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Record W1537906984 · doi:10.1109/iembs.2003.1280861

An integrated microchip for dielectrophoresis based characterization and manipulation of cells

2004· article· en· W1537906984 on OpenAlexaff
Esther Guohua Cen, Lei Qian, K.V.I.S. Kaler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDielectrophoresisElectrokinetic phenomenaLevitationMicroelectrodeMicrofluidicsCharacterization (materials science)Materials scienceElectrodeVoltageNanotechnologyComputer scienceOptoelectronicsElectronic engineeringEngineeringElectrical engineeringPhysicsMagnet

Abstract

fetched live from OpenAlex

In the past, a variety of complementary techniques such as dielectrophoretic levitation, electrorotation and travelling wave dielectrophoresis (TWD) have each been individually and effectively applied in demonstrating the promising applications of DEP in the life and biological science. In this regard, however, little effort has been devoted to the development of an integrated platform that enables simultaneous such manipulations and measurements on a single microfabricated chip. In this paper we discuss the design and implementation of a single microchip (3 mm/spl times/6 mm) onto which three types of microelectrodes of similar geometric layouts but various functions were integrated. Yeast and plant protoplasts known to have well-characterized dielectric properties as test particles were used to investigate the electrokinetic behaviors for these electrodes as a function of frequency and electrode excitation mode. Several characteristic cell electrokinetic behaviors were identified, including linear conveyance, electrorotation and levitation in different regions of the frequency spectrum. The experimental observations demonstrated the feasibility and adaptability of combining the three main electrokinetic effects of DEP as a system on a single microchip platform in a compact manner.

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 score0.403
Threshold uncertainty score0.216

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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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

Citations5
Published2004
Admission routes1
Has abstractyes

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