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Record W2315294121 · doi:10.1109/jsen.2014.2342716

Ferrofluid Sacrificial Microfabrication of Capacitive Pressure Sensors

2014· article· en· W2315294121 on OpenAlexafffund
Babak Assadsangabi, Xing Chen, Daniel Brox, Kenichi Takahata

Bibliographic record

VenueIEEE Sensors Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicCharacterization and Applications of Magnetic Nanoparticles
Canadian institutionsUniversity of British Columbia
FundersBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMicrofabricationFerrofluidMaterials scienceCapacitive sensingFabricationCapacitanceParyleneSurface micromachiningPhotolithographyOptoelectronicsElectrodeCapacitorNanotechnologyVoltageComposite materialElectrical engineeringPolymerChemistryMagnetic field

Abstract

fetched live from OpenAlex

A novel production approach to the fabrication of capacitive micropressure sensors is reported. A magnetic fluid known as ferrofluid is used as the liquid-phase sacrificial layer in the microfabrication process, enabling extremely simple, fast, and low-cost production of the sensors while eliminating the need for photolithographic, bonding, and/or chemical processes. The entire sensor fabrication is performed at/near room temperature. The sensors are designed to be constructed 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, being micromachined to have capacitive cavities with 10-30 μm depths. A Parylene-C membrane with a titanium 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 after the formation of the membrane, suspending it to establish the sensing capacitor. A highly linear response 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 score0.060
Threshold uncertainty score0.449

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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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

Citations14
Published2014
Admission routes2
Has abstractyes

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