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Record W2090885906 · doi:10.1115/imece2003-42738

Heat Transfer and Electrokinetic Flow Analysis in Poly(Dimethylsiloxane) Microfluidic Systems

2003· article· en· W2090885906 on OpenAlexaff
David Erickson, David Sinton, Vesna Nikolić, Dongqing Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrokinetic phenomenaJoule heatingMicrofluidicsMaterials scienceHeat transferBuffer (optical fiber)Thermal conductionVolumetric flow rateCurrent (fluid)Thermal conductivityFlow (mathematics)Analytical Chemistry (journal)Chemical engineeringThermodynamicsComposite materialChromatographyNanotechnologyChemistryMechanicsElectrical engineering

Abstract

fetched live from OpenAlex

Electrokinetic pumping is commonly used as a mechanism for species transport in microfluidic systems. Joule heating, caused by current flow through the buffer solution during electroosmotic flow, can lead to significant increases in the system temperature which can be detrimental to electrophoretic separations and temperature sensitive chemical reactions. In this paper, a combined experimental and numerical approach was used to examine Joule heating and heat transfer at a T intersection for PDMS/PDMS and PDMS/Glass hybrid microfluidic systems. In general it was found the PDMS/Glass chips maintained a more uniform and lower buffer temperature than the PDMS/PDMS systems, since the internally generated heat could be transferred more efficiently (due to the higher thermal conductivity of the glass component) from the channel network to the room temperature reservoir. This increase in temperature was shown to significantly increase the current load and the volume flow rate through the PDMS/PDMS system.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.004
GPT teacher head0.182
Teacher spread0.178 · 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 designSimulation or modeling
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
Published2003
Admission routes1
Has abstractyes

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