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Record W2026469806 · doi:10.1504/ijamc.2009.026858

Towards fully integrated Lab-on-Chip: design, assembly and experimental results

2009· article· en· W2026469806 on OpenAlex
Ebrahim Ghafar Zadeh, Mohamad Sawan

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Advanced Media and Communication · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiosensorPolyelectrolyteLab-on-a-chipMicrofluidicsComputer scienceCapacitive sensingChipCMOSNanotechnologyComputer hardwareEmbedded systemMaterials scienceOptoelectronicsPolymerTelecommunications

Abstract

fetched live from OpenAlex

We present a direct-write CMOS-based platform for Lab-on-Chip (LoC) applications. A fully automated LoC consists of several active and passive components such as biosensors and microchannels integrated on a single chip for single purpose. In this paper, we describe a low-complexity platform consisting of capacitive biosensor and microfluidic structure. The capacitive biosensor is incorporated with nonolayers of polyelectrolyte molecules for creating sensing probes where the passive components such as microchannels are implemented through a direct-write assembly technique. We demonstrate the formation of Polyelectrolyte Sensing Layer (PSL) atop a Microfabricated Gold Electrode (MGE) and CMOS sensor using Chitosan and Alginate. This is an expanded version of a paper presented at the 3rd IEEE International Workshop on Medical Measurements and Applications, 9?10 May 2008, Ottawa, ON, Canada.

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)0.001
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 score0.097
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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