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Printed Polymer based Acoustic Sensor for Temperature Monitoring

2020· article· en· W3096013402 on OpenAlexaff
Kiran Kumar Sappati, Sharmistha Bhadra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsPolymerAcoustic sensorTemperature measurementMaterials scienceComputer scienceAcousticsComposite materialPhysics

Abstract

fetched live from OpenAlex

A flexible printed acoustic sensor for temperature measurement is reported. The sensor is based on an acoustic resonator operating in FPW mode and is fabricated by printing silver Interdigitated transducers (IDTs) on 0-3 lead zirconate titanate (PZT)- poly dimethylo siloxane (PDMS) composite thin film. IDTs are designed with a wavelength of 800 microns and aperture of 12 mm. A temperature variation changes the FPW velocity and thereby the resonant frequency of the FPW resonator. Therefore, temperature of the environment can be monitored by measuring the change of sensor's resonant frequency. Results obtained for the sensor exhibited a linear relationship between the resonant frequency of the sensor and temperature over a over 25 to 120°C temperature range with a sensitivity of 18.111 kHz/°C. The high piezoelectric charge constant of the composite results in a low attenuation of the acoustic resonator. The sensor can be useful where low cost flexible temperature sensing is required.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.232
Teacher spread0.212 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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