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Record W2003017740 · doi:10.1021/ac011104a

An In-Depth Analysis of Electric Cell−Substrate Impedance Sensing To Study the Attachment and Spreading of Mammalian Cells

2002· article· en· W2003017740 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnalytical Chemistry · 2002
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsNational Research Council CanadaBiotechnology Research Institute
Fundersnot available
KeywordsElectrodeChemistryElectrolyteAnalytical Chemistry (journal)Substrate (aquarium)Surface plasmon resonanceElectrical impedanceAuxiliary electrodeNanotechnologyMaterials scienceChromatographyElectrical engineering

Abstract

fetched live from OpenAlex

The attachment and spreading of fibroblast cells on a gold surface coated with fibronectin or ovalbumin were studied by a modified electric cell-substrate impedance sensor. In this system, cells were cultured in a well, equipped with a detecting gold electrode (surface area of 0.057 mm2) and a gold counter electrode (18 mm2). Based on a comprehensive theoretical framework, the impedance of the electrode-electrolyte interface and a cell layer was precisely obtained for frequencies ranging from 1 to 10 kHz. Surface concentrations of the protein adsorbed on the gold surface were determined by a surface plasmon resonance biosensor. The resistance change of the electrode-electrolyte interface at 4 kHz increased linearly with the number of fibroblast cells attached on the detecting electrode. The slope of the linear relationship appeared to depend on the type of coating protein. As the surface area occupied by the cells was also proportional to the cell number, the resistance change was in turn proportional to the area covered by the 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.

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 score0.153
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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