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Record W2410144736 · doi:10.1039/c6lc00534a

A simple acoustofluidic chip for microscale manipulation using evanescent Scholte waves

2016· article· en· W2410144736 on OpenAlexaff
Vivian Aubert, Régis Wunenburger, Tony Valier‐Brasier, David Rabaud, Jean-Philippe Kleman, Cédric Poulain

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsCanadian Nautical Research Society
FundersFrench Infrastructure for Integrated Structural BiologyCentre National de la Recherche ScientifiqueAgence Nationale de la Recherche
KeywordsMicroscale chemistryAcousticsAcoustic waveStanding waveEvanescent waveMaterials scienceSubstrate (aquarium)Acoustic radiation forceOpticsPhysicsGeology

Abstract

fetched live from OpenAlex

Acoustofluidics is acknowledged as a powerful tool offering a contactless and label-free manipulation of fluids, micro-beads, and living cells. To date, most techniques rely on the use of propagating acoustic waves and take advantage of the associated acoustic radiation force in standing or progressive fields. Here, we present a new approach based on the generation of an evanescent acoustic field above a substrate. This field is obtained by means of subsonic interfacial waves giving rise to a well-defined standing wave pattern. By both imaging and probing the evanescent acoustic field, we show that these interfacial waves are guided waves known as quasi-Scholte acoustic waves. Scholte waves present very interesting features for applications in acoustofluidics. Namely, they confine the acoustic energy to the vicinity of the surface, they are nearly lossless and thus can propagate over long distances along the substrate, and finally they do not require any particular material for the substrate. With a very simple and low-cost device we show several examples of applications including patterning lines or arrays of cells, triggering spinning of living cells, and separating plasma from RBC in a whole blood microdroplet.

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 score0.024
Threshold uncertainty score0.580

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 head0.031
GPT teacher head0.252
Teacher spread0.221 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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