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Copper foil-type vibration-based electromagnetic energy harvester

2010· article· en· W2017236989 on OpenAlexaff
Farid Ullah Khan, Farrokh Sassani, Boris Stoeber

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2010
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLIGAPlanarMaterials scienceFabricationVibrationVoltageElectrical engineeringGenerator (circuit theory)AcousticsPower (physics)OptoelectronicsEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents the modeling, simulation, fabrication and experimental results of a vibration-based electromagnetic power generator (EMPG). A novel, low-cost, one-mask technique is used to fabricate the planar coils and the planar spring. This fabrication technique can provide an alternative for processes such as lithographie galvanoformung abformung (LIGA) or SU-8 molding and MEMS electroplating. Commercially available copper foils of 20 µm and 350 µm thicknesses are used for the planar coils and planar spring, respectively. The design with planar coils on either side of the magnets provides enhanced power generation for the same footprint of the device. The harvester's overall volume is 1 cm 3 . Excitation of the EMPG, at the fundamental frequency of 371 Hz, base acceleration of 13.5 g and base amplitude of 24.4 µm, yields an open circuit voltage of 60.1 mV, as well as 46.3 mV load voltage and 10.7 µW power for a 100 Ω load resistance. At a matching impedance of 7.5 Ω the device produced a maximum power of 23.56 µW and a power density of 23.56 µW cm −3 . The simulations based on the analytical model of the device show good agreement with the experimental results.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.172
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), 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

Citations88
Published2010
Admission routes1
Has abstractyes

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