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Record W4213267768 · doi:10.3390/chemosensors10020081

Development of a Love-Wave Biosensor Based on an Analytical Model

2022· article· en· W4213267768 on OpenAlexaff
F. Sarry, Jérémy Bonhomme, Mourad Oudich, Pedro Alberto Segura Chavez, D. Beyssen, Omar Elmazria, M. K. Md Arshad, Paul G. Charette

Bibliographic record

VenueChemosensors · 2022
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsInsertion lossAttenuationViscositySensitivity (control systems)Materials scienceWork (physics)AcousticsPhase (matter)MechanicsPhase velocityThermodynamicsPhysicsOpticsOptoelectronicsElectronic engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

The present work deals with the development of a Love-wave biosensor for the diagnosis of the modification of cell viscosity. The relevant device performance such as insertion loss, attenuation, phase velocity, and sensitivity needs to be analysed as a function of the device structure and also regarding the effect of the liquid loading. In this study, we used an analytical model based on the equation of motions for a Love wave propagating in a three-layer structure. We show that the effect of the viscous coupling leads to insertion losses and a phase shift that impact the acoustic ratio. A comparison between experimental and theoretical results showed a good agreement between the behaviours as it was observed for the phase shift vs. the insertion loss with a limited difference in values (3.11/3.09—experimental/simulation for the sensitivity to the viscosity for different insertion losses) due to the assumptions made on the model used.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.739

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 head0.037
GPT teacher head0.243
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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