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Record W2095187192 · doi:10.1109/iscas.2013.6572035

A portable lab-on-chip platform for magnetic beads density measuring

2013· article· en· W2095187192 on OpenAlexafffund
Yushan Zheng, Cyril Jacquemod, Mohamad Sawan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilCMC Microsystems
KeywordsMicrocoilMicrofluidicsInductanceLab-on-a-chipLinearityChipElectrical impedanceCMOSMaterials sciencePlanarMicrofabricationIntegrated circuitElectrical engineeringElectronic engineeringComputer scienceOptoelectronicsEngineeringNanotechnologyElectromagnetic coilVoltageFabrication

Abstract

fetched live from OpenAlex

We propose in this paper a portable lab-on-chip (LoC) platform dedicated for magnetic beads density measuring. The device consists of two main parts: disposable microfluidic structure and reusable electronic part. With results displayed on build-in LCD screen, the conventional bulky observation equipment is avoided. The principle of detection is that the presence of magnetic beads can affect the effective inductance of planar microcoil integrated inside of the LoC platform. In order to read out the inductance variation, we proposed a CMOS 0.18um application specific integrated circuit, which includes two different sensing blocks, namely impedance sensing and frequency sensing circuit. Preliminary experimental results with magnetic beads show that our proposed LoC platform allows measuring the density of magnetic beads in a wide range with fine linearity, either in continuous-flow microfluidics or digital mcirofluidics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.022
GPT teacher head0.187
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes2
Has abstractyes

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