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Record W2029983337 · doi:10.1088/0960-1317/21/9/095002

Dynamic characteristics of a dielectric elastomer-based microbeam resonator with small vibration amplitude

2011· article· en· W2029983337 on OpenAlexafffund
Chuang Feng, Liying Jiang, Woon‐Ming Lau

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsWestern University
FundersOntario Ministry of Research and Innovation
KeywordsResonatorMicrobeamHelical resonatorMaterials scienceVibrationAcousticsAmplitudeVoltageOptoelectronicsElectrical engineeringOpticsPhysicsEngineering

Abstract

fetched live from OpenAlex

An analytical model is developed in the current work to analyze the dynamic characteristics of a dielectric elastomer (DE)-based microbeam resonator. The ambient pressure effect is taken into account by using the squeeze-film theory. Based on the Euler-Bernoulli beam model, approximate analytical solutions for the quality factor (Q-factor) and the resonant frequencies of the resonator have been derived using Raleigh's method for small amplitude vibration. The results indicate that the ambient pressure has significant effects on the Q-factor and the resonant frequency shift ratio, which represent the dynamic performance of the resonator. The active frequency tuning for such a resonator becomes feasible by changing the applied electrical voltage. It is found that high voltage is beneficial for improving the sensitivity of the resonator. However, high voltage may put the resonator at the risk of mechanical instability. The cut-off voltage for buckling has also been studied to predict the mechanical integrity of the resonator. This study is expected to be useful for design and applications of DE-based microresonators.

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 score0.003

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 integrity0.0010.000
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 head0.009
GPT teacher head0.173
Teacher spread0.163 · 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

Citations50
Published2011
Admission routes2
Has abstractyes

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