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Record W2150469373 · doi:10.1088/0960-1317/17/3/016

Thermoelastic damping in bilayered micromechanical beam resonators

2007· article· en· W2150469373 on OpenAlexafffund
Srikar Vengallatore

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsThermoelastic dampingResonatorMaterials scienceBeam (structure)Microelectromechanical systemsTimoshenko beam theoryMagnetic dampingMechanicsAcousticsStructural engineeringVibrationPhysicsOptoelectronicsThermalEngineeringThermodynamics

Abstract

fetched live from OpenAlex

A detailed analysis of thermoelastic damping (TED) is essential in the design of the next generation of layered composite microresonators employed in microelectromechanical systems (MEMS) for sensing and communications. Here, we present an exact theory to compute the frequency dependence of thermoelastic damping in asymmetric, bilayered, micromechanical Euler–Bernoulli beam resonators. Comparison of the computed values for thermoelastic damping with previously measured internal friction in Au/SiO 2 microcantilevers suggests that TED contributes significantly to damping at higher modes and frequencies (∼1 MHz), but is negligible at lower frequencies, in these structures. The utility of the theory for MEMS design is illustrated by considering the representative example of Al/SiC bilayered 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations95
Published2007
Admission routes2
Has abstractyes

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