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Record W3162763921 · doi:10.1088/1361-6501/ac0270

Temperature compensated differential acoustic sensor for CO <sub>2</sub> sensing

2021· article· en· W3162763921 on OpenAlexafffund
Kiran Kumar Sappati, Sharmistha Bhadra

Bibliographic record

VenueMeasurement Science and Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAcousticsMaterials scienceDifferential (mechanical device)Acoustic sensorEnvironmental sciencePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract Temperature must be accounted for in order to provide accurate measurements in acoustic wave resonator based gas sensors. We present a flexible printed flexural plate wave (FPW) sensor for CO 2 monitoring employing temperature compensation. The FPW sensor is based on a differential interdigitated transducers (IDTs) structure consisting of an input IDT and two identical output IDTs with same distance from the input IDT. Among the two outputs of the differential structure, one is sensitive to the target gas CO 2 and the other one is insensitive to the target gas. The difference between the two output signals’ resonant frequencies is considered as the sensor’s differential response. Experiments at 25 ∘ C and 45 ∘ C show that the sensor’s differential response changes linearly with CO 2 concentration over 20–3000 ppm range. The differential responses at both temperatures are very similar showing the ability of the sensor’s structure to compensate for the temperature change. Sensitivity and limit of detection for CO 2 sensing are measured to be 543 Hz ppm −1 and 5.6 ppm, respectively. Further, the sensor shows good repeatability and low hysteresis. Sensor’s responses are mostly measured by measuring the scattering parameter, forward transmission (S 21 ) between each output IDT and input IDT of the sensor in frequency domain. In the last section, a prototype time domain measurement system is demonstrated to show the feasibility of the time domain measurements with the sensors. The sensor with the potential to be printed with roll-to-roll printing can be a good candidate for CO 2 monitoring where temperature varies.

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 score0.010
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.208
Teacher spread0.192 · 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

Citations7
Published2021
Admission routes2
Has abstractyes

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