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Record W2149613270 · doi:10.1039/b313314c

Superior analytical sensitivity of electromagnetic excitation compared to contact electrode instigation of transverse acoustic waves

2004· article· en· W2149613270 on OpenAlexafffund
Scott Ballantyne, Michael Thompson

Bibliographic record

VenueThe Analyst · 2004
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoUniversity of Guelph
KeywordsAcousticsExcitationSensitivity (control systems)Transverse planeHarmonicMaterials scienceExcited stateSIGNAL (programming language)ElectrodeNoise (video)OpticsPhysicsElectrical engineeringElectronic engineeringEngineeringAtomic physics

Abstract

fetched live from OpenAlex

Quartz disks incorporated into an electrolyte flow-through configuration have been excited by both direct electrode contact and electromagnetic fields to generate propagating transverse acoustic waves in to the fluid. The conventional thickness-mode device was operated at the first harmonic (9 MHz) whereas the EM excited structure functioned successfully at 453 MHz (nominal 49th harmonic). The nature of signals produced by the two devices and potential contributions to noise are evaluated. A comparison of the response of the higher frequency sensor to the introduction of the protein neutravidin to the system reveals at least a seven times higher signal-to-noise ratio than is the result for the conventional bulk-acoustic wave structure. This increase in sensitivity coupled with the possibilities for tuning the frequency of the electromagnetic device and its potential for non-contact excitation offer significant advantages in terms of analytical sensor technology.

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)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations37
Published2004
Admission routes2
Has abstractyes

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