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Record W2916229814 · doi:10.1119/1.5084554

Fabrication and characterization of a microfluidic flow cytometer for the advanced undergraduate laboratory

2019· article· en· W2916229814 on OpenAlexafffund
Daniel J. Gorelik, Faiyza Alam, Joshua N. Milstein, Paul A. E. Piunno

Bibliographic record

VenueAmerican Journal of Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsNanotechnologyFabricationSoft lithographyPolydimethylsiloxaneCharacterization (materials science)Lab-on-a-chipPhysicsMaterials science

Abstract

fetched live from OpenAlex

Microfluidic devices can be used to explore a vast range of phenomena in biophysics and soft-matter physics. While the popularity of these devices is in part driven by the ease of soft-lithography, most research labs still depend upon expensive, clean-room fabrication of photoresist molds, which can make this technique inaccessible to the undergraduate laboratory. However, there are much simpler, if coarser, approaches to designing molds that are capable of producing surprisingly complicated devices. Here, we detail the fabrication and characterization of a microfluidic device for flow cytometry or particle sorting on a chip. Our device is a layered polydimethylsiloxane chip that uses a series of Quake valves to sort. The molds were fabricated on equipment accessible to most undergraduate labs. The techniques and physics we discuss in this manuscript can be employed to create an almost endless variety of devices for learning about complex fluid mechanics, mesoscopic, soft-matter, and biological physics.

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 score0.158
Threshold uncertainty score0.199

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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
Published2019
Admission routes2
Has abstractyes

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