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Record W2031149772 · doi:10.1039/c3lc41199k

End-faced waveguide mediated optical propulsion of microspheres and single cells in a microfluidic device

2013· article· en· W2031149772 on OpenAlexafffund
Lothar Lilge, Duoaud Shah, Luc Charron

Bibliographic record

VenueLab on a Chip · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoOntario Ministry of Health and Long-Term Care
KeywordsWaveguideMicrofluidicsOptical forceCapillary actionMaterials scienceOptical tweezersScatteringOptoelectronicsOpticsNanotechnologyPhysicsComposite material

Abstract

fetched live from OpenAlex

Single cell transport in microfluidic devices is a topic of interest as their utility is becoming appreciated by cell and molecular biologist. Cell transport should minimize mechanical stress due to friction or pressure gradients. Optical forces have the advantage of applying their forces across the cell volume and not only at the cell membrane and are thus preferable. Optical pushing by scattering force is a suitable candidate so highly dependent on the photon irradiance field inside the propagation capillary which in turn is determined by the waveguide properties delivering the radiation pressure. Here we present a numerical approach to predict the optical scattering force, speed and trajectory of cells as a function of waveguide and propagation capillary geometry. Experimental verification of the simulation approach is demonstrated using polystyrene microspheres and leukemia cells. Effects of optical fibre to waveguide alignment, capillary wall angle and temperature on the dynamic viscosity on speed and position of the microspheres and cells inside the propagation capillary are demonstrated.

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 score0.005
Threshold uncertainty score0.479

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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