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Record W2469795368 · doi:10.1088/0960-1317/26/9/095006

Embedded silver PDMS electrodes for single cell electrical impedance spectroscopy

2016· article· en· W2469795368 on OpenAlexafffund
Yuan Wei, Zhensong Xu, Mark A. Cachia, John Nguyen, Yi Zheng, Chen Wang, Yu Sun

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMount Sinai HospitalUniversity of Toronto
FundersCanada Research Chairs
KeywordsElectrodeDielectric spectroscopyElectrical impedanceCapacitanceMaterials scienceMicrofluidicsMembraneOptoelectronicsConductivityEquivalent circuitAnalytical Chemistry (journal)NanotechnologyElectrical engineeringChemistryVoltageEngineeringChromatographyElectrochemistry

Abstract

fetched live from OpenAlex

This paper presents a microfluidic device with wide channels and embedded AgPDMS electrodes for measuring the electrical properties of single cells. The work demonstrates the feasibility of using a large channel design and embedded electrodes for impedance spectroscopy to circumvent issues such as channel clogging and limited device re-usability. AgPDMS electrodes were formed on channel sidewalls for impedance detection and cell electrical properties measurement. Equivalent circuit models were used to interpret multi-frequency impedance data to quantify each cell's cytoplasm conductivity and specific membrane capacitance. T24 cells were tested to validate the microfluidic system and modeling results. Comparisons were then made by measuring two leukemia cell lines (AML-2 and HL-60) which were found to have different cytoplasm conductivity values (0.29 ± 0.15 S m−1 versus 0.47 ± 0.20 S m−1) and specific membrane capacitance values (41 ± 25 mF m−2 versus 55 ± 26 mF m−2) when the cells were flown through the wide channel and measured by the AgPDMS electrodes.

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 score0.280
Threshold uncertainty score0.736

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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

Citations18
Published2016
Admission routes2
Has abstractyes

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