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Record W2342627765 · doi:10.1177/1045389x16642536

Modeling and parametric study of a force-amplified compressive-mode piezoelectric energy harvester

2016· article· en· W2342627765 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

VenueJournal of Intelligent Material Systems and Structures · 2016
Typearticle
Languageen
FieldEngineering
TopicInnovative Energy Harvesting Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnergy harvestingNonlinear systemPiezoelectricityParametric statisticsPower (physics)ExcitationBandwidth (computing)Energy (signal processing)AcousticsElectrical engineeringEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

Piezoelectric energy harvesters have great potential for achieving inexhaustible power supply for small-scale electronic devices. However, the insufficient power-generation capability and the narrow working bandwidth of traditional energy harvesters have significantly hindered their adoption. To address these issues, we propose a nonlinear compressive-mode piezoelectric energy harvester. We embedded a multi-stage force amplification mechanism into the energy harvester, which greatly improved its power-generation capability. In this article, we describe how we first established an analytical model to study the force amplification effect. A lumped-parameter model was then built to simulate the strong nonlinear responses of the proposed energy harvester. A prototype was fabricated which demonstrated a superior power output of 30 mW under an excitation of 0.3 g ([Formula: see text] m/s 2 ). We discuss at the end the effect of geometric parameters that are influential to the performance. The proposed energy harvester is suitable to be used in low-frequency weak-excitation environments for powering wireless sensors.

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 score0.146
Threshold uncertainty score0.442

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 head0.019
GPT teacher head0.241
Teacher spread0.222 · 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