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Record W1990126643 · doi:10.1063/1.4905057

Contact damping in microelectromechanical actuators

2014· article· en· W1990126643 on OpenAlexaff
Mahmoud Khater, Sohail Akhtar, Sangtak Park, Salih Özdemir, Eihab Abdel‐Rahman, C. P. Vyasarayani, Mustafa Yavuz

Bibliographic record

VenueApplied Physics Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsActuatorMicroelectromechanical systemsDrop (telecommunication)Materials scienceContact forceContact areaSubstrate (aquarium)MechanicsPneumatic actuatorOptoelectronicsMechanical engineeringElectrical engineeringPhysicsEngineeringComposite materialClassical mechanics

Abstract

fetched live from OpenAlex

We examine the significance of the energy loss mechanisms active in electrostatic MEMS actuators. We find that the dominant loss mechanism changes depending on the actuator mode of operation. We find that the active mechanisms in the order of their significance are: fluid-structure interactions dominant for actuators operating in air, actuator-substrate interactions dominant for actuators in contact with a substrate under vacuum, and intrinsic loss mechanisms dominant for actuators in-flight under vacuum. Further, experimental results show that the quality factor of an electrostatic MEMS actuator drops drastically as the actuator first comes into line contact with a substrate. As the contact area expands along the actuator length, the quality factor increases. Measurements under 1 Torr vacuum show a three-fold increase in the quality factor as the contact area expands from a line to 30% of the actuator area. This increase in the quality factor is attributed to the drop in the contribution of friction forces into energy losses as contact expands and adhesion forces increase.

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 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 score0.183
Threshold uncertainty score0.637

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 teacher head, 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

Citations6
Published2014
Admission routes1
Has abstractyes

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