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Record W2067716465 · doi:10.1109/embc.2012.6346198

Electrode robustness in artificial cerebrospinal fluid for dielectrophoresis-based LoC

2012· article· en· W2067716465 on OpenAlexafffund
Mohamed Amine Miled, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCMC Microsystems
KeywordsDielectrophoresisElectrodeMaterials scienceMicroelectrodeMicrofluidicsMicroelectronicsLab-on-a-chipCMOSMicrosystemNanotechnologyMultielectrode arrayMicrofabricationOptoelectronicsBiomedical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

In this paper, we present hybrid microelectronics / microfluidic Lab-on-Chip (LoC) platform intended for implantable medical microsystems for neurotransmitter detection. In vitro experiments were achieved using artificial cerebrospinal fluid (ACSF) from Tocris Bioscience where microspheres were immersed to test the behaviour of the designed LoC. One of main features of the proposed LoC platform is its thin thickness, including micro-channels and silicon CMOS chip. The latter is integrated into the glass top-layer of the LoC measuring 0.5 mm. The size of the device is 9 mm × 5 mm. the electrode architecture is composed of 8×2×2 L-shaped electrodes in a 650 µm channel width and 4 sites for interdigitited electrodes. 32 L-shaped electrodes were connected to a electronics circuit for cells manipulation using dielectrophoresis (DEP). The described LoC achieved an efficient separation within a concentration of 50 µl of a solution of microspheres, distilled water (DW) and 500 µl of ACSF. Beyond this concentration, electrode destruction was observed.

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 score0.069
Threshold uncertainty score0.619

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 head0.050
GPT teacher head0.288
Teacher spread0.237 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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