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Record W2467779224 · doi:10.1039/c6lc00648e

On-demand droplet splitting using surface acoustic waves

2016· article· en· W2467779224 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLab on a Chip · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation
KeywordsMicrochannelPolydimethylsiloxaneOn demandTransducerAcousticsMaterials scienceOptoelectronicsNanotechnologyOpticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

We demonstrated the operation of an acoustomicrofluidic device composed of a polydimethylsiloxane (PDMS) microchannel and a slanted-finger interdigitated transducer (SF-IDT), for the on-demand splitting of droplets in an active, accurate, rapid, and size-controllable manner. A narrow beam of surface acoustic waves (SAWs) that emanated from the SF-IDT exerted an acoustic radiation force (ARF) on the droplet's water-oil interface due to the acoustic contrast between the two fluids. The ARF split the mother droplet into two or more daughter droplets of various volumes in a split ratio that was readily controlled by varying the applied voltage or the flow rate. Theoretical estimates of the ARF acting on the droplet interface were used to investigate the mechanism underlying the droplet splitting properties and size control. The versatility of the acoustomicrofluidic device operation was demonstrated by selectively pushing/placing a suspended polystyrene particle into a specific/preferred split daughter droplet using the direct ARF acting on the particle.

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.

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 score0.022
Threshold uncertainty score0.441

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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