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Record W2764226061 · doi:10.3390/mi8100308

A Fluidic Interface with High Flow Uniformity for Reusable Large Area Resonant Biosensors

2017· article· en· W2764226061 on OpenAlexaff
Charles-Louis Azzopardi, Vivien Lacour, Jean‐François Manceau, Magali Barthès, Dimitri Bonnet, Thérèse Leblois

Bibliographic record

VenueMicromachines · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersAgence Nationale de la Recherche
KeywordsMiniaturizationFluidicsFinite element methodBiosensorMaterials scienceInterface (matter)FabricationParticle image velocimetryNanotechnologyMicrofluidicsFlow (mathematics)Electronic engineeringAcousticsOptoelectronicsMechanical engineeringEngineeringElectrical engineeringContact angleMechanicsComposite material

Abstract

fetched live from OpenAlex

Resonant biosensors are known for their high accuracy and high level of miniaturization. However, their fabrication costs prevent them from being used as disposable sensors and their effective commercial success will depend on their ability to be reused repeatedly. Accordingly, all the parts of the sensor in contact with the fluid need to tolerate the regenerative process which uses different chemicals (H₃PO₄, H₂SO₄ based baths) without degrading the characteristics of the sensor. In this paper, we propose a fluidic interface that can meet these requirements, and control the liquid flow uniformity at the surface of the vibrating area. We study different inlet and outlet channel configurations, estimating their performance using numerical simulations based on finite element method (FEM). The interfaces were fabricated using wet chemical etching on Si, which has all the desirable characteristics for a reusable biosensor circuit. Using a glass cover, we could observe the circulation of liquid near the active surface, and by using micro-particle image velocimetry (μPIV) on large surface area we could verify experimentally the effectiveness of the different designs and compare with simulation results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.814

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 science0.0010.000
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 head0.012
GPT teacher head0.226
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 teacher head, 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

Citations5
Published2017
Admission routes1
Has abstractyes

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