MétaCan
Menu
Back to cohort
Record W4282836634 · doi:10.1088/1361-6439/ac7844

A quantitative study of the dynamic response of compliant microfluidic chips in a microfluidics context

2022· article· en· W4282836634 on OpenAlexafffund
Marie Hébert, Jan P. Huissoon, Carolyn L. Ren

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsPolydimethylsiloxaneCapacitanceChipContext (archaeology)Microfluidic chipMaterials scienceFlow (mathematics)Electronic engineeringNanotechnologyEngineeringMechanicsElectrical engineeringChemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Polydimethylsiloxane (PDMS) is a widely used material for microfluidic devices due to its low cost, superior optical properties and fast iterative design process. Its softness however creates challenges for the device design and operation because part of the applied pressures contributes to deform chips instead of controlling the flow. The resulting dynamic behaviour is often ignored in passive microfluidic that focuses on the static behaviour of the chip, however, can cause low accuracy to active microfluidic that actuates flow frequently. Therefore, understanding the dynamic behaviour of microfluidic devices due to material compliance is of fundamental and practical importance. In this study, the microfluidic chip compliance is carefully considered by separating it from the sample tubing compliance. The capacitance is retrieved by assuming a symmetric RC circuit based on the experimentally determined time constant and chip resistance. The experimental capacitance is compared to a theoretical formula for chip designs with different height-to-width ratios and height-to-length ratios and for various fluids. The accuracy is within one order of magnitude that is much closer than previous approximations.

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)0.001
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 score0.099
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Micromechanics and MicroengineeringSame topicMicrofluidic and Capillary Electrophoresis ApplicationsFrench-language works237,207