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Record W2132798562 · doi:10.3390/s8042317

A Micromachined Capacitive Pressure Sensor Using a Cavity-Less Structure with Bulk-Metal/Elastomer Layers and Its Wireless Telemetry Application

2008· article· en· W2132798562 on OpenAlexafffund
Kenichi Takahata, Yogesh B. Gianchandani

Bibliographic record

VenueSensors · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
FundersTRIUMFUniversity of Michigan
KeywordsMaterials scienceCapacitive sensingPressure sensorCapacitanceElectromagnetic coilCapacitorElastomerSurface micromachiningOptoelectronicsAcousticsElectrodeElectrical engineeringComposite materialVoltageMechanical engineeringFabricationEngineering

Abstract

fetched live from OpenAlex

This paper reports a micromachined capacitive pressure sensor intended for applications that require mechanical robustness. The device is constructed with two micromachined metal plates and an intermediate polymer layer that is soft enough to deform in a target pressure range. The plates are formed of micromachined stainless steel fabricated by batch-compatible micro-electro-discharge machining. A polyurethane roomtemperature- vulcanizing liquid rubber of 38-μm thickness is used as the deformable material. This structure eliminates both the vacuum cavity and the associated lead transfer challenges common to micromachined capacitive pressure sensors. For frequency-based interrogation of the capacitance, passive inductor-capacitor tanks are fabricated by combining the capacitive sensor with an inductive coil. The coil has 40 turns of a 127-μmdiameter copper wire. Wireless sensing is demonstrated in liquid by monitoring the variation in the resonant frequency of the tank via an external coil that is magnetically coupled with the tank. The sensitivity at room temperature is measured to be 23-33 ppm/KPa over a dynamic range of 340 KPa, which is shown to match a theoretical estimation. Temperature dependence of the tank is experimentally evaluated.

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 categoriesMeta-epidemiology (narrow)
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 score0.157
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.206
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.

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

Citations36
Published2008
Admission routes2
Has abstractyes

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