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Record W2125487847 · doi:10.1109/icmens.2004.1508940

Dielectrophoretic Membrane Filtration Process by Integrating Microelectrode Array

2006· article· en· W2125487847 on OpenAlexaff
Shahnawaz Molla, S. Bhattacharjee, Jacob H. Masliyah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFiltration (mathematics)MembraneCross-flow filtrationMicroelectrodeDielectrophoresisSeparation processElectrodeMicrofluidicsEmulsionMaterials scienceProcess (computing)ChromatographyChemistryNanotechnologyChemical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

A coupled AC dielectrophoretic membrane filtration technique is proposed in this paper.This technique utilizes ACdielectrophoresis and preferential transport through a semi-permeable membrane. Simultaneous application of ACdielectrophoresis and membrane filtration can be used for separation of trace amounts of a target species from an emulsion. This can be achieved by embedding inter-digitated microelectrode arrays on a membrane surface used in a cross flow filtration system. The electrode array, when actuated by an AC signal, will create a dielectrophoretic force field to capture the target species onto the membrane surface from the flowing emulsion during cross flow filtration. This paper explains the theoretical principles underlying such a process, and describes a simple mathematical framework based on trajectory analysis for assessing the separation efficiency of the technique. Simulation results indicate that preferential transport of the emulsified water through themembrane in a cross flow filtration device can be significantly enhanced by incorporating an AC dielectrophoretic field in the process. The novel technique proposed here can lead to a highly efficient continuous separation process for dilute emulsions.

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 score0.184
Threshold uncertainty score0.519

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 head0.003
GPT teacher head0.175
Teacher spread0.173 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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