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Electrostatic Force-induced Broadband Effect in Electret- based Vertical Vibration Energy Harvesters using Finegrained Stainless Steel Oscillator

2014· article· en· W2085518031 on OpenAlexaff
Haruhiko ASANUMA, Motoaki Hara, Hiroyuki Oguchi, Hiroki Kuwano

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsHatch (Canada)
FundersJapan Society for the Promotion of ScienceNippon Steel Arts Foundation
KeywordsElectretMaterials scienceAir gap (plumbing)Full width at half maximumVibrationCantileverAccelerationBand gapFrequency bandPower (physics)Resonance (particle physics)Power densityAcousticsOptoelectronicsPhysicsElectrical engineeringAtomic physicsComposite materialEngineering

Abstract

fetched live from OpenAlex

We propose a fine-grained stainless-steel as a promising material for a robust oscillator and investigate the dependence of frequency band width, resonance frequency, and output power on initial air gaps in electret-based vertical vibration energy harvesters. Beams of the oscillator showed a shallow side-etched depth less than 10 μm, as well as smooth edges. The oscillator succeeded in travelling over 1-mm displacement without fracture. Also, we found that broader frequency band, as well as lower resonance frequency, can be achieved with reducing the initial air gap, whereas the output power exhibited a peak value at an optimal initial air gap. The results may be attributed to the soft spring effect induced by the stronger electrostatic force. Maximum output power density and FWHM of frequency band width of our harvester are 4.7 μW/cm3 and 14 Hz at initial air gap 0.3 mm and acceleration 4.9 m/s2.

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 score0.288
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.228
Teacher spread0.213 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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