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Record W2002250524 · doi:10.1063/1.3571530

A microfluidic device for simultaneous electrical and mechanical measurements on single cells

2011· article· en· W2002250524 on OpenAlexafffund
Jian Chen, Yi Zheng, Qingyuan Tan, Yan Liang Zhang, Jason Li, William R. Geddie, Michael A.S. Jewett, Yu Sun

Bibliographic record

VenueBiomicrofluidics · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsMaterials scienceCharacterization (materials science)CapacitancePipetteElectrical resistance and conductanceMechanical impedanceElectrical impedanceLab-on-a-chipBiomedical engineeringElectrical resistivity and conductivityNanotechnologyAnalytical Chemistry (journal)Composite materialChemistryElectrodeElectrical engineeringChromatography

Abstract

fetched live from OpenAlex

This paper presents a microfluidic device for simultaneous mechanical and electrical characterization of single cells. The device performs two types of cellular characterization (impedance spectroscopy and micropipette aspiration) on a single chip to enable cell electrical and mechanical characterization. To investigate the performance of the device design, electrical and mechanical properties of MC-3T3 osteoblast cells were measured. Based on electrical models, membrane capacitance of MC-3T3 cells was determined to be 3.39±1.23 and 2.99±0.82 pF at the aspiration pressure of 50 and 100 Pa, respectively. Cytoplasm resistance values were 110.1±37.7 kΩ (50 Pa) and 145.2±44.3 kΩ (100 Pa). Aspiration length of cells was found to be 0.813±0.351 μm at 50 Pa and 1.771±0.623 μm at 100 Pa. Quantified Young's modulus values were 377±189 Pa at 50 Pa and 344±156 Pa at 100 Pa. Experimental results demonstrate the device's capability for characterizing both electrical and mechanical properties of single cells.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.069
GPT teacher head0.224
Teacher spread0.156 · 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 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

Citations93
Published2011
Admission routes2
Has abstractyes

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