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Power Optimization of Triboelectric Energy Harvesters Based on Rectifier Turn-on Time

2025· article· W4416726031 on OpenAlexaff
Maryam Hosseini, Milutin Stanaćević, Shahrzad Towfighian, Ryan Willing, Emre Salman

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsWestern University
Fundersnot available
KeywordsRectifier (neural networks)Triboelectric effectPower (physics)VoltageMaximum power transfer theoremMaximum power principleControl theory (sociology)Point (geometry)Energy (signal processing)

Abstract

fetched live from OpenAlex

This paper describes a method for maximizing the power delivered to the rectifier in energy harvesters. It is demonstrated that there is an optimal turn-on time for rectifiers to maximize power transfer from the harvester. Next, a maximum power point tracking methodology based on rectifier turn-on time (RTOT-MPPT) is developed for triboelectric energy harvesters. The primary advantage of the proposed approach is the relative independence of the optimal turn-on time on the frequency and peak voltage of the harvester output. Thus, the proposed RTOT-MPPT method reduces the complexity of power tracking and can be efficient for a wider range of harvesters. The method is implemented for a triboelectric harvester and simulated in a 180 n m industrial HV-CMOS process, demonstrating that 34% higher power is delivered to the rectifier in each cycle.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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 designSimulation or modeling
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

Citations1
Published2025
Admission routes1
Has abstractyes

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