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Record W4292183228 · doi:10.18280/i2m.210303

Design of a Thickness Sensor Based on a One-Dimensional Phononic Crystal

2022· article· en· W4292183228 on OpenAlexvenueno aff
Ahmed Kahlouche, Mounir Bouras, Abdesselam Hocini

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsnot available
Fundersnot available
KeywordsAcoustic metamaterialsMaterials scienceStack (abstract data type)Transfer-matrix method (optics)Crystal (programming language)Band gapAcousticsTransmission (telecommunications)Resonance (particle physics)Transfer matrixLayer (electronics)Work (physics)Photonic crystalOptoelectronicsOpticsPhysicsElectrical engineeringEngineeringComputer scienceNanotechnologyMechanical engineering

Abstract

fetched live from OpenAlex

Nowadays, sensor technology has attracted great interest in various domains. In this ‎work, we have analyzed a one dimensional phononic crystal made by the stack of N bilayers of ‎‎(LiNbO3/SiO2). The sensor design consists of a one dimensional phononic crystal structure with a ‎defect layer inserted in the middle. Using the Transfer Matrix Method (TMM), the transmission ‎spectrums of acoustic waves are calculated and plotted. In this work, we are interested in the ‎resonance peak that is transmitted inside the phononic band gap. The results obtained show clearly that the resonant frequency of the measured ‎transmission peak is very sensitive to the layer defect properties. This proves that such structure offers a new platform for sensing ‎applications.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.042
GPT teacher head0.271
Teacher spread0.229 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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