetched live from OpenAlex

This paper describes an analytic method to determine the optimal load capacitance of a full wave rectifier (FWR) in triboelectric energy harvesters. The amount of average power delivered from the harvester to the rectifier grows in each cycle, eventually reaching a peak value. In steady state, the average rectifier power depends on the harvester characteristics and the load capacitance of the rectifier. For a given harvester, if the load capacitance is too small, the rectifier power does not reach the maximum power in steady state. Alternatively, if the load capacitance is too large, the number of cycles to reach maximum power increases, causing additional delay. An analytic method is proposed to estimate the optimum value of this capacitance, which ensures maximum power while minimizing the transient time. The results are validated with the measurement results of a triboelectric harvester.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.762
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.231
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
GenreMethods

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

Citations1
Published2025
Admission routes1
Has abstractyes

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