MétaCan
Menu
Back to cohort
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 OpenAlexafffund
Alan Renaudin, Vincent Chabot, Étienne Grondin, Vincent Aimez, Paul G. Charette

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.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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

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

Citations77
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueLab on a ChipSame topicMicrofluidic and Bio-sensing TechnologiesFrench-language works237,207