MétaCan
Menu
Back to cohort

Modeling and experimental investigation of an impact-driven piezoelectric energy harvester from human motion

2013· article· en· W2047051764 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Materials and Structures · 2013
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBimorphEnergy harvestingPiezoelectricityVoltageAcousticsProof massImpulse (physics)Power (physics)Materials scienceMechanical energyElectrical engineeringEngineeringVibrationPhysics

Abstract

fetched live from OpenAlex

An impact-driven piezoelectric energy harvester from human motion is proposed in this paper. A high-frequency PZT-5A bimorph cantilever beam with attached proof mass at the free end was selected. A frequency up-conversion strategy was realized using impulse force generated by human motion. An aluminum prototype was attached to the leg of a person on a treadmill and measurements taken of the dissipated electric energy across multiple resistances over a range of walking speeds. The outer dimensions of this prototype are 90 mm × 40 mm × 24 mm. It has been shown that the average output voltage generated by the piezoelectric bimorph increases sequentially with a faster walking speed, the power varies with the external resistances and maximum levels occur at the optimal resistance, which is consistent with the simulation result. An open circuit voltage of 2.47 V and maximum average power of 51 μW can be achieved across a 20 kΩ external load resistance and 5 km h−1 walking speed. Experimental results reveal that the impact-driven piezoelectric energy harvesting system mounted on a person's leg has the potential for driving wearable devices.

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.

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 score0.086
Threshold uncertainty score0.499

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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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