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Record W2513747242 · doi:10.1109/iscas.2016.7539065

Live demonstration: CMOS capacitive sensor array for real-time analyses of living cells

2016· article· en· W2513747242 on OpenAlexaff
Ghazal Nabovati, Ebrahim Ghafar‐Zadeh, Antoine Letourneau, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsYork UniversityPolytechnique Montréal
Fundersnot available
KeywordsCapacitive sensingCMOSSensitivity (control systems)CapacitanceElectronic engineeringComputer scienceISFETChipBiosensorMultiplexingProcess (computing)Computer hardwareElectrical engineeringVoltageMaterials scienceEngineeringNanotechnologyTransistorElectrodePhysics

Abstract

fetched live from OpenAlex

This work concerns a compact, low-cost and reusable cell-based biosensor which can be employed as a versatile tool to transit Petri dish based experiments from the traditionally labor-intensive process to an automated and streamlined process which is significantly advantageous in different fields of biology and medicine. We demonstrate a fully integrated CMOS capacitive biosensor for tracking the growth of adherent cells and analyzing the effect of anti-cancer agents on the cells behavior. The proposed cell-based platform is composed of an array of 8×8 capacitive sensors working based on the fully differential charge-based measurement technique. The analog output voltage coming from readout interface is converted to a digital bit stream using on-chip DC-input ΣΔ modulator. A novel reconfigurable clocking scheme is proposed which allows reaching to a very high sensitivity and capacitance detection range.

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 score0.373
Threshold uncertainty score0.310

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 head0.026
GPT teacher head0.246
Teacher spread0.220 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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