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Record W2083237338 · doi:10.1109/jsen.2013.2264930

Inkjet Printed All-Polymer Flexural Plate Wave Sensors

2013· article· en· W2083237338 on OpenAlexaff
Christoph Sielmann, John Robert Busch, Boris Stoeber, Konrad Walus

Bibliographic record

VenueIEEE Sensors Journal · 2013
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceSubstrate (aquarium)Analytical Chemistry (journal)ChemistryChromatography

Abstract

fetched live from OpenAlex

We discuss the fabrication and performance of an all-polymer, flexural plate wave gravimetric sensor using flexible, piezoelectric Polyvinylidene fluoride as the substrate and an inkjet-printed interdigital transducer (IDT) employing conductive poly(3, 4-ethylenedioxythiophene) poly(styrenesulfonate) to excite Lamb waves within the film. Lamb waves are measured both electronically, using a second IDT, and mapped directly using a scanning laser Doppler vibrometer. Pulsed wave excitation is utilized to isolate the weak acoustic signal from the electromagnetic crosstalk, enabling the measurement of relative changes in the resonant frequency, Δf/f0, in response to added mass, Δm, to the sensing area. A gravimetric mass sensitivity equivalent to Δf/(f0Δm)=-153 cm2/g is measured by mass loading the sensor with printed polymer layers. It is found that the low stiffness of the substrate contributes significantly to the response of the sensor, yielding a measured overall sensitivity of Δf/(f0Δm)=-83 cm2/g.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2013
Admission routes1
Has abstractyes

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Same venueIEEE Sensors JournalSame topicAcoustic Wave Resonator TechnologiesFrench-language works237,207