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Record W2064625151 · doi:10.1109/icsens.2013.6688301

Microfabrication of capacitive pressure sensors using ferrofluid sacrificial layers

2013· article· en· W2064625151 on OpenAlexaff
Babak Assadsangabi, Xing Chen, Dan Brox, Kenichi Takahata

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrofabricationFerrofluidMaterials scienceCapacitive sensingCapacitanceFabricationParyleneLayer (electronics)Surface micromachiningOptoelectronicsPhotolithographyPressure sensorElectrodeNanotechnologyComposite materialPolymerElectrical engineeringMechanical engineeringChemistry

Abstract

fetched live from OpenAlex

A novel micromachined capacitive pressure sensor that is fabricated using a liquid-phase sacrificial layer is reported. Ferrofluid serves as the sacrificial layer in the microfabrication process, enabling extremely simple, fast, and low-cost production of the sensor, while eliminating the need for photolithographic, bonding, and/or chemical processes. The entire sensor fabrication is performed at/near room temperature. The sensor is based on the 1.5×1.5-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> stainless-steel chip with a 10-μm-deep capacitive cavity. A Parylene-C membrane with a Ti electrode is formed to seal the cavity by depositing it directly on top of the ferrofluid filled in the cavity. The ferrofluid is magnetically extracted from the cavity, suspending the membrane to form the sensing capacitor. A highly linear response with a sensitivity of 12.4 fF/KPa is obtained with the fabricated device. The temperature dependence of the sensor capacitance is experimentally characterized and reported as well.

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 score0.003
Threshold uncertainty score0.297

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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