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Record W2142756470 · doi:10.1039/b911953a

Integrated active mixing and biosensing using surface acoustic waves (SAW) and surface plasmon resonance (SPR) on a common substrate

2009· article· en· W2142756470 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLab on a Chip · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofabricationSurface acoustic waveSurface plasmon resonanceMicrofluidicsMaterials scienceBiosensorSubstrate (aquarium)TransducerInterdigital transducerOptoelectronicsLab-on-a-chipPiezoelectricityRayleigh scatteringPlasmonMixing (physics)NanotechnologyAcousticsOpticsNanoparticleFabrication

Abstract

fetched live from OpenAlex

This article presents a device incorporating surface plasmon resonance (SPR) sensing and surface acoustic wave (SAW) actuation integrated onto a common LiNbO(3) piezoelectric substrate. The device uses Rayleigh-type SAW to provide active microfluidic mixing in the fluid above the SPR sensor. Validation experiments show that SAW-induced microfluidic mixing results in accelerated binding kinetics of an avidin-biotin assay. Results also show that, though SAW action causes a parasitic SPR response due to heat injection into the fluid, a relatively brief relaxation time following the SAW pulses allows the effect to dissipate, without affecting the overall assay response. Since both SPR sensors and SAW transducers can be fabricated simultaneously using low-cost microfabrication methods on a single substrate, the proposed design is well-suited to lab-on-chip 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.

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 categoriesMeta-epidemiology (narrow)
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 score0.104
Threshold uncertainty score1.000

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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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