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Record W3139074588 · doi:10.3390/chemosensors9030058

Advances in Electromagnetic Piezoelectric Acoustic Sensor Technology for Biosensor-Based Detection

2021· article· en· W3139074588 on OpenAlexafffund
Gábor Mészáros, Sanaz Akbarzadeh, Brian De La Franier, Zsófia Keresztes, Michael Thompson

Bibliographic record

VenueChemosensors · 2021
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Toronto
FundersNemzeti Kutatási Fejlesztési és Innovációs HivatalNatural Sciences and Engineering Research Council of CanadaYasouj UniversityEuropean Commission
KeywordsBiosensorPiezoelectricityMaterials scienceAcousticsElectromagnetic coilHarmonicsOscillation (cell signaling)Sensitivity (control systems)OptoelectronicsElectrical engineeringVoltageElectronic engineeringNanotechnologyEngineeringPhysicsChemistry

Abstract

fetched live from OpenAlex

The ultra-high frequency EMPAS (electromagnetic piezoelectric acoustic sensor) device is composed of an electrode-less quartz disc in which shear oscillation is induced by an AC-powered magnetic coil located 30 μm below the substrate. This configuration allows the instigation of high acoustic harmonics (in the region of 49th–53rd), with the resulting enhanced analytical sensitivity for biosensor purposes compared to the conventional thickness-shear mode device. In this paper, we introduce significant improvements to the operation of the system with respect to sensing applications. This includes a new interface program and the capability to measure the acoustic quality factor not available in the prototype version. The enhanced configuration is subject to testing through biosensor detection of surface adsorption of biological macromolecules, which include β-casein, and a gelsolin-actin complex.

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)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.214
Teacher spread0.208 · 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
GenreReview

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

Citations12
Published2021
Admission routes2
Has abstractyes

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Same venueChemosensorsSame topicAcoustic Wave Resonator TechnologiesFrench-language works237,207