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22am2-E2 Stainless Steel-based Robust Oscillator and Optimization of Output Power in Out-of-plane Electrostatic Vibration Energy Harvesters

2014· article· en· W2659885648 on OpenAlexaff
Haruhiko ASANUMA, Hiroyuki Oguchi, Motoaki Hara, Hiroki Kuwano

Bibliographic record

VenueThe Proceedings of the Symposium on Micro-Nano Science and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsVibrationResonance (particle physics)AmplitudeCapacitancePower (physics)Air gap (plumbing)AccelerationMaterials scienceMechanicsPlane (geometry)AcousticsPhysicsAtomic physicsOpticsClassical mechanicsMathematicsComposite materialQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

This research reports a robust oscillator fabricated from a fine-grained stainless steel and optimization of output power in out-of-plane electrostatic vibration energy harvesters. The oscillator (area: 4 cm^2) showed shallow side-etched depth less than 10 μm and thus ideal vertical vibration. We numerically evaluated output powers and resonance frequencies with changing initial air gaps by simultaneously solving equation of motion and Kirchhoff's law. Applied acceleration is 0.5G. The output power increased with decreasing initial air gap and then exhibited a peak value of 25 μW at 0.6-mm air gap, whereas the resonance frequency decreased. The reason for the resulting peak is that stronger electrostatic force less than 0.6-mm air gaps reduces amplitude of the oscillator and thus the variation of capacitance. The decreased resonance frequency may be attributed to the soft spring effect by stronger electrostatic force.

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)0.000
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 score0.033
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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