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Record W2065452206 · doi:10.1115/fedsm-icnmm2010-31213

Modeling and Analysis of Low Voltage Electro-Osmotic Micropump

2010· article· en· W2065452206 on OpenAlexaff
Jayan Ozhikandathil, Muthukumaran Packirisamy, Ion Stiharu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicropumpMultiphysicsMicrofluidicsMicrochannelFabricationVoltageMaterials scienceVolumetric flow rateMechanical engineeringElectrodeLab-on-a-chipMicrofabricationFluidicsElectronic engineeringComputer scienceOptoelectronicsNanotechnologyElectrical engineeringEngineeringMechanicsFinite element methodPhysics

Abstract

fetched live from OpenAlex

Scaling down the biochemical analytical system has been an important topic of research recently. Minimizing the energy requirement for the microfluidic transportation is essential for the realization of a Lab-on-a-chip (LOC) that can perform the Point-of-Care Testing (POCT). In this work, modeling and analysis of a low voltage Electro-osmotic (EO) micropump applicable for the Bio-Microfluidic systems using COMSOL Multiphysics software package is presented. In the previously reported low voltage EO micropump (3), position of electrodes makes the fabrication process a tedious task. Here, we investigate the effects of placing the electrodes tangential to the microchannel since such designs can be easily fabricated using PDMS/glass fabrication process, and also comparing the effect of different electrode configurations on the pump performance. In addition, the effects of geometrical parameters of micropump on volumetric flow rate and velocity profiles are investigated.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.003
GPT teacher head0.186
Teacher spread0.183 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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