MétaCan
Menu
Back to cohort
Record W2144646399 · doi:10.1109/jsen.2011.2166759

Continuous Size-Separation of Airborne Particles in a Microchannel for Aerosol Monitoring

2011· article· en· W2144646399 on OpenAlexafffund
Allison Schaap, Chiu‐Wing Winnie Chu, Boris Stoeber

Bibliographic record

VenueIEEE Sensors Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsAerosolMicrochannelParticle sizeMicrofluidicsSeparation (statistics)Particle (ecology)RangingMaterials scienceChannel (broadcasting)Particle-size distributionNanotechnologyMechanicsMeteorologyPhysicsChemical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

The real-time monitoring of aerosols is important for protecting human health and the environment. The small size of microchannels and their potential for integration with sensing technologies suggest them as a promising tool for the next generation of aerosol sensors. To that end, we present a novel microfluidics-based system for the size-separation of aerosols using the centrifugal force exerted on each particle as it travels around a curve. We demonstrate with simulations and experiments the separation by size of mixtures of aerosolized microspheres with diameters ranging from 0.2-3.2 μm. With bidisperse particle sizes in a single-outlet channel, a separation efficiency is defined to quantify how much of each of the two sizes of particles are located in distinct regions of the channel; the separation of mixtures at up to 80% separation efficiency is demonstrated. Particles are also separated into two or three outlets, each containing a different distribution of particles by size. Both experimental particle position and separation efficiency results match well with the simulations.

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 score0.009
Threshold uncertainty score0.288

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 head0.054
GPT teacher head0.307
Teacher spread0.253 · 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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Sensors JournalSame topicInhalation and Respiratory Drug DeliveryFrench-language works237,207