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Record W2942293185 · doi:10.1109/jmems.2019.2910228

Achieving High Quality Factor Without Vacuum Packaging by High Density Proof Mass Integration in Vibration Energy Harvesters

2019· article· en· W2942293185 on OpenAlexafffund
Andre Dompierre, Srikar Vengallatore, Luc G. Fréchette

Bibliographic record

VenueJournal of Microelectromechanical Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsMcGill UniversityInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsMicroelectromechanical systemsProof massCantileverFootprintVibrationDragMaterials scienceResonatorPiezoelectricityFluidicsScalingQuality (philosophy)MechanicsPhysicsAcousticsOptoelectronicsElectrical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper presents a simple approach to control fluidic damping, and thereby improve the mechanical quality factor at ambient pressure, of AlN-based piezoelectric resonant energy harvesters by using high density proof masses. Using models adapted from the literature, and accounting for the simultaneous transverse and rotational motion of the cantilever beam, scaling laws are extracted for the fluidic quality factor, Qf, as a function of the fluid damping regime, either due to drag or squeeze film forces. Subsequently, we demonstrate the utility of the scaling laws by characterizing silicon-based devices and tungsten tip masses. By accounting for other damping sources and the device operating frequency, we achieve close to an order of magnitude improvement on Qf with this strategy, going from 398 to 4193. Beside potential for footprint reductions and higher power outputs, these results suggest that high density proof mass integration can be an alternative to vacuum packaging for MEMS based vibration energy harvesting.

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 categoriesMeta-epidemiology (narrow)
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 score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.216
Teacher spread0.208 · 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
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
Published2019
Admission routes2
Has abstractyes

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