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Record W2603361648 · doi:10.1039/c7sm01771e

Abiotic streamers in a microfluidic system

2017· article· en· W2603361648 on OpenAlexaff
Nandini Debnath, Mahtab Hassanpourfard, Ranajay Ghosh, Japan Trivedi, Thomas Thundat, Mohtada Sadrzadeh, Aloke Kumar

Bibliographic record

VenueSoft Matter · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
Fundersnot available
KeywordsMicrofluidicsPolystyrenePolyacrylamideParticle (ecology)NanotechnologyConcentration gradientAbiotic componentChemistryChemical engineeringMaterials scienceChromatographyPolymerPolymer chemistryOrganic chemistryEngineeringBiologyEcology

Abstract

fetched live from OpenAlex

In this work, we report the phenomenon of formation of particle aggregates in the form of thin slender strings when a polyacrylamide (PAM) solution, laden with polystyrene (PS) beads is introduced into a microfluidic device containing an array of micropillars. PAM and a dilute solution of PS beads are introduced into the microfluidic channel through two separate inlets and localized particle aggregation is found to occur under certain flow regimes. The particle aggregates initially have a string-like morphology and are tethered at their ends to the micropillar walls, while the structure remains suspended in the fluid medium. Such a morphology inspired us to name these structures streamers. The flow regimes under which streamer formation is observed are quantified through state diagrams. We discuss the streamer formation time-scales and also show that streamer formation is likely the result of the flocculation of PS beads. Streamer formation has implications in investigating particle-laden complex flows through porous media.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.198
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2017
Admission routes1
Has abstractyes

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