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Record W3088197243 · doi:10.1038/s41598-020-72700-5

Rapid fabrication of sieved microwells and cross-flow microparticle trapping

2020· article· en· W3088197243 on OpenAlexafffund
Lauren Romita, Shyan Thompson, Dae Kun Hwang

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSettlingMicrofluidicsNanotechnologyFabricationMaterials scienceParticle (ecology)SeedingSedimentationDispersion (optics)Volumetric flow rateMechanicsEnvironmental sciencePhysicsOpticsGeologyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

The use of microwells is popular for a wide range of applications due to its' simplicity. However, the seeding of conventional microwells, which are closed at the bottom, is restricted to gravitational sedimentation for cell or particle deposition and therefore require lengthy settling times to maximize well occupancy. The addition of microfluidics to the capture process has accelerated cell or particle dispersion and improved capture ability but is mostly limited to gravitationally-driven settling for capture into the wells. An alternative approach to conventional closed-microwells, sieved microwells supersedes reliance on gravity by using hydrodynamic forces through the open pores at the bottom of the microwells to draw targets into the wells. We have developed a rapid fabrication method, based on flow lithography techniques, which allows us to easily customize the mesh pore sizes in a simple two-step process. Finally, by combining this microwell design with cross-flow trapping in a microfluidic two-layered channel, we achieve an 88 ± 6% well occupancy in under 10 s.

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 score0.011
Threshold uncertainty score0.341

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 head0.019
GPT teacher head0.210
Teacher spread0.192 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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