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Record W1519265832 · doi:10.1109/ccece.2015.7129090

Simulation and optical measurement of MEMS thermal actuator sub-micron displacements in air and water

2015· article· en· W1519265832 on OpenAlexaff
Bruno Barazani, Stephan Warnat, Ted Hubbard

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsActuatorDisplacement (psychology)Materials scienceMicroelectromechanical systemsFinite element methodThermalTemperature measurementChevron (anatomy)Fast Fourier transformOpticsMechanicsAcousticsMechanical engineeringStructural engineeringEngineeringOptoelectronicsElectrical engineeringPhysicsThermodynamicsComputer scienceGeology

Abstract

fetched live from OpenAlex

The aim of this study is to simulate and measure sub-micron motion of a chevron type MEMS thermal actuator in air and water. Finite element analysis (FEA) of the physical system provided predictions of the actuator temperature increase and displacement in both media. Simulations indicate that for 6V the maximum temperature on the chevron actuator arm is 335 °C in air and 35 °C in water. In water the temperature increase is confined to the vicinity of the heat source: 30 μm from the actuator center the temperature increase is smaller than 0.5 °C. FEA predicts a chevron displacement of 0.98 μm in air and 49 nm in water at 6 V. Experimental measurements of displacements were performed using an FFT image analysis algorithm with sub-micron precision. Experimental results show a displacement of 1.11 ± 0.01 μm in air and 67 ± 17 nm in water for 6V. The agreement between simulated and measured results validates the accuracy of the computational model used in this study. From the experimental data, the performance of the thermal actuator in water is about 6% of its performance in air. The study indicates the feasibility of using thermal actuators to perform cell tests in aqueous media.

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 score0.021
Threshold uncertainty score0.163

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 head0.026
GPT teacher head0.245
Teacher spread0.219 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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