MétaCan
Menu
Back to cohort

Air gap optimization for output power and band width in out-of-plane vibration energy harvesters employing electrets

2015· article· en· W2486095375 on OpenAlexaff
Haruhiko ASANUMA, Motoaki Hara, Hiroyuki Oguchi, Hiroki Kuwano

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsElectretVibrationPower (physics)Air gap (plumbing)Energy (signal processing)Plane (geometry)Materials scienceEnergy harvestingAcousticsEngineeringPhysicsElectrical engineeringComposite materialMathematicsGeometry

Abstract

fetched live from OpenAlex

We investigated the dependence of output power, frequency band width, and resonance frequency on the initial air gap for electret-based out-of-plane vibration energy harvesters, both numerically and experimentally. In this investigation, the external acceleration and surface charge densities of the electret were held constant. The numerical investigation predicted the following results: (1) an optimum value exists in the initial air gap to maximize the output power; and (2) enhanced electrostatic forces with decreasing the initial air gap emphasize the soft spring effect, which widens the frequency band width and lowers the resonance frequency. The experimental results showed behaviour consistent with the numerical predictions. The maximum output power in experiment was 4.0 μ W at the optimum initial air gap of 0.43 mm when the external acceleration and the frequency were 4.9 m s −2 and 102 Hz, respectively. With reducing the initial air gap to 0.28 mm, the frequency band width increased to 17 Hz, a 2.6-fold increase over the optimum initial air gap. The peak output power at the initial air gap of 0.28 mm was 2.7 μ W, when the external acceleration and frequency were 4.9 m s −2 and 96 Hz, respectively.

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 score0.439
Threshold uncertainty score0.649

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 head0.018
GPT teacher head0.209
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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Micromechanics and MicroengineeringSame topicInnovative Energy Harvesting TechnologiesFrench-language works237,207